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Minikel, Eric

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Minikel

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Eric

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Minikel, Eric
Author's Publications: search.filters.isAuthorOfPublication.9388fe83-29f9-4111-a838-012a88a1d1f8

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    Analysis of protein-coding genetic variation in 60,706 humans
    (2016) Lek, Monkol; Karczewski, Konrad; Minikel, Eric; Samocha, Kaitlin E.; Banks, Eric; Fennell, Timothy; O'Donnell-Luria, Anne H; Ware, James S; Hill, Andrew J; Cummings, Beryl; Tukiainen, Taru; Birnbaum, Daniel P; Kosmicki, Jack; Duncan, Laramie E; Estrada, Karol; Zhao, Fengmei; Zou, James; Pierce-Hoffman, Emma; Berghout, Joanne; Cooper, David N; Deflaux, Nicole; DePristo, Mark; Do, Ron; Flannick, Jason; Fromer, Menachem; Gauthier, Laura; Goldstein, Jackie; Gupta, Namrata; Howrigan, Daniel; Kiezun, Adam; Kurki, Mitja; Moonshine, Ami Levy; Natarajan, Pradeep; Orozco, Lorena; Peloso, Gina M; Poplin, Ryan; Rivas, Manuel A; Ruano-Rubio, Valentin; Rose, Samuel A; Ruderfer, Douglas M; Shakir, Khalid; Stenson, Peter D; Stevens, Christine; Thomas, Brett P; Tiao, Grace; Tusie-Luna, Maria T; Weisburd, Ben; Won, Hong-Hee; Yu, Dongmei; Altshuler, David; Ardissino, Diego; Boehnke, Michael; Danesh, John; Donnelly, Stacey; Elosua, Roberto; Florez, Jose; Gabriel, Stacey B; Getz, Gad; Glatt, Stephen J; Hultman, Christina M; Kathiresan, Sekar; Laakso, Markku; McCarroll, Steven; McCarthy, Mark I; McGovern, Dermot; McPherson, Ruth; Neale, Benjamin; Palotie, Aarno; Purcell, Shaun M; Saleheen, Danish; Scharf, Jeremiah; Sklar, Pamela; Sullivan, Patrick F; Tuomilehto, Jaakko; Tsuang, Ming T; Watkins, Hugh C; Wilson, James G; Daly, Mark; MacArthur, Daniel
    Summary Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.
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    Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples
    (Nature Publishing Group, 2017) Walsh, Roddy; Thomson, Kate L.; Ware, James S.; Funke, Birgit; Woodley, Jessica; McGuire, Karen J.; Mazzarotto, Francesco; Blair, Edward; Seller, Anneke; Taylor, Jenny C.; Minikel, Eric; Exome Aggregation Consortium; MacArthur, Daniel; Farrall, Martin; Cook, Stuart A.; Watkins, Hugh
    Purpose: The accurate interpretation of variation in Mendelian disease genes has lagged behind data generation as sequencing has become increasingly accessible. Ongoing large sequencing efforts present huge interpretive challenges, but they also provide an invaluable opportunity to characterize the spectrum and importance of rare variation. Methods: We analyzed sequence data from 7,855 clinical cardiomyopathy cases and 60,706 Exome Aggregation Consortium (ExAC) reference samples to obtain a better understanding of genetic variation in a representative autosomal dominant disorder. Results: We found that in some genes previously reported as important causes of a given cardiomyopathy, rare variation is not clinically informative because there is an unacceptably high likelihood of false-positive interpretation. By contrast, in other genes, we find that diagnostic laboratories may be overly conservative when assessing variant pathogenicity. Conclusions: We outline improved analytical approaches that evaluate which genes and variant classes are interpretable and propose that these will increase the clinical utility of testing across a range of Mendelian diseases. Genet Med 19 2, 192–203.
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    Insights into the genetic epidemiology of Crohn's and rare diseases in the Ashkenazi Jewish population
    (Public Library of Science, 2018) Rivas, Manuel A.; Avila, Brandon E.; Koskela, Jukka; Huang, Hailiang; Stevens, Christine; Pirinen, Matti; Haritunians, Talin; Neale, Benjamin; Kurki, Mitja; Ganna, Andrea; Graham, Daniel; Glaser, Benjamin; Peter, Inga; Atzmon, Gil; Barzilai, Nir; Levine, Adam P.; Schiff, Elena; Pontikos, Nikolas; Weisburd, Ben; Lek, Monkol; Karczewski, Konrad; Bloom, Jonathan; Minikel, Eric; Petersen, Britt-Sabina; Beaugerie, Laurent; Seksik, Philippe; Cosnes, Jacques; Schreiber, Stefan; Bokemeyer, Bernd; Bethge, Johannes; Heap, Graham; Ahmad, Tariq; Plagnol, Vincent; Segal, Anthony W.; Targan, Stephan; Turner, Dan; Saavalainen, Paivi; Farkkila, Martti; Kontula, Kimmo; Palotie, Aarno; Brant, Steven R.; Duerr, Richard H.; Silverberg, Mark S.; Rioux, John D.; Weersma, Rinse K.; Franke, Andre; Jostins, Luke; Anderson, Carl A.; Barrett, Jeffrey C.; MacArthur, Daniel; Jalas, Chaim; Sokol, Harry; Xavier, Ramnik; Pulver, Ann; Cho, Judy H.; McGovern, Dermot P. B.; Daly, Mark
