PUBLICATIONS

@article{pmid41664592,

title = {Mapping whole-organism genetic comorbidities across model Species using unified ontologies},

author = {Caitlin Peaslee and Madison Held and Eloise Fadial and Donald F Conrad},

doi = {10.1093/genetics/iyag038},

issn = {1943-2631},

year  = {2026},

date = {2026-04-01},

urldate = {2026-04-01},

journal = {Genetics},

volume = {232},

number = {4},

abstract = {Understanding how genetic variation contributes to organism-wide phenotypes is critical for identifying mechanisms of disease. Here, we present a computational approach to analyze whole-organism comorbidities associated with genes underlying non-obstructive azoospermia (NOA), the most severe form of male infertility. We curated 204 mouse genes with experimentally validated spermatogenic failure and mapped their orthologs and phenotype annotations across humans, M. musculus, D. rerio, D. melanogaster, and C. elegans using a unified cross-species phenotype structure. This framework integrates a newly developed reproductive phenotype ontology with standardized whole-body phenotype categories to enable direct cross-species comparisons, using thousands of genotype-phenotype associations stored in model organism databases. Our analysis shows that most NOA genes have conserved orthologs across models, and that perturbation of these genes is frequently associated with non-reproductive phenotypes. In particular, integumentary defects and neoplastic phenotypes recur across species, supporting a shared genetic basis for epidemiological links between male infertility and systemic disease. Gene-level comorbidity patterns are partially predictable from single-cell RNA-seq, whole-body gene expression, and Gene Ontology annotations, suggesting that fundamental biological constraints shape the systemic consequences of reproductive gene dysfunction. Clustering genes by comorbidity profiles further distinguished genes with isolated reproductive effects from those with broad organismal consequences. This work demonstrates the power of ontology-based cross-species analysis for identifying pleiotropic effects of Mendelian disease mutations, and provides a resource, CoMorbidity DataBase Mapper (CoMo DBM), for joint analysis of genotype-phenotype associations in model organism databases.},

keywords = {GEMINI, Genomic Variation, Human Reproduction},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40204980,

title = {Benchmark of computational methods to detect digenism in sequencing data},

author = {Marie-Sophie C Ogloblinsky and Donald F Conrad and Anaïs Baudot and Elisabeth Tournier-Lasserve and and Emmanuelle Génin and Gaëlle Marenne},

doi = {10.1038/s41431-025-01834-9},

issn = {1476-5438},

year  = {2025},

date = {2025-04-01},

urldate = {2025-04-01},

journal = {Eur J Hum Genet},

abstract = {Digenic inheritance is characterized by the combined alteration of two different genes leading to a disease. It could explain the etiology of many currently undiagnosed rare diseases. With the advent of next-generation sequencing technologies, the identification of digenic inheritance patterns has become more technically feasible, yet still poses significant challenges without any gold standard method. Here, we present a comprehensive overview of the existing methods developed to detect digenic inheritance in sequencing data and provide a classification in cohort-based and individual-based methods. The latter category of methods appeared the most applicable to rare diseases, especially the ones not needing patient phenotypic description as input. We discuss the availability of the different methods, their output and scalability to inform potential users. Focusing on methods to detect digenic inheritance in the case of very rare or heterogeneous diseases, we propose a benchmark using different real-life scenarios involving known digenic and putative neutral pairs of genes. Among these different methods, DiGePred stood out as the one giving the least number of false positives, ARBOCK as giving the greatest number of true positives, and DIEP as having the best balance between both. By synthesizing the state-of-the-art techniques and providing insights into their practical utility, this benchmark serves as a valuable resource for researchers and clinicians in selecting suitable methodologies for detecting digenic inheritance in a wide range of disorders using sequencing data.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40494321,

title = {Easy-PSAP: An Integrated Workflow to Prioritize Pathogenic Variants in Sequence Data from a Single Individual},

author = {Marie-Sophie C Ogloblinsky and Marc B Gros-La-Faige and Daniel P Lewinsohn and Mathilde Nguyen and Lourdes Velo-Suarez and Anthony Herzig and Thomas E Ludwig and Helen Castillo-Madeen and Donald F Conrad and Emmanuelle Génin and Gaëlle Marenne},

doi = {10.1159/000543671},

issn = {1423-0062},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Hum Hered},

volume = {90},

number = {1},

pages = {33--40},

abstract = {INTRODUCTION: Next-generation sequencing (NGS) data analysis has become an integral part of clinical genetic diagnosis, raising the question of variant prioritization. The Population Sampling Probability (PSAP) method has been developed to tackle the issue of variant prioritization in the exome of a single patient, by leveraging allele frequencies from population databases and a variant pathogenicity score.nnMETHODS: Here, we present Easy-PSAP, a completely new implementation of the PSAP method comprising two user-friendly and highly adaptable pipelines. Easy-PSAP allows the gene-based recalibration of any in silico pathogenicity prediction score compared to scores of variants seen in the general population, including popular scores like CADD or AlphaMissense. Easy-PSAP can evaluate genetic variants at the scale of a whole exome or a whole genome using information from the latest population and annotation databases.nnRESULTS: Through simulations on synthetic disease exomes, we show that Easy-PSAP is able to rank more than 50% of causal pathogenic variants in the top 10 variants for an autosomal dominant model of transmission and in top 1 for an autosomal recessive model of transmission.nnDISCUSSION: These findings, along with the accessibility of the pipeline to both researchers and clinicians, make Easy-PSAP a state-of-the-art tool for variant prioritization in NGS data that can continue to evolve as new frameworks and databases become available. Easy-PSAP is implemented in R and bash within an open-source Snakemake framework. It is available on GitHub alongside conda environments containing the required dependencies (<ext-link ext-link-type="uri" xlink:href="https://github.com/msogloblinsky/Easy-PSAP" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/msogloblinsky/Easy-PSAP</ext-link>).},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39318036,

title = {PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis},

author = {Marie-Sophie C Ogloblinsky and Ozvan Bocher and Chaker Aloui and Anne-Louise Leutenegger and Ozan Ozisik and Anaïs Baudot and Elisabeth Tournier-Lasserve and Helen Castillo-Madeen and Daniel Lewinsohn and Donald F Conrad and Emmanuelle Génin and Gaëlle Marenne},

doi = {10.1002/gepi.22593},

issn = {1098-2272},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

journal = {Genet Epidemiol},

abstract = {The introduction of Next-Generation Sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The Population Sampling Probability (PSAP) method was developed to meet this aim but only for coding variants in exome data. Here, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large data sets of exomes and genomes from the general population. PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than 50% of non-coding ClinVar variants were among the top 10 variants of the genome. On real sequencing data from six patients with Cerebral Small Vessel Disease and nine patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37053313,

title = {The origins and functional effects of postzygotic mutations throughout the human life span},

author = {Nicole B Rockweiler and Avinash Ramu and Liina Nagirnaja and Wing H Wong and Michiel J Noordam and Casey W Drubin and Ni Huang and Brian Miller and Ellen Z Todres and Katinka A Vigh-Conrad and Antonino Zito and Kerrin S Small and Kristin G Ardlie and Barak A Cohen and Donald F Conrad},

doi = {10.1126/science.abn7113},

issn = {1095-9203},

year  = {2023},

date = {2023-04-01},

urldate = {2023-04-01},

journal = {Science},

volume = {380},

number = {6641},

pages = {eabn7113},

abstract = {Postzygotic mutations (PZMs) begin to accrue in the human genome immediately after fertilization, but how and when PZMs affect development and lifetime health remain unclear. To study the origins and functional consequences of PZMs, we generated a multitissue atlas of PZMs spanning 54 tissue and cell types from 948 donors. Nearly half the variation in mutation burden among tissue samples can be explained by measured technical and biological effects, and 9% can be attributed to donor-specific effects. Through phylogenetic reconstruction of PZMs, we found that their type and predicted functional impact vary during prenatal development, across tissues, and through the germ cell life cycle. Thus, methods for interpreting effects across the body and the life span are needed to fully understand the consequences of genetic variants.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33864768,

title = {Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease},

author = {Olivia M de Goede and Daniel C Nachun and Nicole M Ferraro and Michael J Gloudemans and Abhiram S Rao and Craig Smail and Tiffany Y Eulalio and François Aguet and Bernard Ng and Jishu Xu and Alvaro N Barbeira and Stephane E Castel and Sarah Kim-Hellmuth and YoSon Park and Alexandra J Scott and Benjamin J Strober and and Christopher D Brown and Xiaoquan Wen and Ira M Hall and Alexis Battle and Tuuli Lappalainen and Hae Kyung Im and Kristin G Ardlie and Sara Mostafavi and Thomas Quertermous and Karla Kirkegaard and Stephen B Montgomery},

doi = {10.1016/j.cell.2021.03.050},

issn = {1097-4172},

year  = {2021},

date = {2021-05-01},

urldate = {2021-05-01},

journal = {Cell},

volume = {184},

number = {10},

pages = {2633--2648.e19},

abstract = {Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid33239672,

title = {The genetic architecture of sporadic and multiple consecutive miscarriage},

