@article {132, title = {Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 02 04}, pages = {219-239}, abstract = {

We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation (|r|=80\%) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5~{\texttimes} 10). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9~{\texttimes} 10 and 1.7~{\texttimes} 10, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5~{\texttimes} 10).

}, keywords = {Computer Simulation, Diabetes Mellitus, Type 2, Evolution, Molecular, Gene-Environment Interaction, Genetic Heterogeneity, Genetic Pleiotropy, Genome, Human, Genome-Wide Association Study, Glycated Hemoglobin A, Humans, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Sample Size, Selection, Genetic}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.12.005}, author = {Stern, Aaron J and Speidel, Leo and Zaitlen, Noah A and Nielsen, Rasmus} } @article {145, title = {Population sequencing data reveal a compendium of mutational processes in the human germ line.}, journal = {Science}, volume = {373}, year = {2021}, month = {2021 08 27}, pages = {1030-1035}, abstract = {

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}, issn = {1095-9203}, doi = {10.1126/science.aba7408}, author = {Seplyarskiy, Vladimir B and Soldatov, Ruslan A and Koch, Evan and McGinty, Ryan J and Goldmann, Jakob M and Hernandez, Ryan D and Barnes, Kathleen and Correa, Adolfo and Burchard, Esteban G and Ellinor, Patrick T and McGarvey, Stephen T and Mitchell, Braxton D and Vasan, Ramachandran S and Redline, Susan and Silverman, Edwin and Weiss, Scott T and Arnett, Donna K and Blangero, John and Boerwinkle, Eric and He, Jiang and Montgomery, Courtney and Rao, D C and Rotter, Jerome I and Taylor, Kent D and Brody, Jennifer A and Chen, Yii-Der Ida and de Las Fuentes, Lisa and Hwu, Chii-Min and Rich, Stephen S and Manichaikul, Ani W and Mychaleckyj, Josyf C and Palmer, Nicholette D and Smith, Jennifer A and Kardia, Sharon L R and Peyser, Patricia A and Bielak, Lawrence F and O{\textquoteright}Connor, Timothy D and Emery, Leslie S and Gilissen, Christian and Wong, Wendy S W and Kharchenko, Peter V and Sunyaev, Shamil} } @article {133, title = {RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.}, journal = {PLoS Genet}, volume = {17}, year = {2021}, month = {2021 01}, pages = {e1009315}, abstract = {

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

}, keywords = {Biological Specimen Banks, Genome, Human, Genome-Wide Association Study, Genotyping Techniques, Haplotypes, Humans, Models, Genetic, Pedigree, Polymorphism, Single Nucleotide}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009315}, author = {Naseri, Ardalan and Shi, Junjie and Lin, Xihong and Zhang, Shaojie and Zhi, Degui} } @article {74, title = {Identifying causal variants and genes using functional genomics in specialized cell types and contexts.}, journal = {Hum Genet}, volume = {139}, year = {2020}, month = {2020 Jan}, pages = {95-102}, abstract = {

A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.

}, keywords = {Cell Lineage, Genes, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1432-1203}, doi = {10.1007/s00439-019-02044-2}, author = {Liu, Boxiang and Montgomery, Stephen B} } @article {94, title = {Transcriptomic signatures across human tissues identify functional rare genetic variation.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

}, keywords = {Genetic Variation, Genome, Human, Humans, Multifactorial Inheritance, Organ Specificity, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz5900}, author = {Ferraro, Nicole M and Strober, Benjamin J and Einson, Jonah and Abell, Nathan S and Aguet, Francois and Barbeira, Alvaro N and Brandt, Margot and Bucan, Maja and Castel, Stephane E and Davis, Joe R and Greenwald, Emily and Hess, Gaelen T and Hilliard, Austin T and Kember, Rachel L and Kotis, Bence and Park, YoSon and Peloso, Gina and Ramdas, Shweta and Scott, Alexandra J and Smail, Craig and Tsang, Emily K and Zekavat, Seyedeh M and Ziosi, Marcello and Ardlie, Kristin G and Assimes, Themistocles L and Bassik, Michael C and Brown, Christopher D and Correa, Adolfo and Hall, Ira and Im, Hae Kyung and Li, Xin and Natarajan, Pradeep and Lappalainen, Tuuli and Mohammadi, Pejman and Montgomery, Stephen B and Battle, Alexis} } @article {55, title = {Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.}, journal = {Curr Protoc Hum Genet}, volume = {101}, year = {2019}, month = {2019 04}, pages = {e83}, abstract = {

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. {\textcopyright} 2019 by John Wiley \& Sons, Inc.