    As part of a broader collaborative network of exome sequencing studies, we developed a jointly called data set of 5,685 Ashkenazi Jewish exomes. We make publicly available a resource of site and allele frequencies, which should serve as a reference for medical genetics in the Ashkenazim (hosted in part at https://ibd.broadinstitute.org, also available in gnomAD at http://gnomad.broadinstitute.org). We estimate that 34% of protein-coding alleles present in the Ashkenazi Jewish population at frequencies greater than 0.2% are significantly more frequent (mean 15-fold) than their maximum frequency observed in other reference populations. Arising via a well-described founder effect approximately 30 generations ago, this catalog of enriched alleles can contribute to differences in genetic risk and overall prevalence of diseases between populations. As validation we document 148 AJ enriched protein-altering alleles that overlap with "pathogenic" ClinVar alleles (table available at https://github.com/macarthur-lab/clinvar/blob/master/output/clinvar.tsv), including those that account for 10–100 fold differences in prevalence between AJ and non-AJ populations of some rare diseases, especially recessive conditions, including Gaucher disease (GBA, p.Asn409Ser, 8-fold enrichment); Canavan disease (ASPA, p.Glu285Ala, 12-fold enrichment); and Tay-Sachs disease (HEXA, c.1421+1G>C, 27-fold enrichment; p.Tyr427IlefsTer5, 12-fold enrichment). We next sought to use this catalog, of well-established relevance to Mendelian disease, to explore Crohn's disease, a common disease with an estimated two to four-fold excess prevalence in AJ. We specifically attempt to evaluate whether strong acting rare alleles, particularly protein-truncating or otherwise large effect-size alleles, enriched by the same founder-effect, contribute excess genetic risk to Crohn's disease in AJ, and find that ten rare genetic risk factors in NOD2 and LRRK2 are enriched in AJ (p < 0.005), including several novel contributing alleles, show evidence of association to CD. Independently, we find that genomewide common variant risk defined by GWAS shows a strong difference between AJ and non-AJ European control population samples (0.97 s.d. higher, p<10−16). Taken together, the results suggest coordinated selection in AJ population for higher CD risk alleles in general. The results and approach illustrate the value of exome sequencing data in case-control studies along with reference data sets like ExAC (sites VCF available via FTP at ftp.broadinstitute.org/pub/ExAC_release/release0.3/) to pinpoint genetic variation that contributes to variable disease predisposition across populations.
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    ClinVar data parsing
    (F1000Research, 2017) Zhang, Xiaolei; Minikel, Eric; O'Donnell-Luria, Anne H.; MacArthur, Daniel; Ware, James S.; Weisburd, Ben
    This software repository provides a pipeline for converting raw ClinVar data files into analysis-friendly tab-delimited tables, and also provides these tables for the most recent ClinVar release. Separate tables are generated for genome builds GRCh37 and GRCh38 as well as for mono-allelic variants and complex multi-allelic variants. Additionally, the tables are augmented with allele frequencies from the ExAC and gnomAD datasets as these are often consulted when analyzing ClinVar variants. Overall, this work provides ClinVar data in a format that is easier to work with and can be directly loaded into a variety of popular analysis tools such as R, python pandas, and SQL databases.
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    Using high-resolution variant frequencies to empower clinical genome interpretation
    (Nature Publishing Group, 2017) Whiffin, Nicola; Minikel, Eric; Walsh, Roddy; O’Donnell-Luria, Anne H; Karczewski, Konrad; Ing, Alexander Y; Barton, Paul J R; Funke, Birgit; Cook, Stuart A; MacArthur, Daniel; Ware, James S
    Purpose Whole-exome and whole-genome sequencing have transformed the discovery of genetic variants that cause human Mendelian disease, but discriminating pathogenic from benign variants remains a daunting challenge. Rarity is recognized as a necessary, although not sufficient, criterion for pathogenicity, but frequency cutoffs used in Mendelian analysis are often arbitrary and overly lenient. Recent very large reference datasets, such as the Exome Aggregation Consortium (ExAC), provide an unprecedented opportunity to obtain robust frequency estimates even for very rare variants. Methods: We present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets. Results: Using the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, without removing true pathogenic variants (false-positive rate<0.001). Conclusion: We outline a statistically robust framework for assessing whether a variant is “too common” to be causative for a Mendelian disorder of interest. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.