author = {Triin Laisk and Ana Luiza G Soares and Teresa Ferreira and Jodie N Painter and Jenny C Censin and Samantha Laber and Jonas Bacelis and Chia-Yen Chen and Maarja Lepamets and Kuang Lin and Siyang Liu and Iona Y Millwood and Avinash Ramu and Jennifer Southcombe and Marianne S Andersen and Ling Yang and Christian M Becker and Anders D Børglum and Scott D Gordon and Jonas Bybjerg-Grauholm and Øyvind Helgeland and David M Hougaard and Xin Jin and Stefan Johansson and Julius Juodakis and Christiana Kartsonaki and Viktorija Kukushkina and Penelope A Lind and Andres Metspalu and Grant W Montgomery and Andrew P Morris and Ole Mors and Preben B Mortensen and Pål R Njølstad and Merete Nordentoft and Dale R Nyholt and Margaret Lippincott and Stephanie Seminara and Andres Salumets and Harold Snieder and Krina Zondervan and Thomas Werge and Zhengming Chen and Donald F Conrad and Bo Jacobsson and Liming Li and Nicholas G Martin and Benjamin M Neale and Rasmus Nielsen and Robin G Walters and Ingrid Granne and Sarah E Medland and Reedik Mägi and Deborah A Lawlor and Cecilia M Lindgren},

doi = {10.1038/s41467-020-19742-5},

issn = {2041-1723},

year  = {2020},

date = {2020-11-01},

urldate = {2020-11-01},

journal = {Nat Commun},

volume = {11},

number = {1},

pages = {5980},

abstract = {Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.},

keywords = {Genomic Variation, Human Reproduction},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32913098,

title = {The GTEx Consortium atlas of genetic regulatory effects across human tissues},

author = { },

doi = {10.1126/science.aaz1776},

issn = {1095-9203},

year  = {2020},

date = {2020-09-01},

journal = {Science},

volume = {369},

number = {6509},

pages = {1318--1330},

abstract = {The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32913075,

title = {Cell type-specific genetic regulation of gene expression across human tissues},

author = {Sarah Kim-Hellmuth and François Aguet and Meritxell Oliva and Manuel Muñoz-Aguirre and Silva Kasela and Valentin Wucher and Stephane E Castel and Andrew R Hamel and Ana Viñuela and Amy L Roberts and Serghei Mangul and Xiaoquan Wen and Gao Wang and Alvaro N Barbeira and Diego Garrido-Martín and Brian B Nadel and Yuxin Zou and Rodrigo Bonazzola and Jie Quan and Andrew Brown and Angel Martinez-Perez and José Manuel Soria and  and Gad Getz and Emmanouil T Dermitzakis and Kerrin S Small and Matthew Stephens and Hualin S Xi and Hae Kyung Im and Roderic Guigó and Ayellet V Segrè and Barbara E Stranger and Kristin G Ardlie and Tuuli Lappalainen},

doi = {10.1126/science.aaz8528},

issn = {1095-9203},

year  = {2020},

date = {2020-09-01},

journal = {Science},

volume = {369},

number = {6509},

abstract = {The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32913074,

title = {Determinants of telomere length across human tissues},

author = {Kathryn Demanelis and Farzana Jasmine and Lin S Chen and Meytal Chernoff and Lin Tong and Dayana Delgado and Chenan Zhang and Justin Shinkle and Mekala Sabarinathan and Hannah Lin and Eduardo Ramirez and Meritxell Oliva and Sarah Kim-Hellmuth and Barbara E Stranger and Tsung-Po Lai and Abraham Aviv and Kristin G Ardlie and François Aguet and Habibul Ahsan and  and Jennifer A Doherty and Muhammad G Kibriya and Brandon L Pierce},

doi = {10.1126/science.aaz6876},

issn = {1095-9203},

year  = {2020},

date = {2020-09-01},

journal = {Science},

volume = {369},

number = {6509},

abstract = {Telomere shortening is a hallmark of aging. Telomere length (TL) in blood cells has been studied extensively as a biomarker of human aging and disease; however, little is known regarding variability in TL in nonblood, disease-relevant tissue types. Here, we characterize variability in TLs from 6391 tissue samples, representing >20 tissue types and 952 individuals from the Genotype-Tissue Expression (GTEx) project. We describe differences across tissue types, positive correlation among tissue types, and associations with age and ancestry. We show that genetic variation affects TL in multiple tissue types and that TL may mediate the effect of age on gene expression. Our results provide the foundational knowledge regarding TL in healthy tissues that is needed to interpret epidemiological studies of TL and human health.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32913073,

title = {Transcriptomic signatures across human tissues identify functional rare genetic variation},

author = {Nicole M Ferraro and Benjamin J Strober and Jonah Einson and Nathan S Abell and Francois Aguet and Alvaro N Barbeira and Margot Brandt and Maja Bucan and Stephane E Castel and Joe R Davis and Emily Greenwald and Gaelen T Hess and Austin T Hilliard and Rachel L Kember and Bence Kotis and YoSon Park and Gina Peloso and Shweta Ramdas and Alexandra J Scott and Craig Smail and Emily K Tsang and Seyedeh M Zekavat and Marcello Ziosi and Aradhana and  and Kristin G Ardlie and Themistocles L Assimes and Michael C Bassik and Christopher D Brown and Adolfo Correa and Ira Hall and Hae Kyung Im and Xin Li and Pradeep Natarajan and  and Tuuli Lappalainen and Pejman Mohammadi and Stephen B Montgomery and Alexis Battle},

doi = {10.1126/science.aaz5900},

issn = {1095-9203},

year  = {2020},

date = {2020-09-01},

journal = {Science},

volume = {369},

number = {6509},

abstract = {Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32913072,

title = {The impact of sex on gene expression across human tissues},

author = {Meritxell Oliva and Manuel Muñoz-Aguirre and Sarah Kim-Hellmuth and Valentin Wucher and Ariel D H Gewirtz and Daniel J Cotter and Princy Parsana and Silva Kasela and Brunilda Balliu and Ana Viñuela and Stephane E Castel and Pejman Mohammadi and François Aguet and Yuxin Zou and Ekaterina A Khramtsova and Andrew D Skol and Diego Garrido-Martín and Ferran Reverter and Andrew Brown and Patrick Evans and Eric R Gamazon and Anthony Payne and Rodrigo Bonazzola and Alvaro N Barbeira and Andrew R Hamel and Angel Martinez-Perez and José Manuel Soria and  and Brandon L Pierce and Matthew Stephens and Eleazar Eskin and Emmanouil T Dermitzakis and Ayellet V Segrè and Hae Kyung Im and Barbara E Engelhardt and Kristin G Ardlie and Stephen B Montgomery and Alexis J Battle and Tuuli Lappalainen and Roderic Guigó and Barbara E Stranger},

doi = {10.1126/science.aba3066},

issn = {1095-9203},

year  = {2020},

date = {2020-09-01},

journal = {Science},

volume = {369},

number = {6509},

abstract = {Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32912332,

title = {A vast resource of allelic expression data spanning human tissues},

author = {Stephane E Castel and François Aguet and Pejman Mohammadi and  and Kristin G Ardlie and Tuuli Lappalainen},

doi = {10.1186/s13059-020-02122-z},

issn = {1474-760X},

year  = {2020},

date = {2020-09-01},

journal = {Genome Biol},

volume = {21},

number = {1},

pages = {234},

abstract = {Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid32764743,

title = {Mutation of CFAP57, a protein required for the asymmetric targeting of a subset of inner dynein arms in Chlamydomonas, causes primary ciliary dyskinesia},

author = {Ximena M Bustamante-Marin and Amjad Horani and Mihaela Stoyanova and Wu-Lin Charng and Mathieu Bottier and Patrick R Sears and Wei-Ning Yin and Leigh Anne Daniels and Hailey Bowen and Donald F Conrad and Michael R Knowles and Lawrence E Ostrowski and Maimoona A Zariwala and Susan K Dutcher},

