@article {120, title = {Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 11}, pages = {1145-1150}, abstract = {

The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet-Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes-a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.

}, keywords = {Alleles, Bardet-Biedl Syndrome, Cohort Studies, Exome, Genetic Variation, Humans}, issn = {1546-1718}, doi = {10.1038/s41588-020-0707-1}, author = {Kousi, Maria and S{\"o}ylemez, Onuralp and Ozanturk, Ayseg{\"u}l and Mourtzi, Niki and Akle, Sebastian and Jungreis, Irwin and Muller, Jean and Cassa, Christopher A and Brand, Harrison and Mokry, Jill Anne and Wolf, Maxim Y and Sadeghpour, Azita and McFadden, Kelsey and Lewis, Richard A and Talkowski, Michael E and Dollfus, H{\'e}l{\`e}ne and Kellis, Manolis and Davis, Erica E and Sunyaev, Shamil R and Katsanis, Nicholas} } @article {124, title = {Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis".}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 10}, pages = {e1009165}, abstract = {

BACKGROUND: The majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.

RESULTS: We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.

CONCLUSIONS: Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.

}, keywords = {Alleles, Animals, Epistasis, Genetic, Evolution, Molecular, Genotype, Humans, Mice, Models, Genetic, Multifactorial Inheritance, Phenotype, Quantitative Trait Loci, Saccharomyces cerevisiae, Selection, Genetic}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009165}, author = {Rau, Christoph D and Gonzales, Natalia M and Bloom, Joshua S and Park, Danny and Ayroles, Julien and Palmer, Abraham A and Lusis, Aldons J and Zaitlen, Noah} } @article {128, title = {A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 11}, pages = {e1008802}, abstract = {

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

}, keywords = {Alagille Syndrome, Alleles, Biological Variation, Population, DiGeorge Syndrome, European Continental Ancestry Group, Female, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Humans, Male, Marfan Syndrome, Noonan Syndrome, Phenotype, Polymorphism, Single Nucleotide, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008802}, author = {Tcheandjieu, Catherine and Aguirre, Matthew and Gustafsson, Stefan and Saha, Priyanka and Potiny, Praneetha and Haendel, Melissa and Ingelsson, Erik and Rivas, Manuel A and Priest, James R} } @article {48, title = {Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Feb 15}, pages = {790}, abstract = {

Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)], where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1\%) explain less than 10\% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.

}, keywords = {Algorithms, Alleles, Biological Specimen Banks, Gene Frequency, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Selection, Genetic, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-019-08424-6}, author = {Schoech, Armin P and Jordan, Daniel M and Loh, Po-Ru and Gazal, Steven and O{\textquoteright}Connor, Luke J and Balick, Daniel J and Palamara, Pier F and Finucane, Hilary K and Sunyaev, Shamil R and Price, Alkes L} } @article {44, title = {Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Oct}, pages = {1366-1374}, abstract = {

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}, issn = {1546-1718}, doi = {10.1038/s41588-018-0216-7}, author = {Westra, Harm-Jan and Mart{\'\i}nez-Bonet, Marta and Onengut-Gumuscu, Suna and Lee, Annette and Luo, Yang and Teslovich, Nikola and Worthington, Jane and Martin, Javier and Huizinga, Tom and Klareskog, Lars and Rantapaa-Dahlqvist, Solbritt and Chen, Wei-Min and Quinlan, Aaron and Todd, John A and Eyre, Steve and Nigrovic, Peter A and Gregersen, Peter K and Rich, Stephen S and Raychaudhuri, Soumya} } @article {40, title = {Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Nov}, pages = {1600-1607}, abstract = {

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5\%<= minor allele frequency <5\%) and common (minor allele frequency >=5\%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 {\textpm} 1\% of low-frequency variant heritability ([Formula: see text]) versus 2.1 {\textpm} 0.2\% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 {\textpm} 12\% of [Formula: see text] versus 12 {\textpm} 2\% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5\%).