}, keywords = {Algorithms, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genotype, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {1934-8258}, doi = {10.1002/cphg.83}, author = {Weissenkampen, J Dylan and Jiang, Yu and Eckert, Scott and Jiang, Bibo and Li, Bingshan and Liu, Dajiang J} } @article {51, title = {A multi-task convolutional deep neural network for variant calling in single molecule sequencing.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 03 01}, pages = {998}, abstract = {

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15\%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53\% F1-score on 1KP common variants, and 98.65, 92.57, 87.26\% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

}, keywords = {Base Sequence, Computational Biology, DNA Mutational Analysis, Genome, Human, Genome-Wide Association Study, Genomics, Genotype, Genotyping Techniques, Humans, INDEL Mutation, Nanopores, Neural Networks, Computer, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software}, issn = {2041-1723}, doi = {10.1038/s41467-019-09025-z}, author = {Luo, Ruibang and Sedlazeck, Fritz J and Lam, Tak-Wah and Schatz, Michael C} } @article {43, title = {Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 10 02}, pages = {4038}, abstract = {

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

}, keywords = {Genome, Human, Human Genetics, Humans, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-018-06159-4}, author = {Regier, Allison A and Farjoun, Yossi and Larson, David E and Krasheninina, Olga and Kang, Hyun Min and Howrigan, Daniel P and Chen, Bo-Juen and Kher, Manisha and Banks, Eric and Ames, Darren C and English, Adam C and Li, Heng and Xing, Jinchuan and Zhang, Yeting and Matise, Tara and Abecasis, Gon{\c c}alo R and Salerno, Will and Zody, Michael C and Neale, Benjamin M and Hall, Ira M} } @article {61, title = {Genetic diversity in populations across Latin America: implications for population and medical genetic studies.}, journal = {Curr Opin Genet Dev}, volume = {53}, year = {2018}, month = {2018 12}, pages = {98-104}, abstract = {

Hispanic/Latino (H/L) populations, although linked by culture and aspects of shared history, reflect the complexity of history and migration influencing the Americas. The original settlement by indigenous Americans, followed by postcolonial admixture from multiple continents, has yielded localized genetic patterns. In addition, numerous H/L populations appear to have signatures of pre-colonization and post-colonization bottlenecks, indicating that tens of millions of H/Ls may harbor signatures of founder effects today. Based on both population and medical genetic findings we highlight the extreme differentiation across the Americas, providing evidence for why H/Ls should not be considered a single population in modern human genetics. We highlight the need for additional sampling of understudied H/L groups, and ramifications of these findings for genomic medicine in one-tenth of the world{\textquoteright}s population.

}, keywords = {African Continental Ancestry Group, European Continental Ancestry Group, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genomics, Humans, Latin America}, issn = {1879-0380}, doi = {10.1016/j.gde.2018.07.006}, author = {Belbin, Gillian M and Nieves-Col{\'o}n, Maria A and Kenny, Eimear E and Moreno-Estrada, Andres and Gignoux, Christopher R} } @article {34, title = {A synthetic-diploid benchmark for accurate variant-calling evaluation.}, journal = {Nat Methods}, volume = {15}, year = {2018}, month = {2018 08}, pages = {595-597}, abstract = {

Existing benchmark datasets for use in evaluating variant-calling accuracy are constructed from a consensus of known short-variant callers, and they are thus biased toward easy regions that are accessible by these algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two fully homozygous human cell lines, which provides a relatively more accurate and less biased estimate of small-variant-calling error rates in a realistic context.

}, keywords = {Algorithms, Benchmarking, Cell Line, Tumor, Databases, Genetic, Diploidy, Female, Genetic Variation, Genome, Human, Homozygote, Humans, Hydatidiform Mole, Pregnancy, Synthetic Biology, Uterine Neoplasms, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-018-0054-7}, author = {Li, Heng and Bloom, Jonathan M and Farjoun, Yossi and Fleharty, Mark and Gauthier, Laura and Neale, Benjamin and MacArthur, Daniel} } @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 {30, title = {The impact of rare variation on gene expression across tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 10 11}, pages = {239-243}, abstract = {

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58\% of underexpression and 28\% of overexpression outliers have nearby conserved rare variants compared to 8\% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

}, keywords = {Bayes Theorem, Female, Gene Expression Profiling, Genetic Variation, Genome, Human, Genomics, Genotype, Humans, Male, Models, Genetic, Organ Specificity, Sequence Analysis, RNA}, issn = {1476-4687}, doi = {10.1038/nature24267}, author = {Li, Xin and Kim, Yungil and Tsang, Emily K and Davis, Joe R and Damani, Farhan N and Chiang, Colby and Hess, Gaelen T and Zappala, Zachary and Strober, Benjamin J and Scott, Alexandra J and Li, Amy and Ganna, Andrea and Bassik, Michael C and Merker, Jason D and Hall, Ira M and Battle, Alexis and Montgomery, Stephen B} } @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} } @article {25, title = {VEXOR: an integrative environment for prioritization of functional variants in fine-mapping analysis.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 May 01}, pages = {1389-1391}, abstract = {

MOTIVATION: The identification of the functional variants responsible for observed genome-wide association studies (GWAS) signals is one of the most challenging tasks of the post-GWAS research era. Several tools have been developed to annotate genetic variants by their genomic location and potential functional implications. Each of these tools has its own requirements and internal logic, which forces the user to become acquainted with each interface.

RESULTS: From an awareness of the amount of work needed to analyze a single locus, we have built a flexible, versatile and easy-to-use web interface designed to help in prioritizing variants and predicting their potential functional implications. This interface acts as a single-point of entry linking association results with reference tools and relevant experiments.

AVAILABILITY AND IMPLEMENTATION: VEXOR is an integrative web application implemented through the Shiny framework and available at: http://romix.genome.ulaval.ca/vexor.

CONTACT: arnaud.droit@crchuq.ulaval.ca.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Genome, Human, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Genetic, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw826}, author = {Lema{\c c}on, Audrey and Joly Beauparlant, Charles and Soucy, Penny and Allen, Jamie and Easton, Douglas and Kraft, Peter and Simard, Jacques and Droit, Arnaud} }