doi = {10.1371/journal.pgen.1008691},

issn = {1553-7404},

year  = {2020},

date = {2020-08-01},

urldate = {2020-08-01},

journal = {PLoS Genet},

volume = {16},

number = {8},

pages = {e1008691},

abstract = {Primary ciliary dyskinesia (PCD) is characterized by chronic airway disease, reduced fertility, and randomization of the left/right body axis. It is caused by defects of motile cilia and sperm flagella. We screened a cohort of affected individuals that lack an obvious axonemal defect for pathogenic variants using whole exome capture, next generation sequencing, and bioinformatic analysis assuming an autosomal recessive trait. We identified one subject with an apparently homozygous nonsense variant [(c.1762C>T), p.(Arg588*)] in the uncharacterized CFAP57 gene. Interestingly, the variant results in the skipping of exon 11 (58 amino acids), which may be due to disruption of an exonic splicing enhancer. In normal human nasal epithelial cells, CFAP57 localizes throughout the ciliary axoneme. Nasal cells from the PCD patient express a shorter, mutant version of CFAP57 and the protein is not incorporated into the axoneme. The missing 58 amino acids include portions of WD repeats that may be important for loading onto the intraflagellar transport (IFT) complexes for transport or docking onto the axoneme. A reduced beat frequency and an alteration in ciliary waveform was observed. Knockdown of CFAP57 in human tracheobronchial epithelial cells (hTECs) recapitulates these findings. Phylogenetic analysis showed that CFAP57 is highly conserved in organisms that assemble motile cilia. CFAP57 is allelic with the BOP2/IDA8/FAP57 gene identified previously in Chlamydomonas reinhardtii. Two independent, insertional fap57 Chlamydomonas mutant strains show reduced swimming velocity and altered waveforms. Tandem mass tag (TMT) mass spectroscopy shows that FAP57 is missing, and the "g" inner dyneins (DHC7 and DHC3) and the "d" inner dynein (DHC2) are reduced, but the FAP57 paralog FBB7 is increased. Together, our data identify a homozygous variant in CFAP57 that causes PCD that is likely due to a defect in the inner dynein arm assembly process.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31636325,

title = {Identification of genetic variants in CFAP221 as a cause of primary ciliary dyskinesia},

author = {Ximena M Bustamante-Marin and Adam Shapiro and Patrick R Sears and Wu-Lin Charng and Donald F Conrad and Margaret W Leigh and Michael R Knowles and Lawrence E Ostrowski and Maimoona A Zariwala},

doi = {10.1038/s10038-019-0686-1},

issn = {1435-232X},

year  = {2020},

date = {2020-01-01},

journal = {J Hum Genet},

volume = {65},

number = {2},

pages = {175--180},

abstract = {Primary ciliary dyskinesia (PCD) is a rare disorder that affects the biogenesis or function of motile cilia resulting in chronic airway disease. PCD is genetically and phenotypically heterogeneous, with causative mutations identified in over 40 genes; however, the genetic basis of many cases is unknown. Using whole-exome sequencing, we identified three affected siblings with clinical symptoms of PCD but normal ciliary structure, carrying compound heterozygous loss-of-function variants in CFAP221. Computational analysis suggests that these variants are the most damaging alleles shared by all three siblings. Nasal epithelial cells from one of the subjects demonstrated slightly reduced beat frequency (16.5 Hz vs 17.7 Hz, p = 0.16); however, waveform analysis revealed that the CFAP221 defective cilia beat in an aberrant circular pattern. These results show that genetic variants in CFAP221 cause PCD and that CFAP221 should be considered a candidate gene in cases where PCD is suspected but cilia structure and beat frequency appear normal.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31316157,

title = {Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data},

author = {Alberto Romagnoni and Simon Jégou and Kristel Van Steen and Gilles Wainrib and Jean-Pierre Hugot and },

doi = {10.1038/s41598-019-46649-z},

issn = {2045-2322},

year  = {2019},

date = {2019-07-01},

journal = {Sci Rep},

volume = {9},

number = {1},

pages = {10351},

abstract = {Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid29021290,

title = {Identifying -mediators for -eQTLs across many human tissues using genomic mediation analysis},

author = {Fan Yang and Jiebiao Wang and  and Brandon L Pierce and Lin S Chen},

doi = {10.1101/gr.216754.116},

issn = {1549-5469},

year  = {2017},

date = {2017-11-01},

journal = {Genome Res},

volume = {27},

number = {11},

pages = {1859--1871},

abstract = {The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (-eQTLs). More research is needed to identify effects of genetic variation on distant genes (-eQTLs) and understand their biological mechanisms. One common -eQTLs mechanism is "mediation" by a local () transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "-mediators" of -eQTLs, including those "-hubs" involved in regulation of many -genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying -eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study -mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of -hubs and -eQTL regulation across tissue types.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid29021288,

title = {Co-expression networks reveal the tissue-specific regulation of transcription and splicing},

author = {Ashis Saha and Yungil Kim and Ariel D H Gewirtz and Brian Jo and Chuan Gao and Ian C McDowell and  and Barbara E Engelhardt and Alexis Battle},

doi = {10.1101/gr.216721.116},

issn = {1549-5469},

year  = {2017},

date = {2017-11-01},

journal = {Genome Res},

volume = {27},

number = {11},

pages = {1843--1858},

abstract = {Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid29022598,

title = {Landscape of X chromosome inactivation across human tissues},

author = {Taru Tukiainen and Alexandra-Chloé Villani and Angela Yen and Manuel A Rivas and Jamie L Marshall and Rahul Satija and Matt Aguirre and Laura Gauthier and Mark Fleharty and Andrew Kirby and Beryl B Cummings and Stephane E Castel and Konrad J Karczewski and François Aguet and Andrea Byrnes and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and Tuuli Lappalainen and Aviv Regev and Kristin G Ardlie and Nir Hacohen and Daniel G MacArthur},

doi = {10.1038/nature24265},

issn = {1476-4687},

year  = {2017},

date = {2017-10-01},

journal = {Nature},

volume = {550},

number = {7675},

pages = {244--248},

abstract = {X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid29022597,

title = {Genetic effects on gene expression across human tissues},

author = { and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and Alexis Battle and Christopher D Brown and Barbara E Engelhardt and Stephen B Montgomery},

doi = {10.1038/nature24277},

issn = {1476-4687},

year  = {2017},

date = {2017-10-01},

journal = {Nature},

volume = {550},

number = {7675},

pages = {204--213},

abstract = {Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid29022589,

title = {Dynamic landscape and regulation of RNA editing in mammals},

author = {Meng How Tan and Qin Li and Raghuvaran Shanmugam and Robert Piskol and Jennefer Kohler and Amy N Young and Kaiwen Ivy Liu and Rui Zhang and Gokul Ramaswami and Kentaro Ariyoshi and Ankita Gupte and Liam P Keegan and Cyril X George and Avinash Ramu and Ni Huang and Elizabeth A Pollina and Dena S Leeman and Alessandra Rustighi and Y P Sharon Goh and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and Ajay Chawla and Giannino Del Sal and Gary Peltz and Anne Brunet and Donald F Conrad and Charles E Samuel and Mary A O'Connell and Carl R Walkley and Kazuko Nishikura and Jin Billy Li},

doi = {10.1038/nature24041},

issn = {1476-4687},

year  = {2017},

date = {2017-10-01},

journal = {Nature},

volume = {550},

number = {7675},

pages = {249--254},

abstract = {Adenosine-to-inosine (A-to-I) RNA editing is a conserved post-transcriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules. Although many editing sites have recently been discovered, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of non-repetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis- and trans-regulation of A-to-I editing.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid29022581,

title = {The impact of rare variation on gene expression across tissues},

author = {Xin Li and Yungil Kim and Emily K Tsang and Joe R Davis and Farhan N Damani and Colby Chiang and Gaelen T Hess and Zachary Zappala and Benjamin J Strober and Alexandra J Scott and Amy Li and Andrea Ganna and Michael C Bassik and Jason D Merker and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and  and Ira M Hall and Alexis Battle and Stephen B Montgomery},

doi = {10.1038/nature24267},

issn = {1476-4687},

year  = {2017},

date = {2017-10-01},

journal = {Nature},

volume = {550},

number = {7675},

pages = {239--243},

abstract = {Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28334373,

title = {Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions},

author = {Steven H Wu and Rachel S Schwartz and David J Winter and Donald F Conrad and Reed A Cartwright},

doi = {10.1093/bioinformatics/btx133},

issn = {1367-4811},

year  = {2017},

date = {2017-08-01},

journal = {Bioinformatics},

volume = {33},

number = {15},

pages = {2322--2329},

abstract = {MOTIVATION: Accurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others.nnRESULTS: We modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls.nnAVAILABILITY AND IMPLEMENTATION: Methods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858).nnCONTACT: [email protected] INFORMATION: Supplementary data is available at Bioinformatics online.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28658705,

title = {How to Map the Genetic Basis for Conditions that are Comorbid with Male Infertility},

author = {Liina Nagirnaja and Katinka Vigh-Conrad and Donald F Conrad},

doi = {10.1055/s-0037-1603567},

issn = {1526-4564},

year  = {2017},

date = {2017-05-01},

urldate = {2017-05-01},

journal = {Semin Reprod Med},

volume = {35},

number = {3},

pages = {225--230},

keywords = {Genomic Variation, Human Reproduction},

pubstate = {published},

tppubtype = {article}

}

@article{pmid28369037,

title = {The impact of structural variation on human gene expression},

author = {Colby Chiang and Alexandra J Scott and Joe R Davis and Emily K Tsang and Xin Li and Yungil Kim and Tarik Hadzic and Farhan N Damani and Liron Ganel and and Stephen B Montgomery and Alexis Battle and Donald F Conrad and Ira M Hall},