}, keywords = {Alleles, Biological Specimen Banks, Gene Frequency, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Molecular Sequence Annotation, Open Reading Frames, Polymorphism, Single Nucleotide, Selection, Genetic, United Kingdom, White People}, issn = {1546-1718}, doi = {10.1038/s41588-018-0231-8}, author = {Gazal, Steven and Loh, Po-Ru and Finucane, Hilary K and Ganna, Andrea and Schoech, Armin and Sunyaev, Shamil and Price, Alkes L} } @article {7, title = {The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium.}, journal = {Genetics}, volume = {205}, year = {2017}, month = {2017 Jan}, pages = {375-383}, abstract = {

Statistical models in medical and population genetics typically assume that individuals assort randomly in a population. While this simplifies model complexity, it contradicts an increasing body of evidence of nonrandom mating in human populations. Specifically, it has been shown that assortative mating is significantly affected by genomic ancestry. In this work, we examine the effects of ancestry-assortative mating on the linkage disequilibrium between local ancestry tracks of individuals in an admixed population. To accomplish this, we develop an extension to the Wright-Fisher model that allows for ancestry-based assortative mating. We show that ancestry-assortment perturbs the distribution of local ancestry linkage disequilibrium (LAD) and the variance of ancestry in a population as a function of the number of generations since admixture. This assortment effect can induce errors in demographic inference of admixed populations when methods assume random mating. We derive closed form formulae for LAD under an assortative-mating model with and without migration. We observe that LAD depends on the correlation of global ancestry of couples in each generation, the migration rate of each of the ancestral populations, the initial proportions of ancestral populations, and the number of generations since admixture. We also present the first direct evidence of ancestry-assortment in African Americans and examine LAD in simulated and real admixed population data of African Americans. We find that demographic inference under the assumption of random mating significantly underestimates the number of generations since admixture, and that accounting for assortative mating using the patterns of LAD results in estimates that more closely agrees with the historical narrative.

}, keywords = {Alleles, Black or African American, Datasets as Topic, Gene Frequency, Genetics, Population, Genomics, Human Migration, Humans, Linkage Disequilibrium, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide}, issn = {1943-2631}, doi = {10.1534/genetics.116.192138}, author = {Zaitlen, Noah and Huntsman, Scott and Hu, Donglei and Spear, Melissa and Eng, Celeste and Oh, Sam S and White, Marquitta J and Mak, Angel and Davis, Adam and Meade, Kelly and Brigino-Buenaventura, Emerita and LeNoir, Michael A and Bibbins-Domingo, Kirsten and Burchard, Esteban G and Halperin, Eran} } @article {18, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @article {38, title = {Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Oct}, pages = {1421-1427}, abstract = {

Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 {\texttimes} 10); the youngest 20\% of common SNPs explain 3.9 times more heritability than the oldest 20\%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

}, keywords = {Alleles, Chi-Square Distribution, Datasets as Topic, Genetic Fitness, Genetic Variation, Humans, Linkage Disequilibrium, Models, Genetic, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Selection, Genetic}, issn = {1546-1718}, doi = {10.1038/ng.3954}, author = {Gazal, Steven and Finucane, Hilary K and Furlotte, Nicholas A and Loh, Po-Ru and Palamara, Pier Francesco and Liu, Xuanyao and Schoech, Armin and Bulik-Sullivan, Brendan and Neale, Benjamin M and Gusev, Alexander and Price, Alkes L} } @article {13, title = {Negative selection in humans and fruit flies involves synergistic epistasis.}, journal = {Science}, volume = {356}, year = {2017}, month = {2017 May 05}, pages = {539-542}, abstract = {

Negative selection against deleterious alleles produced by mutation influences within-population variation as the most pervasive form of natural selection. However, it is not known whether deleterious alleles affect fitness independently, so that cumulative fitness loss depends exponentially on the number of deleterious alleles, or synergistically, so that each additional deleterious allele results in a larger decrease in relative fitness. Negative selection with synergistic epistasis should produce negative linkage disequilibrium between deleterious alleles and, therefore, an underdispersed distribution of the number of deleterious alleles in the genome. Indeed, we detected underdispersion of the number of rare loss-of-function alleles in eight independent data sets from human and fly populations. Thus, selection against rare protein-disrupting alleles is characterized by synergistic epistasis, which may explain how human and fly populations persist despite high genomic mutation rates.

}, keywords = {Alleles, Animals, Drosophila melanogaster, Epistasis, Genetic, Genetic Fitness, Genome, Human, Genome, Insect, Humans, Linkage Disequilibrium, Mutation Rate, Mutation, Missense, Selection, Genetic}, issn = {1095-9203}, doi = {10.1126/science.aah5238}, author = {Sohail, Mashaal and Vakhrusheva, Olga A and Sul, Jae Hoon and Pulit, Sara L and Francioli, Laurent C and van den Berg, Leonard H and Veldink, Jan H and de Bakker, Paul I W and Bazykin, Georgii A and Kondrashov, Alexey S and Sunyaev, Shamil R} }