Population sequencing data reveal a compendium of mutational processes in the human germ line. - Seplyarskiy, Vladimir B; Soldatov, Ruslan A; Koch, Evan; McGinty, Ryan J; Goldmann, Jakob M; Hernandez, Ryan D; Barnes, Kathleen; Correa, Adolfo; Burchard, Esteban G; Ellinor, Patrick T; McGarvey, Stephen T; Mitchell, Braxton D; et al. (Science, 2021)
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.
Keywords: Algorithms; CpG Islands; DNA Damage; DNA Demethylation; DNA Mutational Analysis; DNA Replication; Genetic Variation; Genome, Human; Germ Cells; Germ-Line Mutation; Humans; Long Interspersed Nucleotide Elements; Mutagenesis; Oocytes; Transcription, Genetic

Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts. - Raffield, Laura M; Iyengar, Apoorva K; Wang, Biqi; Gaynor, Sheila M; Spracklen, Cassandra N; Zhong, Xue; Kowalski, Madeline H; Salimi, Shabnam; Polfus, Linda M; Benjamin, Emelia J; Bis, Joshua C; Bowler, Russell; et al. (Am J Hum Genet, 2020)
Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.
Keywords: African Continental Ancestry Group; Asian Continental Ancestry Group; C-Reactive Protein; Cohort Studies; European Continental Ancestry Group; Gene Frequency; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Linkage Disequilibrium; Polymorphism, Single Nucleotide; Whole Genome Sequencing

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. - Li, Xihao; Li, Zilin; Zhou, Hufeng; Gaynor, Sheila M; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Aslibekyan, Stella; Ballantyne, Christie M; Bielak, Lawrence F; et al. (Nat Genet, 2020)
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.
Keywords: Cholesterol, LDL; Computer Simulation; Genetic Predisposition to Disease; Genetic Variation; Genome; Genome-Wide Association Study; Humans; Models, Genetic; Molecular Sequence Annotation; Phenotype; Whole Genome Sequencing

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies. - Chen, Han; Huffman, Jennifer E; Brody, Jennifer A; Wang, Chaolong; Lee, Seunggeun; Li, Zilin; Gogarten, Stephanie M; Sofer, Tamar; Bielak, Lawrence F; Bis, Joshua C; Blangero, John; Bowler, Russell P; et al. (Am J Hum Genet, 2019)
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.
Keywords: Chromosomes, Human, Pair 4; Cloud Computing; Female; Fibrinogen; Genetic Association Studies; Genetics, Population; Humans; Male; Models, Genetic; National Heart, Lung, and Blood Institute (U.S.); Precision Medicine; Research Design; Time Factors; United States; Whole Genome Sequencing

A common loss-of-function variant is associated with lower vitamin B concentration in African Americans. - Hu, Yao; Raffield, Laura M; Polfus, Linda M; Moscati, Arden; Nadkarni, Girish; Preuss, Michael H; Zhong, Xue; Wei, Qiang; Rich, Stephen S; Li, Yun; Wilson, James G; Correa, Adolfo; et al. (Blood, 2018)
Keywords: Black or African American; Female; Genome-Wide Association Study; Humans; Loss of Function Mutation; Male; Polymorphism, Single Nucleotide; Transcobalamins; Vitamin B 12; Vitamin B Deficiency

Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes. - Westra, Harm-Jan; Martínez-Bonet, Marta; Onengut-Gumuscu, Suna; Lee, Annette; Luo, Yang; Teslovich, Nikola; Worthington, Jane; Martin, Javier; Huizinga, Tom; Klareskog, Lars; Rantapaa-Dahlqvist, Solbritt; Chen, Wei-Min; et al. (Nat Genet, 2018)
To define potentially causal variants for autoimmune disease, we fine-mapped 76 rheumatoid arthritis (11,475 cases, 15,870 controls) and type 1 diabetes loci (9,334 cases, 11,111 controls). After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets of ≤5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.
Keywords: Alleles; Arthritis, Rheumatoid; Case-Control Studies; CD28 Antigens; Chromosome Mapping; CTLA-4 Antigen; Diabetes Mellitus, Type 1; Gene Frequency; Genetic Loci; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Jurkat Cells; Mutation; Polymorphism, Single Nucleotide; Quantitative Trait Loci; RNA, Long Noncoding; Tumor Necrosis Factor alpha-Induced Protein 3