doi = {10.1038/ng.3834},

issn = {1546-1718},

year  = {2017},

date = {2017-05-01},

urldate = {2017-05-01},

journal = {Nat Genet},

volume = {49},

number = {5},

pages = {692--699},

abstract = {Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid27776118,

title = {Genome-wide significance testing of variation from single case exomes},

author = {Amy B Wilfert and Katherine R Chao and Madhurima Kaushal and Sanjay Jain and Sebastian Zöllner and David R Adams and Donald F Conrad},

doi = {10.1038/ng.3697},

issn = {1546-1718},

year  = {2016},

date = {2016-12-01},

urldate = {2016-12-01},

journal = {Nat Genet},

volume = {48},

number = {12},

pages = {1455--1461},

abstract = {Standard techniques from genetic epidemiology are ill-suited to formally assess the significance of variants identified from a single case. We developed a statistical inference framework for identifying unusual functional variation from a single exome or genome, what we refer to as the 'n-of-one' problem. Using this approach we assessed our ability to identify the causal genotypes in over 5 million simulated cases of Mendelian disease, identifying 39% of disease genotypes as the most damaging unit in a typical exome background. We applied our approach to 129 n-of-one families from the Undiagnosed Diseases Program, nominating 60% of 30 disease genes determined to be diagnostic by a standard clinical workup. Our method can currently produce well-calibrated P values when applied to single genomes, can facilitate integration of multiple data types for n-of-one analyses, and, with further work, could become a widely used epidemiological method like linkage analysis or genome-wide association analysis.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid27206562,

title = {Defining the Phenotype and Assessing Severity in Phosphoglucomutase-1 Deficiency},

author = {Sunnie Yan-Wai Wong and Lesa J Beamer and Therese Gadomski and Tomas Honzik and Miski Mohamed and Saskia B Wortmann and Katja S Brocke Holmefjord and Marit Mork and Francis Bowling and Jolanta Sykut-Cegielska and Dieter Koch and Amanda Ackermann and Charles A Stanley and Daisy Rymen and Avraham Zeharia and Moeen Al-Sayed and Thomas Marquardt and Jaak Jaeken and Dirk Lefeber and Donald F Conrad and Tamas Kozicz and Eva Morava},

doi = {10.1016/j.jpeds.2016.04.021},

issn = {1097-6833},

year  = {2016},

date = {2016-08-01},

journal = {J Pediatr},

volume = {175},

pages = {130--136.e8},

abstract = {OBJECTIVE: To define phenotypic groups and identify predictors of disease severity in patients with phosphoglucomutase-1 deficiency (PGM1-CDG).nnSTUDY DESIGN: We evaluated 27 patients with PGM1-CDG who were divided into 3 phenotypic groups, and group assignment was validated by a scoring system, the Tulane PGM1-CDG Rating Scale (TPCRS). This scale evaluates measurable clinical features of PGM1-CDG. We examined the relationship between genotype, enzyme activity, and TPCRS score by using regression analysis. Associations between the most common clinical features and disease severity were evaluated by principal component analysis.nnRESULTS: We found a statistically significant stratification of the TPCRS scores among the phenotypic groups (P < .001). Regression analysis showed that there is no significant correlation between genotype, enzyme activity, and TPCRS score. Principal component analysis identified 5 variables that contributed to 54% variance in the cohort and are predictive of disease severity: congenital malformation, cardiac involvement, endocrine deficiency, myopathy, and growth.nnCONCLUSIONS: We established a scoring algorithm to reliably evaluate disease severity in patients with PGM1-CDG on the basis of their clinical history and presentation. We also identified 5 clinical features that are predictors of disease severity; 2 of these features can be evaluated by physical examination, without the need for specific diagnostic testing and thus allow for rapid assessment and initiation of therapy.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid27087321,

title = {A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies},

author = {Corneliu A Bodea and Benjamin M Neale and Stephan Ripke and  and Mark J Daly and Bernie Devlin and Kathryn Roeder},

doi = {10.1016/j.ajhg.2016.02.025},

issn = {1537-6605},

year  = {2016},

date = {2016-05-01},

journal = {Am J Hum Genet},

volume = {98},

number = {5},

pages = {857--868},

abstract = {One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid26473511,

title = {Improved detection of disease-associated variation by sex-specific characterization and prediction of genes required for fertility},

author = {N R Y Ho and N Huang and D F Conrad},

doi = {10.1111/andr.12109},

issn = {2047-2927},

year  = {2015},

date = {2015-11-01},

urldate = {2015-11-01},

journal = {Andrology},

volume = {3},

number = {6},

pages = {1140--1149},

abstract = {Despite its great potential, high-throughput functional genomic data are rarely integrated and applied to characterizing the genomic basis of fertility. We obtained and reprocessed over 30 functional genomics datasets from human and mouse germ cells to perform genome-wide prediction of genes underlying various reproductive phenotypes in both species. Genes involved in male fertility are easier to predict than their female analogs. Of the multiple genomic data types examined, protein-protein interactions are by far the most informative for gene prediction, followed by gene expression, and then epigenetic marks. As an application of our predictions, we show that copy number variants (CNVs) disrupting predicted fertility genes are more strongly associated with gonadal dysfunction in male and female case-control cohorts when compared to all gene-disrupting CNVs (OR = 1.64, p < 1.64 × 10(-8) vs. OR = 1.25, p < 4 × 10(-6)). Using gender-specific fertility gene annotations further increased the observed associations (OR = 2.31, p < 2.2 × 10(-16)). We provide our gene predictions as a resource with this article.},

keywords = {Genomic Variation, Human Reproduction},

pubstate = {published},

tppubtype = {article}

}

@article{pmid26016481,

title = {Using whole-genome sequences of the LG/J and SM/J inbred mouse strains to prioritize quantitative trait genes and nucleotides},

author = {Igor Nikolskiy and Donald F Conrad and Sung Chun and Justin C Fay and James M Cheverud and Heather A Lawson},

doi = {10.1186/s12864-015-1592-3},

issn = {1471-2164},

year  = {2015},

date = {2015-05-01},

journal = {BMC Genomics},

volume = {16},

number = {1},

pages = {415},

abstract = {BACKGROUND: The laboratory mouse is the most commonly used model for studying variation in complex traits relevant to human disease. Here we present the whole-genome sequences of two inbred strains, LG/J and SM/J, which are frequently used to study variation in complex traits as diverse as aging, bone-growth, adiposity, maternal behavior, and methamphetamine sensitivity.nnRESULTS: We identified small nucleotide variants (SNVs) and structural variants (SVs) in the LG/J and SM/J strains relative to the reference genome and discovered novel variants in these two strains by comparing their sequences to other mouse genomes. We find that 39% of the LG/J and SM/J genomes are identical-by-descent (IBD). We characterized amino-acid changing mutations using three algorithms: LRT, PolyPhen-2 and SIFT. We also identified polymorphisms between LG/J and SM/J that fall in regulatory regions and highly informative transcription factor binding sites (TFBS). We intersected these functional predictions with quantitative trait loci (QTL) mapped in advanced intercrosses of these two strains. We find that QTL are both over-represented in non-IBD regions and highly enriched for variants predicted to have a functional impact. Variants in QTL associated with metabolic (231 QTL identified in an F16 generation) and developmental (41 QTL identified in an F34 generation) traits were interrogated and we highlight candidate quantitative trait genes (QTG) and nucleotides (QTN) in a QTL on chr13 associated with variation in basal glucose levels and in a QTL on chr6 associated with variation in tibia length.nnCONCLUSIONS: We show how integrating genomic sequence with QTL reduces the QTL search space and helps researchers prioritize candidate genes and nucleotides for experimental follow-up. Additionally, given the LG/J and SM/J phylogenetic context among inbred strains, these data contribute important information to the genomic landscape of the laboratory mouse.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25954003,

title = {Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome},

author = {Manuel A Rivas and Matti Pirinen and Donald F Conrad and Monkol Lek and Emily K Tsang and Konrad J Karczewski and Julian B Maller and Kimberly R Kukurba and David S DeLuca and Menachem Fromer and Pedro G Ferreira and Kevin S Smith and Rui Zhang and Fengmei Zhao and Eric Banks and Ryan Poplin and Douglas M Ruderfer and Shaun M Purcell and Taru Tukiainen and Eric V Minikel and Peter D Stenson and David N Cooper and Katharine H Huang and Timothy J Sullivan and Jared Nedzel and  and  and Carlos D Bustamante and Jin Billy Li and Mark J Daly and Roderic Guigo and Peter Donnelly and Kristin Ardlie and Michael Sammeth and Emmanouil T Dermitzakis and Mark I McCarthy and Stephen B Montgomery and Tuuli Lappalainen and Daniel G MacArthur},

