%0 Journal Article %J Science %D 2021 %T Population sequencing data reveal a compendium of mutational processes in the human germ line. %A Seplyarskiy, Vladimir B %A Soldatov, Ruslan A %A Koch, Evan %A McGinty, Ryan J %A Goldmann, Jakob M %A Hernandez, Ryan D %A Barnes, Kathleen %A Correa, Adolfo %A Burchard, Esteban G %A Ellinor, Patrick T %A McGarvey, Stephen T %A Mitchell, Braxton D %A Vasan, Ramachandran S %A Redline, Susan %A Silverman, Edwin %A Weiss, Scott T %A Arnett, Donna K %A Blangero, John %A Boerwinkle, Eric %A He, Jiang %A Montgomery, Courtney %A Rao, D C %A Rotter, Jerome I %A Taylor, Kent D %A Brody, Jennifer A %A Chen, Yii-Der Ida %A de Las Fuentes, Lisa %A Hwu, Chii-Min %A Rich, Stephen S %A Manichaikul, Ani W %A Mychaleckyj, Josyf C %A Palmer, Nicholette D %A Smith, Jennifer A %A Kardia, Sharon L R %A Peyser, Patricia A %A Bielak, Lawrence F %A O'Connor, Timothy D %A Emery, Leslie S %A Gilissen, Christian %A Wong, Wendy S W %A Kharchenko, Peter V %A Sunyaev, Shamil %K Algorithms %K CpG Islands %K DNA Damage %K DNA Demethylation %K DNA Mutational Analysis %K DNA Replication %K Genetic Variation %K Genome, Human %K Germ Cells %K Germ-Line Mutation %K Humans %K Long Interspersed Nucleotide Elements %K Mutagenesis %K Oocytes %K Transcription, Genetic %X

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

%B Science %V 373 %P 1030-1035 %8 2021 08 27 %G eng %N 6558 %1 https://www.ncbi.nlm.nih.gov/pubmed/34385354?dopt=Abstract %R 10.1126/science.aba7408 %0 Journal Article %J Nat Genet %D 2020 %T Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. %A Li, Xihao %A Li, Zilin %A Zhou, Hufeng %A Gaynor, Sheila M %A Liu, Yaowu %A Chen, Han %A Sun, Ryan %A Dey, Rounak %A Arnett, Donna K %A Aslibekyan, Stella %A Ballantyne, Christie M %A Bielak, Lawrence F %A Blangero, John %A Boerwinkle, Eric %A Bowden, Donald W %A Broome, Jai G %A Conomos, Matthew P %A Correa, Adolfo %A Cupples, L Adrienne %A Curran, Joanne E %A Freedman, Barry I %A Guo, Xiuqing %A Hindy, George %A Irvin, Marguerite R %A Kardia, Sharon L R %A Kathiresan, Sekar %A Khan, Alyna T %A Kooperberg, Charles L %A Laurie, Cathy C %A Liu, X Shirley %A Mahaney, Michael C %A Manichaikul, Ani W %A Martin, Lisa W %A Mathias, Rasika A %A McGarvey, Stephen T %A Mitchell, Braxton D %A Montasser, May E %A Moore, Jill E %A Morrison, Alanna C %A O'Connell, Jeffrey R %A Palmer, Nicholette D %A Pampana, Akhil %A Peralta, Juan M %A Peyser, Patricia A %A Psaty, Bruce M %A Redline, Susan %A Rice, Kenneth M %A Rich, Stephen S %A Smith, Jennifer A %A Tiwari, Hemant K %A Tsai, Michael Y %A Vasan, Ramachandran S %A Wang, Fei Fei %A Weeks, Daniel E %A Weng, Zhiping %A Wilson, James G %A Yanek, Lisa R %A Neale, Benjamin M %A Sunyaev, Shamil R %A Abecasis, Gonçalo R %A Rotter, Jerome I %A Willer, Cristen J %A Peloso, Gina M %A Natarajan, Pradeep %A Lin, Xihong %K Cholesterol, LDL %K Computer Simulation %K Genetic Predisposition to Disease %K Genetic Variation %K Genome %K Genome-Wide Association Study %K Humans %K Models, Genetic %K Molecular Sequence Annotation %K Phenotype %K Whole Genome Sequencing %X

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

%B Nat Genet %V 52 %P 969-983 %8 2020 09 %G eng %N 9 %1 https://www.ncbi.nlm.nih.gov/pubmed/32839606?dopt=Abstract %R 10.1038/s41588-020-0676-4 %0 Journal Article %J Am J Hum Genet %D 2019 %T Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies. %A Chen, Han %A Huffman, Jennifer E %A Brody, Jennifer A %A Wang, Chaolong %A Lee, Seunggeun %A Li, Zilin %A Gogarten, Stephanie M %A Sofer, Tamar %A Bielak, Lawrence F %A Bis, Joshua C %A Blangero, John %A Bowler, Russell P %A Cade, Brian E %A Cho, Michael H %A Correa, Adolfo %A Curran, Joanne E %A de Vries, Paul S %A Glahn, David C %A Guo, Xiuqing %A Johnson, Andrew D %A Kardia, Sharon %A Kooperberg, Charles %A Lewis, Joshua P %A Liu, Xiaoming %A Mathias, Rasika A %A Mitchell, Braxton D %A O'Connell, Jeffrey R %A Peyser, Patricia A %A Post, Wendy S %A Reiner, Alex P %A Rich, Stephen S %A Rotter, Jerome I %A Silverman, Edwin K %A Smith, Jennifer A %A Vasan, Ramachandran S %A Wilson, James G %A Yanek, Lisa R %A Redline, Susan %A Smith, Nicholas L %A Boerwinkle, Eric %A Borecki, Ingrid B %A Cupples, L Adrienne %A Laurie, Cathy C %A Morrison, Alanna C %A Rice, Kenneth M %A Lin, Xihong %K Chromosomes, Human, Pair 4 %K Cloud Computing %K Female %K Fibrinogen %K Genetic Association Studies %K Genetics, Population %K Humans %K Male %K Models, Genetic %K National Heart, Lung, and Blood Institute (U.S.) %K Precision Medicine %K Research Design %K Time Factors %K United States %K Whole Genome Sequencing %X

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

%B Am J Hum Genet %V 104 %P 260-274 %8 2019 02 07 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/30639324?dopt=Abstract %R 10.1016/j.ajhg.2018.12.012