@article {841, title = {The GTEx Consortium atlas of genetic regulatory effects across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, 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 = {Datasets as Topic, Disease, Female, Gene Expression Regulation, Genome-Wide Association Study, Humans, Male, Organ Specificity, Quantitative Trait Loci, Sequence Analysis, RNA}, issn = {1095-9203}, doi = {10.1126/science.aaz1776} } @article {859, title = {A missense variant in Mitochondrial Amidoxime Reducing Component 1 gene and protection against liver disease.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 04}, pages = {e1008629}, abstract = {

Analyzing 12,361 all-cause cirrhosis cases and 790,095 controls from eight cohorts, we identify a common missense variant in the Mitochondrial Amidoxime Reducing Component 1 gene (MARC1 p.A165T) that associates with protection from all-cause cirrhosis (OR 0.91, p = 2.3*10-11). This same variant also associates with lower levels of hepatic fat on computed tomographic imaging and lower odds of physician-diagnosed fatty liver as well as lower blood levels of alanine transaminase (-0.025 SD, 3.7*10-43), alkaline phosphatase (-0.025 SD, 1.2*10-37), total cholesterol (-0.030 SD, p = 1.9*10-36) and LDL cholesterol (-0.027 SD, p = 5.1*10-30) levels. We identified a series of additional MARC1 alleles (low-frequency missense p.M187K and rare protein-truncating p.R200Ter) that also associated with lower cholesterol levels, liver enzyme levels and reduced risk of cirrhosis (0 cirrhosis cases for 238 R200Ter carriers versus 17,046 cases of cirrhosis among 759,027 non-carriers, p = 0.04) suggesting that deficiency of the MARC1 enzyme may lower blood cholesterol levels and protect against cirrhosis.

}, keywords = {Alleles, Cholesterol, LDL, Coronary Artery Disease, Datasets as Topic, Fatty Liver, Female, Genetic Predisposition to Disease, Homozygote, Humans, Liver, Liver Cirrhosis, Liver Cirrhosis, Alcoholic, Loss of Function Mutation, Male, Middle Aged, Mitochondrial Proteins, Mutation, Missense, Oxidoreductases}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008629}, author = {Emdin, Connor A and Haas, Mary E and Khera, Amit V and Aragam, Krishna and Chaffin, Mark and Klarin, Derek and Hindy, George and Jiang, Lan and Wei, Wei-Qi and Feng, Qiping and Karjalainen, Juha and Havulinna, Aki and Kiiskinen, Tuomo and Bick, Alexander and Ardissino, Diego and Wilson, James G and Schunkert, Heribert and McPherson, Ruth and Watkins, Hugh and Elosua, Roberto and Bown, Matthew J and Samani, Nilesh J and Baber, Usman and Erdmann, Jeanette and Gupta, Namrata and Danesh, John and Saleheen, Danish and Chang, Kyong-Mi and Vujkovic, Marijana and Voight, Ben and Damrauer, Scott and Lynch, Julie and Kaplan, David and Serper, Marina and Tsao, Philip and Mercader, Josep and Hanis, Craig and Daly, Mark and Denny, Joshua and Gabriel, Stacey and Kathiresan, Sekar} } @article {372, title = {Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort.}, journal = {Nucleic Acids Res}, volume = {45}, year = {2017}, month = {2017 02 28}, pages = {1633-1648}, abstract = {

We developed an algorithm, HMZDelFinder, that uses whole exome sequencing (WES) data to identify rare and intragenic homozygous and hemizygous (HMZ) deletions that may represent complete loss-of-function of the indicated gene. HMZDelFinder was applied to 4866 samples in the Baylor-Hopkins Center for Mendelian Genomics (BHCMG) cohort and detected 773 HMZ deletion calls (567 homozygous or 206 hemizygous) with an estimated sensitivity of 86.5\% (82\% for single-exonic and 88\% for multi-exonic calls) and precision of 78\% (53\% single-exonic and 96\% for multi-exonic calls). Out of 773 HMZDelFinder-detected deletion calls, 82 were subjected to array comparative genomic hybridization (aCGH) and/or breakpoint PCR and 64 were confirmed. These include 18 single-exon deletions out of which 8 were exclusively detected by HMZDelFinder and not by any of seven other CNV detection tools examined. Further investigation of the 64 validated deletion calls revealed at least 15 pathogenic HMZ deletions. Of those, 7 accounted for 17-50\% of pathogenic CNVs in different disease cohorts where 7.1-11\% of the molecular diagnosis solved rate was attributed to CNVs. In summary, we present an algorithm to detect rare, intragenic, single-exon deletion CNVs using WES data; this tool can be useful for disease gene discovery efforts and clinical WES analyses.

}, keywords = {Algorithms, Alternative Splicing, Cohort Studies, Computational Biology, Consanguinity, Datasets as Topic, DNA Copy Number Variations, Exome, Genetic Diseases, Inborn, Hemizygote, High-Throughput Nucleotide Sequencing, Homozygote, Humans, Inheritance Patterns, Models, Genetic, Pedigree, Reproducibility of Results, Sequence Deletion, Workflow}, issn = {1362-4962}, doi = {10.1093/nar/gkw1237}, author = {Gambin, Tomasz and Akdemir, Zeynep C and Yuan, Bo and Gu, Shen and Chiang, Theodore and Carvalho, Claudia M B and Shaw, Chad and Jhangiani, Shalini and Boone, Philip M and Eldomery, Mohammad K and Karaca, Ender and Bayram, Yavuz and Stray-Pedersen, Asbj{\o}rg and Muzny, Donna and Charng, Wu-Lin and Bahrambeigi, Vahid and Belmont, John W and Boerwinkle, Eric and Beaudet, Arthur L and Gibbs, Richard A and Lupski, James R} } @article {415, title = {Increasing Generality and Power of Rare-Variant Tests by Utilizing Extended Pedigrees.}, journal = {Am J Hum Genet}, volume = {99}, year = {2016}, month = {2016 Oct 06}, pages = {846-859}, abstract = {

Recently, multiple studies have performed whole-exome or whole-genome sequencing to identify groups of rare variants associated with complex traits and diseases. They have primarily utilized case-control study designs that often require thousands of individuals to reach acceptable statistical power. Family-based studies can be more powerful because a rare variant can be enriched in an extended pedigree and segregate with the phenotype. Although many methods have been proposed for using family data to discover rare variants involved in a disease, a majority of them focus on a specific pedigree structure and are designed to analyze either binary or continuously measured outcomes. In this article, we propose RareIBD, a general and powerful approach to identifying rare variants involved in disease susceptibility. Our method can be applied to large extended families of arbitrary structure, including pedigrees with only affected individuals. The method accommodates both binary and quantitative traits. A series of simulation experiments suggest that RareIBD is a powerful test that outperforms existing approaches. In addition, our method accounts for individuals in top generations, which are not usually genotyped in extended families. In contrast to available statistical tests, RareIBD generates accurate p values even when genetic data from these individuals are missing. We applied RareIBD, as well as other methods, to two extended family datasets generated by different genotyping technologies and representing different ethnicities. The analysis of real data confirmed that RareIBD is the only method that properly controls type I error.

}, keywords = {Datasets as Topic, Ethnic Groups, Family, Female, Genetic Predisposition to Disease, Genetic Variation, Genotype, Humans, Male, Models, Genetic, Pedigree, Phenotype, Research Design}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.08.015}, author = {Sul, Jae Hoon and Cade, Brian E and Cho, Michael H and Qiao, Dandi and Silverman, Edwin K and Redline, Susan and Sunyaev, Shamil} }