doi = {10.1126/science.1261877},

issn = {1095-9203},

year  = {2015},

date = {2015-05-01},

journal = {Science},

volume = {348},

number = {6235},

pages = {666--669},

abstract = {Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25010716,

title = {Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing},

author = {Andrew E O Hughes and Vincent Magrini and Ryan Demeter and Christopher A Miller and Robert Fulton and Lucinda L Fulton and William C Eades and Kevin Elliott and Sharon Heath and Peter Westervelt and Li Ding and Donald F Conrad and Brian S White and Jin Shao and Daniel C Link and John F DiPersio and Elaine R Mardis and Richard K Wilson and Timothy J Ley and Matthew J Walter and Timothy A Graubert},

doi = {10.1371/journal.pgen.1004462},

issn = {1553-7404},

year  = {2014},

date = {2014-07-01},

journal = {PLoS Genet},

volume = {10},

number = {7},

pages = {e1004462},

abstract = {Next-generation sequencing has been used to infer the clonality of heterogeneous tumor samples. These analyses yield specific predictions-the population frequency of individual clones, their genetic composition, and their evolutionary relationships-which we set out to test by sequencing individual cells from three subjects diagnosed with secondary acute myeloid leukemia, each of whom had been previously characterized by whole genome sequencing of unfractionated tumor samples. Single-cell mutation profiling strongly supported the clonal architecture implied by the analysis of bulk material. In addition, it resolved the clonal assignment of single nucleotide variants that had been initially ambiguous and identified areas of previously unappreciated complexity. Accordingly, we find that many of the key assumptions underlying the analysis of tumor clonality by deep sequencing of unfractionated material are valid. Furthermore, we illustrate a single-cell sequencing strategy for interrogating the clonal relationships among known variants that is cost-effective, scalable, and adaptable to the analysis of both hematopoietic and solid tumors, or any heterogeneous population of cells.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25010687,

title = {Cis and trans effects of human genomic variants on gene expression},

author = {Julien Bryois and Alfonso Buil and David M Evans and John P Kemp and Stephen B Montgomery and Donald F Conrad and Karen M Ho and Susan Ring and Matthew Hurles and Panos Deloukas and George Davey Smith and Emmanouil T Dermitzakis},

doi = {10.1371/journal.pgen.1004461},

issn = {1553-7404},

year  = {2014},

date = {2014-07-01},

journal = {PLoS Genet},

volume = {10},

number = {7},

pages = {e1004461},

abstract = {Gene expression is a heritable cellular phenotype that defines the function of a cell and can lead to diseases in case of misregulation. In order to detect genetic variations affecting gene expression, we performed association analysis of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) with gene expression measured in 869 lymphoblastoid cell lines of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort in cis and in trans. We discovered that 3,534 genes (false discovery rate (FDR) = 5%) are affected by an expression quantitative trait locus (eQTL) in cis and 48 genes are affected in trans. We observed that CNVs are more likely to be eQTLs than SNPs. In addition, we found that variants associated to complex traits and diseases are enriched for trans-eQTLs and that trans-eQTLs are enriched for cis-eQTLs. As a variant affecting both a gene in cis and in trans suggests that the cis gene is functionally linked to the trans gene expression, we looked specifically for trans effects of cis-eQTLs. We discovered that 26 cis-eQTLs are associated to 92 genes in trans with the cis-eQTLs of the transcriptions factors BATF3 and HMX2 affecting the most genes. We then explored if the variation of the level of expression of the cis genes were causally affecting the level of expression of the trans genes and discovered several causal relationships between variation in the level of expression of the cis gene and variation of the level of expression of the trans gene. This analysis shows that a large sample size allows the discovery of secondary effects of human variations on gene expression that can be used to construct short directed gene regulatory networks.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid24759409,

title = {Guidelines for investigating causality of sequence variants in human disease},

author = {D G MacArthur and T A Manolio and D P Dimmock and H L Rehm and J Shendure and G R Abecasis and D R Adams and R B Altman and S E Antonarakis and E A Ashley and J C Barrett and L G Biesecker and D F Conrad and G M Cooper and N J Cox and M J Daly and M B Gerstein and D B Goldstein and J N Hirschhorn and S M Leal and L A Pennacchio and J A Stamatoyannopoulos and S R Sunyaev and D Valle and B F Voight and W Winckler and C Gunter},

doi = {10.1038/nature13127},

issn = {1476-4687},

year  = {2014},

date = {2014-04-01},

journal = {Nature},

volume = {508},

number = {7497},

pages = {469--476},

abstract = {The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid23871472,

title = {Reciprocal duplication of the Williams-Beuren syndrome deletion on chromosome 7q11.23 is associated with schizophrenia},

author = {Jennifer Gladys Mulle and Ann E Pulver and John A McGrath and Paula S Wolyniec and Anne F Dodd and David J Cutler and Jonathan Sebat and Dheeraj Malhotra and Gerald Nestadt and Donald F Conrad and Matthew Hurles and Chris P Barnes and Masashi Ikeda and Nakao Iwata and Douglas F Levinson and Pablo V Gejman and Alan R Sanders and Jubao Duan and Adele A Mitchell and Inga Peter and Pamela Sklar and Colm T O'Dushlaine and Detelina Grozeva and Michael C O'Donovan and Michael J Owen and Christina M Hultman and Anna K Kähler and Patrick F Sullivan and  and George Kirov and Stephen T Warren},

doi = {10.1016/j.biopsych.2013.05.040},

issn = {1873-2402},

year  = {2014},

date = {2014-03-01},

journal = {Biol Psychiatry},

volume = {75},

number = {5},

pages = {371--377},

abstract = {BACKGROUND: Several copy number variants (CNVs) have been implicated as susceptibility factors for schizophrenia (SZ). Some of these same CNVs also increase risk for autism spectrum disorders, suggesting an etiologic overlap between these conditions. Recently, de novo duplications of a region on chromosome 7q11.23 were associated with autism spectrum disorders. The reciprocal deletion of this region causes Williams-Beuren syndrome.nnMETHODS: We assayed an Ashkenazi Jewish cohort of 554 SZ cases and 1014 controls for genome-wide CNV. An excess of large rare and de novo CNVs were observed, including a 1.4 Mb duplication on chromosome 7q11.23 identified in two unrelated patients. To test whether this 7q11.23 duplication is also associated with SZ, we obtained data for 14,387 SZ cases and 28,139 controls from seven additional studies with high-resolution genome-wide CNV detection. We performed a meta-analysis, correcting for study population of origin, to assess whether the duplication is associated with SZ.nnRESULTS: We found duplications at 7q11.23 in 11 of 14,387 SZ cases with only 1 in 28,139 control subjects (unadjusted odds ratio 21.52, 95% confidence interval: 3.13-922.6, p value 5.5 × 10(-5); adjusted odds ratio 10.8, 95% confidence interval: 1.46-79.62, p value .007). Of three SZ duplication carriers with detailed retrospective data, all showed social anxiety and language delay premorbid to SZ onset, consistent with both human studies and animal models of the 7q11.23 duplication.nnCONCLUSIONS: We have identified a new CNV associated with SZ. Reciprocal duplication of the Williams-Beuren syndrome deletion at chromosome 7q11.23 confers an approximately tenfold increase in risk for SZ.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid24127788,

title = {Reduced burden of very large and rare CNVs in bipolar affective disorder},

author = {Detelina Grozeva and George Kirov and Donald F Conrad and Chris P Barnes and Matthew Hurles and Michael J Owen and Michael C O'Donovan and Nick Craddock},

doi = {10.1111/bdi.12125},

issn = {1399-5618},

year  = {2013},

date = {2013-12-01},

journal = {Bipolar Disord},

volume = {15},

number = {8},

pages = {893--898},

abstract = {OBJECTIVES: Large, rare chromosomal copy number variants (CNVs) have been shown to increase the risk for schizophrenia and other neuropsychiatric disorders including autism, attention-deficit hyperactivity disorder, learning difficulties, and epilepsy. Their role in bipolar disorder (BD) is less clear. There are no reports of an increase in large, rare CNVs in BD in general, but some have reported an increase in early-onset cases. We previously found that the rate of such CNVs in individuals with BD was not increased, even in early-onset cases. Our aim here was to examine the rate of large rare CNVs in BD in comparison with a new large independent reference sample from the same country.nnMETHODS: We studied the CNVs in a case-control sample consisting of 1,650 BD cases (reported previously) and 10,259 reference individuals without a known psychiatric disorder who took part in the original Wellcome Trust Case Control Consortium (WTCCC) study. The 10,259 reference individuals were affected with six non-psychiatric disorders (coronary artery disease, types 1 and 2 diabetes, hypertension, Crohn's disease, and rheumatoid arthritis). Affymetrix 500K array genotyping data were used to call the CNVs.nnRESULTS: The rate of CNVs > 100 kb was not statistically different between cases and controls. The rate of very large (defined as > 1 Mb) and rare (< 1%) CNVs was significantly lower in patients with BD compared with the reference group. CNV loci associated with schizophrenia were not enriched in BD and, in fact, cases of BD had the lowest number of such CNVs compared with any of the WTCCC cohorts; this finding held even for the early-onset BD cases.nnCONCLUSIONS: Schizophrenia and BD differ with respect to CNV burden and association with specific CNVs. Our findings support the hypothesis that BD is etiologically distinct from schizophrenia with respect to large, rare CNVs and the accompanying associated neurodevelopmental abnormalities.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid23975140,

