@article {839, title = {Mapping and characterization of structural variation in 17,795 human genomes.}, journal = {Nature}, volume = {583}, year = {2020}, month = {2020 07}, pages = {83-89}, abstract = {

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2\% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2\% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90\% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2\% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

}, keywords = {Alleles, Case-Control Studies, Continental Population Groups, Epigenesis, Genetic, Female, Gene Dosage, Genetic Variation, Genetics, Population, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Male, Molecular Sequence Annotation, Quantitative Trait Loci, Software, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2371-0}, author = {Abel, Haley J and Larson, David E and Regier, Allison A and Chiang, Colby and Das, Indraniel and Kanchi, Krishna L and Layer, Ryan M and Neale, Benjamin M and Salerno, William J and Reeves, Catherine and Buyske, Steven and Matise, Tara C and Muzny, Donna M and Zody, Michael C and Lander, Eric S and Dutcher, Susan K and Stitziel, Nathan O and Hall, Ira M} } @article {784, title = {The mutational constraint spectrum quantified from variation in 141,456 humans.}, journal = {Nature}, volume = {581}, year = {2020}, month = {2020 05}, pages = {434-443}, abstract = {

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

}, keywords = {Adult, Brain, Cardiovascular Diseases, Cohort Studies, Databases, Genetic, Exome, Female, Genes, Essential, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Loss of Function Mutation, Male, Mutation Rate, Proprotein Convertase 9, Reproducibility of Results, RNA, Messenger, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2308-7}, author = {Karczewski, Konrad J and Francioli, Laurent C and Tiao, Grace and Cummings, Beryl B and Alf{\"o}ldi, Jessica and Wang, Qingbo and Collins, Ryan L and Laricchia, Kristen M and Ganna, Andrea and Birnbaum, Daniel P and Gauthier, Laura D and Brand, Harrison and Solomonson, Matthew and Watts, Nicholas A and Rhodes, Daniel and Singer-Berk, Moriel and England, Eleina M and Seaby, Eleanor G and Kosmicki, Jack A and Walters, Raymond K and Tashman, Katherine and Farjoun, Yossi and Banks, Eric and Poterba, Timothy and Wang, Arcturus and Seed, Cotton and Whiffin, Nicola and Chong, Jessica X and Samocha, Kaitlin E and Pierce-Hoffman, Emma and Zappala, Zachary and O{\textquoteright}Donnell-Luria, Anne H and Minikel, Eric Vallabh and Weisburd, Ben and Lek, Monkol and Ware, James S and Vittal, Christopher and Armean, Irina M and Bergelson, Louis and Cibulskis, Kristian and Connolly, Kristen M and Covarrubias, Miguel and Donnelly, Stacey and Ferriera, Steven and Gabriel, Stacey and Gentry, Jeff and Gupta, Namrata and Jeandet, Thibault and Kaplan, Diane and Llanwarne, Christopher and Munshi, Ruchi and Novod, Sam and Petrillo, Nikelle and Roazen, David and Ruano-Rubio, Valentin and Saltzman, Andrea and Schleicher, Molly and Soto, Jose and Tibbetts, Kathleen and Tolonen, Charlotte and Wade, Gordon and Talkowski, Michael E and Neale, Benjamin M and Daly, Mark J and MacArthur, Daniel G} } @article {875, title = {Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.}, journal = {Am J Hum Genet}, volume = {107}, year = {2020}, month = {2020 07 02}, pages = {46-59}, abstract = {

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait{\textquoteright}s heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.

}, keywords = {Aged, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.05.004}, author = {Chun, Sung and Imakaev, Maxim and Hui, Daniel and Patsopoulos, Nikolaos A and Neale, Benjamin M and Kathiresan, Sekar and Stitziel, Nathan O and Sunyaev, Shamil R} } @article {836, title = {A structural variation reference for medical and population genetics.}, journal = {Nature}, volume = {581}, year = {2020}, month = {2020 05}, pages = {444-451}, abstract = {

Structural variants (SVs) rearrange large segments of DNA and can have profound consequences in evolution and human disease. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) have become integral in the interpretation of single-nucleotide variants (SNVs). However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54\% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29\% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9\% of samples, and estimate that 0.13\% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings. This SV resource is freely distributed via the gnomAD browser and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

}, keywords = {Continental Population Groups, Disease, Female, Genetic Testing, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genotyping Techniques, Humans, Male, Middle Aged, Mutation, Polymorphism, Single Nucleotide, Reference Standards, Selection, Genetic, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2287-8}, author = {Collins, Ryan L and Brand, Harrison and Karczewski, Konrad J and Zhao, Xuefang and Alf{\"o}ldi, Jessica and Francioli, Laurent C and Khera, Amit V and Lowther, Chelsea and Gauthier, Laura D and Wang, Harold and Watts, Nicholas A and Solomonson, Matthew and O{\textquoteright}Donnell-Luria, Anne and Baumann, Alexander and Munshi, Ruchi and Walker, Mark and Whelan, Christopher W and Huang, Yongqing and Brookings, Ted and Sharpe, Ted and Stone, Matthew R and Valkanas, Elise and Fu, Jack and Tiao, Grace and Laricchia, Kristen M and Ruano-Rubio, Valentin and Stevens, Christine and Gupta, Namrata and Cusick, Caroline and Margolin, Lauren and Taylor, Kent D and Lin, Henry J and Rich, Stephen S and Post, Wendy S and Chen, Yii-Der Ida and Rotter, Jerome I and Nusbaum, Chad and Philippakis, Anthony and Lander, Eric and Gabriel, Stacey and Neale, Benjamin M and Kathiresan, Sekar and Daly, Mark J and Banks, Eric and MacArthur, Daniel G and Talkowski, Michael E} } @article {550, title = {De Novo Sequence and Copy Number Variants Are Strongly Associated with Tourette Disorder and Implicate Cell Polarity in Pathogenesis.}, journal = {Cell Rep}, volume = {24}, year = {2018}, month = {2018 Sep 25}, pages = {3441-3454.e12}, abstract = {

We previously established the contribution of de novo damaging sequence variants to Tourette disorder (TD) through whole-exome sequencing of 511 trios. Here, we sequence an additional 291 TD trios and analyze the combined set of 802 trios. We observe an overrepresentation of de novo damaging variants in simplex, but not multiplex, families; we identify a high-confidence TD risk gene, CELSR3 (cadherin EGF LAG seven-pass G-type receptor 3); we find that the genes mutated in TD patients are enriched for those related to cell polarity, suggesting a common pathway underlying pathobiology; and we confirm a statistically significant excess of de novo copy number variants in TD. Finally, we identify significant overlap of de novo sequence variants between TD and obsessive-compulsive disorder and de novo copy number variants between TD and autism spectrum disorder, consistent with shared genetic risk.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.08.082}, author = {Wang, Sheng and Mandell, Jeffrey D and Kumar, Yogesh and Sun, Nawei and Morris, Montana T and Arbelaez, Juan and Nasello, Cara and Dong, Shan and Duhn, Clif and Zhao, Xin and Yang, Zhiyu and Padmanabhuni, Shanmukha S and Yu, Dongmei and King, Robert A and Dietrich, Andrea and Khalifa, Najah and Dahl, Niklas and Huang, Alden Y and Neale, Benjamin M and Coppola, Giovanni and Mathews, Carol A and Scharf, Jeremiah M and Fernandez, Thomas V and Buxbaum, Joseph D and De Rubeis, Silvia and Grice, Dorothy E and Xing, Jinchuan and Heiman, Gary A and Tischfield, Jay A and Paschou, Peristera and Willsey, A Jeremy and State, Matthew W} }