title = {DeNovoGear: de novo indel and point mutation discovery and phasing},

author = {Avinash Ramu and Michiel J Noordam and Rachel S Schwartz and Arthur Wuster and Matthew E Hurles and Reed A Cartwright and Donald F Conrad},

doi = {10.1038/nmeth.2611},

issn = {1548-7105},

year  = {2013},

date = {2013-10-01},

urldate = {2013-10-01},

journal = {Nat Methods},

volume = {10},

number = {10},

pages = {985--987},

abstract = {We present DeNovoGear software for analyzing de novo mutations from familial and somatic tissue sequencing data. DeNovoGear uses likelihood-based error modeling to reduce the false positive rate of mutation discovery in exome analysis and fragment information to identify the parental origin of germ-line mutations. We used DeNovoGear on human whole-genome sequencing data to produce a set of predicted de novo insertion and/or deletion (indel) mutations with a 95% validation rate.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid23593153,

title = {Novel loci associated with increased risk of sudden cardiac death in the context of coronary artery disease},

author = {Adriana Huertas-Vazquez and Christopher P Nelson and Xiuqing Guo and Kyndaron Reinier and Audrey Uy-Evanado and Carmen Teodorescu and Jo Ayala and Katherine Jerger and Harpriya Chugh and  and Peter S Braund and Panos Deloukas and Alistair S Hall and Anthony J Balmforth and Michelle Jones and Kent D Taylor and Sara L Pulit and Christopher Newton-Cheh and Karen Gunson and Jonathan Jui and Jerome I Rotter and Christine M Albert and Nilesh J Samani and Sumeet S Chugh},

doi = {10.1371/journal.pone.0059905},

issn = {1932-6203},

year  = {2013},

date = {2013-01-01},

journal = {PLoS One},

volume = {8},

number = {4},

pages = {e59905},

abstract = {BACKGROUND: Recent genome-wide association studies (GWAS) have identified novel loci associated with sudden cardiac death (SCD). Despite this progress, identified DNA variants account for a relatively small portion of overall SCD risk, suggesting that additional loci contributing to SCD susceptibility await discovery. The objective of this study was to identify novel DNA variation associated with SCD in the context of coronary artery disease (CAD).nnMETHODS AND FINDINGS: Using the MetaboChip custom array we conducted a case-control association analysis of 119,117 SNPs in 948 SCD cases (with underlying CAD) from the Oregon Sudden Unexpected Death Study (Oregon-SUDS) and 3,050 controls with CAD from the Wellcome Trust Case-Control Consortium (WTCCC). Two newly identified loci were significantly associated with increased risk of SCD after correction for multiple comparisons at: rs6730157 in the RAB3GAP1 gene on chromosome 2 (P = 4.93×10(-12), OR = 1.60) and rs2077316 in the ZNF365 gene on chromosome 10 (P = 3.64×10(-8), OR = 2.41).nnCONCLUSIONS: Our findings suggest that RAB3GAP1 and ZNF365 are relevant candidate genes for SCD and will contribute to the mechanistic understanding of SCD susceptibility.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid23242139,

title = {Mosaic PPM1D mutations are associated with predisposition to breast and ovarian cancer},

author = {Elise Ruark and Katie Snape and Peter Humburg and Chey Loveday and Ilirjana Bajrami and Rachel Brough and Daniel Nava Rodrigues and Anthony Renwick and Sheila Seal and Emma Ramsay and Silvana Del Vecchio Duarte and Manuel A Rivas and Margaret Warren-Perry and Anna Zachariou and Adriana Campion-Flora and Sandra Hanks and Anne Murray and Naser Ansari Pour and Jenny Douglas and Lorna Gregory and Andrew Rimmer and Neil M Walker and Tsun-Po Yang and Julian W Adlard and Julian Barwell and Jonathan Berg and Angela F Brady and Carole Brewer and Glen Brice and Cyril Chapman and Jackie Cook and Rosemarie Davidson and Alan Donaldson and Fiona Douglas and Diana Eccles and D Gareth Evans and Lynn Greenhalgh and Alex Henderson and Louise Izatt and Ajith Kumar and Fiona Lalloo and Zosia Miedzybrodzka and Patrick J Morrison and Joan Paterson and Mary Porteous and Mark T Rogers and Susan Shanley and Lisa Walker and Martin Gore and Richard Houlston and Matthew A Brown and Mark J Caufield and Panagiotis Deloukas and Mark I McCarthy and John A Todd and  and  and Clare Turnbull and Jorge S Reis-Filho and Alan Ashworth and Antonis C Antoniou and Christopher J Lord and Peter Donnelly and Nazneen Rahman},

doi = {10.1038/nature11725},

issn = {1476-4687},

year  = {2013},

date = {2013-01-01},

journal = {Nature},

volume = {493},

number = {7432},

pages = {406--410},

abstract = {Improved sequencing technologies offer unprecedented opportunities for investigating the role of rare genetic variation in common disease. However, there are considerable challenges with respect to study design, data analysis and replication. Using pooled next-generation sequencing of 507 genes implicated in the repair of DNA in 1,150 samples, an analytical strategy focused on protein-truncating variants (PTVs) and a large-scale sequencing case-control replication experiment in 13,642 individuals, here we show that rare PTVs in the p53-inducible protein phosphatase PPM1D are associated with predisposition to breast cancer and ovarian cancer. PPM1D PTV mutations were present in 25 out of 7,781 cases versus 1 out of 5,861 controls (P = 1.12 × 10(-5)), including 18 mutations in 6,912 individuals with breast cancer (P = 2.42 × 10(-4)) and 12 mutations in 1,121 individuals with ovarian cancer (P = 3.10 × 10(-9)). Notably, all of the identified PPM1D PTVs were mosaic in lymphocyte DNA and clustered within a 370-base-pair region in the final exon of the gene, carboxy-terminal to the phosphatase catalytic domain. Functional studies demonstrate that the mutations result in enhanced suppression of p53 in response to ionizing radiation exposure, suggesting that the mutant alleles encode hyperactive PPM1D isoforms. Thus, although the mutations cause premature protein truncation, they do not result in the simple loss-of-function effect typically associated with this class of variant, but instead probably have a gain-of-function effect. Our results have implications for the detection and management of breast and ovarian cancer risk. More generally, these data provide new insights into the role of rare and of mosaic genetic variants in common conditions, and the use of sequencing in their identification.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid23104008,

title = {Bayesian refinement of association signals for 14 loci in 3 common diseases},

author = { and Julian B Maller and Gilean McVean and Jake Byrnes and Damjan Vukcevic and Kimmo Palin and Zhan Su and Joanna M M Howson and Adam Auton and Simon Myers and Andrew Morris and Matti Pirinen and Matthew A Brown and Paul R Burton and Mark J Caulfield and Alastair Compston and Martin Farrall and Alistair S Hall and Andrew T Hattersley and Adrian V S Hill and Christopher G Mathew and Marcus Pembrey and Jack Satsangi and Michael R Stratton and Jane Worthington and Nick Craddock and Matthew Hurles and Willem Ouwehand and Miles Parkes and Nazneen Rahman and Audrey Duncanson and John A Todd and Dominic P Kwiatkowski and Nilesh J Samani and Stephen C L Gough and Mark I McCarthy and Panagiotis Deloukas and Peter Donnelly},

doi = {10.1038/ng.2435},

issn = {1546-1718},

year  = {2012},

date = {2012-12-01},

journal = {Nat Genet},

volume = {44},

number = {12},

pages = {1294--1301},

abstract = {To further investigate susceptibility loci identified by genome-wide association studies, we genotyped 5,500 SNPs across 14 associated regions in 8,000 samples from a control group and 3 diseases: type 2 diabetes (T2D), coronary artery disease (CAD) and Graves' disease. We defined, using Bayes theorem, credible sets of SNPs that were 95% likely, based on posterior probability, to contain the causal disease-associated SNPs. In 3 of the 14 regions, TCF7L2 (T2D), CTLA4 (Graves' disease) and CDKN2A-CDKN2B (T2D), much of the posterior probability rested on a single SNP, and, in 4 other regions (CDKN2A-CDKN2B (CAD) and CDKAL1, FTO and HHEX (T2D)), the 95% sets were small, thereby excluding most SNPs as potentially causal. Very few SNPs in our credible sets had annotated functions, illustrating the limitations in understanding the mechanisms underlying susceptibility to common diseases. Our results also show the value of more detailed mapping to target sequences for functional studies.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid22922229,

title = {Seven newly identified loci for autoimmune thyroid disease},

author = {Jason D Cooper and Matthew J Simmonds and Neil M Walker and Oliver Burren and Oliver J Brand and Hui Guo and Chris Wallace and Helen Stevens and Gillian Coleman and  and Jayne A Franklyn and John A Todd and Stephen C L Gough},

doi = {10.1093/hmg/dds357},

issn = {1460-2083},

year  = {2012},

date = {2012-12-01},

journal = {Hum Mol Genet},

volume = {21},

number = {23},

pages = {5202--5208},

abstract = {Autoimmune thyroid disease (AITD), including Graves' disease (GD) and Hashimoto's thyroiditis (HT), is one of the most common of the immune-mediated diseases. To further investigate the genetic determinants of AITD, we conducted an association study using a custom-made single-nucleotide polymorphism (SNP) array, the ImmunoChip. The SNP array contains all known and genotype-able SNPs across 186 distinct susceptibility loci associated with one or more immune-mediated diseases. After stringent quality control, we analysed 103 875 common SNPs (minor allele frequency >0.05) in 2285 GD and 462 HT patients and 9364 controls. We found evidence for seven new AITD risk loci (P < 1.12 × 10(-6); a permutation test derived significance threshold), five at locations previously associated and two at locations awaiting confirmation, with other immune-mediated diseases.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid23128233,

title = {Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease},

author = {Luke Jostins and Stephan Ripke and Rinse K Weersma and Richard H Duerr and Dermot P McGovern and Ken Y Hui and James C Lee and L Philip Schumm and Yashoda Sharma and Carl A Anderson and Jonah Essers and Mitja Mitrovic and Kaida Ning and Isabelle Cleynen and Emilie Theatre and Sarah L Spain and Soumya Raychaudhuri and Philippe Goyette and Zhi Wei and Clara Abraham and Jean-Paul Achkar and Tariq Ahmad and Leila Amininejad and Ashwin N Ananthakrishnan and Vibeke Andersen and Jane M Andrews and Leonard Baidoo and Tobias Balschun and Peter A Bampton and Alain Bitton and Gabrielle Boucher and Stephan Brand and Carsten Büning and Ariella Cohain and Sven Cichon and Mauro D'Amato and Dirk De Jong and Kathy L Devaney and Marla Dubinsky and Cathryn Edwards and David Ellinghaus and Lynnette R Ferguson and Denis Franchimont and Karin Fransen and Richard Gearry and Michel Georges and Christian Gieger and Jürgen Glas and Talin Haritunians and Ailsa Hart and Chris Hawkey and Matija Hedl and Xinli Hu and Tom H Karlsen and Limas Kupcinskas and Subra Kugathasan and Anna Latiano and Debby Laukens and Ian C Lawrance and Charlie W Lees and Edouard Louis and Gillian Mahy and John Mansfield and Angharad R Morgan and Craig Mowat and William Newman and Orazio Palmieri and Cyriel Y Ponsioen and Uros Potocnik and Natalie J Prescott and Miguel Regueiro and Jerome I Rotter and Richard K Russell and Jeremy D Sanderson and Miquel Sans and Jack Satsangi and Stefan Schreiber and Lisa A Simms and Jurgita Sventoraityte and Stephan R Targan and Kent D Taylor and Mark Tremelling and Hein W Verspaget and Martine De Vos and Cisca Wijmenga and David C Wilson and Juliane Winkelmann and Ramnik J Xavier and Sebastian Zeissig and Bin Zhang and Clarence K Zhang and Hongyu Zhao and  and Mark S Silverberg and Vito Annese and Hakon Hakonarson and Steven R Brant and Graham Radford-Smith and Christopher G Mathew and John D Rioux and Eric E Schadt and Mark J Daly and Andre Franke and Miles Parkes and Severine Vermeire and Jeffrey C Barrett and Judy H Cho},

doi = {10.1038/nature11582},

issn = {1476-4687},

year  = {2012},

date = {2012-11-01},

journal = {Nature},

volume = {491},

number = {7422},

pages = {119--124},

abstract = {Crohn's disease and ulcerative colitis, the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry, with rising prevalence in other populations. Genome-wide association studies and subsequent meta-analyses of these two diseases as separate phenotypes have implicated previously unsuspected mechanisms, such as autophagy, in their pathogenesis and showed that some IBD loci are shared with other inflammatory diseases. Here we expand on the knowledge of relevant pathways by undertaking a meta-analysis of Crohn's disease and ulcerative colitis genome-wide association scans, followed by extensive validation of significant findings, with a combined total of more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci, that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional (consistently favouring one allele over the course of human history) and balancing (favouring the retention of both alleles within populations) selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe considerable overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid22057783,

title = {Exploration of signals of positive selection derived from genotype-based human genome scans using re-sequencing data},

author = {Min Hu and Qasim Ayub and José Afonso Guerra-Assunção and Quan Long and Zemin Ning and Ni Huang and Irene Gallego Romero and Lira Mamanova and Pelin Akan and Xin Liu and Alison J Coffey and Daniel J Turner and Harold Swerdlow and John Burton and Michael A Quail and Donald F Conrad and Anton J Enright and Chris Tyler-Smith and Yali Xue},

doi = {10.1007/s00439-011-1111-9},

issn = {1432-1203},

year  = {2012},

date = {2012-05-01},

journal = {Hum Genet},

volume = {131},

number = {5},

pages = {665--674},

abstract = {We have investigated whether regions of the genome showing signs of positive selection in scans based on haplotype structure also show evidence of positive selection when sequence-based tests are applied, whether the target of selection can be localized more precisely, and whether such extra evidence can lead to increased biological insights. We used two tools: simulations under neutrality or selection, and experimental investigation of two regions identified by the HapMap2 project as putatively selected in human populations. Simulations suggested that neutral and selected regions should be readily distinguished and that it should be possible to localize the selected variant to within 40 kb at least half of the time. Re-sequencing of two ~300 kb regions (chr4:158Mb and chr10:22Mb) lacking known targets of selection in HapMap CHB individuals provided strong evidence for positive selection within each and suggested the micro-RNA gene hsa-miR-548c as the best candidate target in one region, and changes in regulation of the sperm protein gene SPAG6 in the other.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid22543379,

title = {The 1000 Genomes Project: data management and community access},

author = {Laura Clarke and Xiangqun Zheng-Bradley and Richard Smith and Eugene Kulesha and Chunlin Xiao and Iliana Toneva and Brendan Vaughan and Don Preuss and Rasko Leinonen and Martin Shumway and Stephen Sherry and Paul Flicek and },

doi = {10.1038/nmeth.1974},

issn = {1548-7105},

year  = {2012},

date = {2012-04-01},

journal = {Nat Methods},

volume = {9},

number = {5},

pages = {459--462},

abstract = {The 1000 Genomes Project was launched as one of the largest distributed data collection and analysis projects ever undertaken in biology. In addition to the primary scientific goals of creating both a deep catalog of human genetic variation and extensive methods to accurately discover and characterize variation using new sequencing technologies, the project makes all of its data publicly available. Members of the project data coordination center have developed and deployed several tools to enable widespread data access.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid22130109,

title = {Independent estimation of the frequency of rare CNVs in the UK population confirms their role in schizophrenia},

author = {Detelina Grozeva and Donald F Conrad and Chris P Barnes and Matthew Hurles and Michael J Owen and Michael C O'Donovan and Nick Craddock and George Kirov and },

doi = {10.1016/j.schres.2011.11.004},

issn = {1573-2509},

year  = {2012},

date = {2012-03-01},

journal = {Schizophr Res},

volume = {135},

number = {1-3},

pages = {1--7},

abstract = {BACKGROUND: Several large, rare chromosomal copy number variants (CNVs) have recently been shown to increase risk for schizophrenia and other neuropsychiatric disorders including autism, ADHD, learning difficulties and epilepsy.nnAIMS: We wanted to examine the frequencies of these schizophrenia-associated variants in a large sample of individuals with non-psychiatric illnesses to better understand the robustness and specificity of the association with schizophrenia.nnMETHODS: We used Affymetrix 500K microarray data from 10,259 individuals from the UK Wellcome Trust Case Control Consortium (WTCCC) who are affected with six non-psychiatric disorders (coronary artery disease, Crohn's disease, hypertension, rheumatoid arthritis, types 1 and 2 diabetes) to establish the frequencies of nine CNV loci strongly implicated in schizophrenia, and compared them with the previous findings.nnRESULTS: Deletions at 1q21.1, 3q29, 15q11.2, 15q13.1 and 22q11.2 (VCFS region), and duplications at 16p11.2 were found significantly more often in schizophrenia cases, compared with the WTCCC reference set. Deletions at 17p12 and 17q12, were also more common in schizophrenia cases but not significantly so, while duplications at 16p13.1 were found at nearly the same rate as in previous schizophrenia samples. The frequencies of CNVs in the WTCCC non-psychiatric controls at three of the loci (15q11.2, 16p13.1 and 17p12) were significantly higher than those reported in previous control populations.nnCONCLUSIONS: The evidence for association with schizophrenia is compelling for six rare CNV loci, while the remaining three require further replication in large studies. Risk at these loci extends to other neurodevelopmental disorders but their involvement in common non-psychiatric disorders should also be investigated.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid22344438,

title = {A systematic survey of loss-of-function variants in human protein-coding genes},

author = {Daniel G MacArthur and Suganthi Balasubramanian and Adam Frankish and Ni Huang and James Morris and Klaudia Walter and Luke Jostins and Lukas Habegger and Joseph K Pickrell and Stephen B Montgomery and Cornelis A Albers and Zhengdong D Zhang and Donald F Conrad and Gerton Lunter and Hancheng Zheng and Qasim Ayub and Mark A DePristo and Eric Banks and Min Hu and Robert E Handsaker and Jeffrey A Rosenfeld and Menachem Fromer and Mike Jin and Xinmeng Jasmine Mu and Ekta Khurana and Kai Ye and Mike Kay and Gary Ian Saunders and Marie-Marthe Suner and Toby Hunt and If H A Barnes and Clara Amid and Denise R Carvalho-Silva and Alexandra H Bignell and Catherine Snow and Bryndis Yngvadottir and Suzannah Bumpstead and David N Cooper and Yali Xue and Irene Gallego Romero and  and Jun Wang and Yingrui Li and Richard A Gibbs and Steven A McCarroll and Emmanouil T Dermitzakis and Jonathan K Pritchard and Jeffrey C Barrett and Jennifer Harrow and Matthew E Hurles and Mark B Gerstein and Chris Tyler-Smith},

doi = {10.1126/science.1215040},

issn = {1095-9203},

year  = {2012},

date = {2012-02-01},

journal = {Science},

volume = {335},

number = {6070},

pages = {823--828},

abstract = {Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid21917140,

title = {The functional spectrum of low-frequency coding variation},

author = {Gabor T Marth and Fuli Yu and Amit R Indap and Kiran Garimella and Simon Gravel and Wen Fung Leong and Chris Tyler-Smith and Matthew Bainbridge and Tom Blackwell and Xiangqun Zheng-Bradley and Yuan Chen and Danny Challis and Laura Clarke and Edward V Ball and Kristian Cibulskis and David N Cooper and Bob Fulton and Chris Hartl and Dan Koboldt and Donna Muzny and Richard Smith and Carrie Sougnez and Chip Stewart and Alistair Ward and Jin Yu and Yali Xue and David Altshuler and Carlos D Bustamante and Andrew G Clark and Mark Daly and Mark DePristo and Paul Flicek and Stacey Gabriel and Elaine Mardis and Aarno Palotie and Richard Gibbs and },

doi = {10.1186/gb-2011-12-9-r84},

issn = {1474-760X},

year  = {2011},

date = {2011-09-01},

journal = {Genome Biol},

volume = {12},

number = {9},

pages = {R84},

abstract = {BACKGROUND: Rare coding variants constitute an important class of human genetic variation, but are underrepresented in current databases that are based on small population samples. Recent studies show that variants altering amino acid sequence and protein function are enriched at low variant allele frequency, 2 to 5%, but because of insufficient sample size it is not clear if the same trend holds for rare variants below 1% allele frequency.nnRESULTS: The 1000 Genomes Exon Pilot Project has collected deep-coverage exon-capture data in roughly 1,000 human genes, for nearly 700 samples. Although medical whole-exome projects are currently afoot, this is still the deepest reported sampling of a large number of human genes with next-generation technologies. According to the goals of the 1000 Genomes Project, we created effective informatics pipelines to process and analyze the data, and discovered 12,758 exonic SNPs, 70% of them novel, and 74% below 1% allele frequency in the seven population samples we examined. Our analysis confirms that coding variants below 1% allele frequency show increased population-specificity and are enriched for functional variants.nnCONCLUSIONS: This study represents a large step toward detecting and interpreting low frequency coding variation, clearly lays out technical steps for effective analysis of DNA capture data, and articulates functional and population properties of this important class of genetic variation.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid21730125,

title = {Demographic history and rare allele sharing among human populations},

author = {Simon Gravel and Brenna M Henn and Ryan N Gutenkunst and Amit R Indap and Gabor T Marth and Andrew G Clark and Fuli Yu and Richard A Gibbs and  and Carlos D Bustamante},

doi = {10.1073/pnas.1019276108},

issn = {1091-6490},

year  = {2011},

date = {2011-07-01},

journal = {Proc Natl Acad Sci U S A},

volume = {108},

number = {29},

pages = {11983--11988},

abstract = {High-throughput sequencing technology enables population-level surveys of human genomic variation. Here, we examine the joint allele frequency distributions across continental human populations and present an approach for combining complementary aspects of whole-genome, low-coverage data and targeted high-coverage data. We apply this approach to data generated by the pilot phase of the Thousand Genomes Project, including whole-genome 2-4× coverage data for 179 samples from HapMap European, Asian, and African panels as well as high-coverage target sequencing of the exons of 800 genes from 697 individuals in seven populations. We use the site frequency spectra obtained from these data to infer demographic parameters for an Out-of-Africa model for populations of African, European, and Asian descent and to predict, by a jackknife-based approach, the amount of genetic diversity that will be discovered as sample sizes are increased. We predict that the number of discovered nonsynonymous coding variants will reach 100,000 in each population after ∼1,000 sequenced chromosomes per population, whereas ∼2,500 chromosomes will be needed for the same number of synonymous variants. Beyond this point, the number of segregating sites in the European and Asian panel populations is expected to overcome that of the African panel because of faster recent population growth. Overall, we find that the majority of human genomic variable sites are rare and exhibit little sharing among diverged populations. Our results emphasize that replication of disease association for specific rare genetic variants across diverged populations must overcome both reduced statistical power because of rarity and higher population divergence.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid21666693,

title = {Variation in genome-wide mutation rates within and between human families},

author = {Donald F Conrad and Jonathan E M Keebler and Mark A DePristo and Sarah J Lindsay and Yujun Zhang and Ferran Casals and Youssef Idaghdour and Chris L Hartl and Carlos Torroja and Kiran V Garimella and Martine Zilversmit and Reed Cartwright and Guy A Rouleau and Mark Daly and Eric A Stone and Matthew E Hurles and Philip Awadalla and },

doi = {10.1038/ng.862},

issn = {1546-1718},

year  = {2011},

date = {2011-06-01},

journal = {Nat Genet},

volume = {43},

number = {7},

pages = {712--714},

abstract = {J.B.S. Haldane proposed in 1947 that the male germline may be more mutagenic than the female germline. Diverse studies have supported Haldane's contention of a higher average mutation rate in the male germline in a variety of mammals, including humans. Here we present, to our knowledge, the first direct comparative analysis of male and female germline mutation rates from the complete genome sequences of two parent-offspring trios. Through extensive validation, we identified 49 and 35 germline de novo mutations (DNMs) in two trio offspring, as well as 1,586 non-germline DNMs arising either somatically or in the cell lines from which the DNA was derived. Most strikingly, in one family, we observed that 92% of germline DNMs were from the paternal germline, whereas, in contrast, in the other family, 64% of DNMs were from the maternal germline. These observations suggest considerable variation in mutation rates within and between families.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}

@article{pmid21293372,

title = {Mapping copy number variation by population-scale genome sequencing},

author = {Ryan E Mills and Klaudia Walter and Chip Stewart and Robert E Handsaker and Ken Chen and Can Alkan and Alexej Abyzov and Seungtai Chris Yoon and Kai Ye and R Keira Cheetham and Asif Chinwalla and Donald F Conrad and Yutao Fu and Fabian Grubert and Iman Hajirasouliha and Fereydoun Hormozdiari and Lilia M Iakoucheva and Zamin Iqbal and Shuli Kang and Jeffrey M Kidd and Miriam K Konkel and Joshua Korn and Ekta Khurana and Deniz Kural and Hugo Y K Lam and Jing Leng and Ruiqiang Li and Yingrui Li and Chang-Yun Lin and Ruibang Luo and Xinmeng Jasmine Mu and James Nemesh and Heather E Peckham and Tobias Rausch and Aylwyn Scally and Xinghua Shi and Michael P Stromberg and Adrian M Stütz and Alexander Eckehart Urban and Jerilyn A Walker and Jiantao Wu and Yujun Zhang and Zhengdong D Zhang and Mark A Batzer and Li Ding and Gabor T Marth and Gil McVean and Jonathan Sebat and Michael Snyder and Jun Wang and Kenny Ye and Evan E Eichler and Mark B Gerstein and Matthew E Hurles and Charles Lee and Steven A McCarroll and Jan O Korbel and },

doi = {10.1038/nature09708},

issn = {1476-4687},

year  = {2011},

date = {2011-02-01},

journal = {Nature},

volume = {470},

number = {7332},

pages = {59--65},

abstract = {Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.},

keywords = {Genomic Variation},

pubstate = {published},

tppubtype = {article}

}