Causal SNPs Research Articles

Pathway Analysis of Genome Wide Association Studies (GWAS) Data Associated with Male Infertility

Background: Infertility is a common condition affecting approximately 10–20% of the reproductive age population. Idiopathic infertility cases are thought to have a genetic basis, but the underlying causes are largely unknown. However, the genetic basis underlying male infertility in humans is only partially understood. The Purpose of the study is to understand the current state of research on the genetics of male infertility and its association with significant biological mechanisms. Results: We performed an Identify Candidate Causal SNPs and Pathway (ICSN Pathway) analysis using a genome-wide association study (GWAS) dataset, and NCBI-PubMed search which included 632 SNPs in GWAS and 451 SNPs from the PubMed server, respectively. The ICSN Pathway analysis produced three hypothetical biological mechanisms associated with male infertility: (1) rs8084 and rs7192→HLA-DRA→inflammatory pathways and cell adhesion; rs7550231 and rs2234167→TNFRSF14→TNF Receptor Superfamily Member 14→T lymphocyte proliferation and activation; rs1105879 and rs2070959→UGT1A6→UDP glucuronosyltransferase family 1 member A6→Metabolism of Xenobiotics, androgen, estrogen, retinol, and carbohydrates. Conclusions: We believe that our results may be helpful to study the genetic mechanisms of male infertility. Pathway-based methods have been applied to male infertility GWAS datasets to investigate the biological mechanisms and reported some novel male infertility risk pathways. This pathway analysis using GWAS dataset suggests that the biological process related to inflammation and metabolism might contribute to male infertility susceptibility. Our analysis suggests that genetic contribution to male infertility operates through multiple genes affecting common inflammatory diseases interacting in functional pathways.

Genomic data analysis using a two stage expectation propagation algorithm for analysis of sparse Bayesian high-dimensional instrumental variables regression

Simultaneous analysis of gene expression data and genetic variants is highly of interest, especially when the number of gene expressions and genetic variants are both greater than the sample size. Association of both causal genes and effective SNPs makes the use of sparse modeling of such genetic data sets, highly important. The high-dimensional sparse instrumental variables models are one of such useful association models, which models the simultaneous relation of the gene expressions and genetic variants with complex traits. From a Bayesian viewpoint, the sparsity can be favored using sparsity-enforcing priors such as spike-and-slab priors. A two-stage modification of the expectation propagation (EP) algorithm is proposed and examined for approximate inference in high-dimensional sparse instrumental variables models with spike-and-slab priors. This method is an adoption of the classical two-stage least squares method, to be used with the Bayes context. A simulation study is performed to examine the performance of the methods. The proposed method is applied to analysis of the mouse obesity data.

Linear Mixed-Effect Models Through the Lens of Hardy-Weinberg Disequilibrium.

For genetic association studies with related individuals, the linear mixed-effect model is the most commonly used method. In this report, we show that contrary to the popular belief, this standard method can be sensitive to departure from Hardy–Weinberg equilibrium (i.e., Hardy–Weinberg disequilibrium) at the causal SNPs in two ways. First, when the trait heritability is treated as a nuisance parameter, although the association test has correct type I error control, the resulting heritability estimate can be biased, often upward, in the presence of Hardy–Weinberg disequilibrium. Second, if the true heritability is used in the linear mixed-effect model, then the corresponding association test can be biased in the presence of Hardy–Weinberg disequilibrium. We provide some analytical insights along with supporting empirical results from simulation and application studies.

Defining novel causal SNPs and linked phenotypes at melanoma-associated loci.

A number of genomic regions have been associated with melanoma risk through genome-wide association studies; however, the causal variants underlying the majority of these associations remain unknown. Here, we sequenced either the full locus or the functional regions including exons of 19 melanoma-associated loci in 1959 British melanoma cases and 737 controls. Variant filtering followed by Fisher’s exact test analyses identified 66 variants associated with melanoma risk. Sequential conditional logistic regression identified the distinct haplotypes on which variants reside, and massively parallel reporter assays provided biological insights into how these variants influence gene function. We performed further analyses to link variants to melanoma risk phenotypes and assessed their association with melanoma-specific survival. Our analyses replicate previously known associations in the melanocortin 1 receptor (MC1R) and tyrosinase (TYR) loci, while identifying novel potentially causal variants at the MTAP/CDKN2A and CASP8 loci. These results improve our understanding of the architecture of melanoma risk and outcome.

Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer

The high-grade serous ovarian cancer (HGSOC) risk locus at chromosome 1p34.3 resides within a frequently amplified genomic region signifying the presence of an oncogene. Here, we integrate in silico variant-to-function analysis with functional studies to characterize the oncogenic potential of candidate genes in the 1p34.3 locus. Fine mapping of genome-wide association statistics identified candidate causal SNPs local to H3K27ac-demarcated enhancer regions that exhibit allele-specific binding for CTCF in HGSOC and normal fallopian tube secretory epithelium cells (FTSECs). SNP risk associations colocalized with eQTL for six genes (DNALI1, GNL2, RSPO1, SNIP1, MEAF6, and LINC01137) that are more highly expressed in carriers of the risk allele, and three (DNALI1, GNL2, and RSPO1) were upregulated in HGSOC compared to normal ovarian surface epithelium cells and/or FTSECs. Increased expression of GNL2 and MEAF6 was associated with shorter survival in HGSOC with 1p34.3 amplifications. Despite its activation of β-catenin signaling, RSPO1 overexpression exerted no effects on proliferation or colony formation in our study of ovarian cancer and FTSECs. Instead, GNL2, MEAF6, and SNIP1 silencing impaired in vitro ovarian cancer cell growth. Additionally, GNL2 silencing diminished xenograft tumor formation, whereas overexpression stimulated proliferation and colony formation in FTSECs. GNL2 influences 60S ribosomal subunit maturation and global protein synthesis in ovarian cancer and FTSECs, providing a potential mechanism of how GNL2 upregulation might promote ovarian cancer development and mediate genetic susceptibility of HGSOC.

Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification.

While the cohort level accuracy of polygenic risk score has been widely assessed, uncertainty in PRS—estimates of genetic value at the individual level remains underexplored. Here we show that Bayesian PRS methods can estimate the variance of an individual’s PRS and can yield well-calibrated credible intervals with posterior sampling. For real traits in the UK Biobank (N=291,273 unrelated “white British”) we observe large variance in individual PRS estimates which impacts interpretation of PRS-based stratification; averaging across 13 traits, only 0.8% (s.d. 1.6%) of individuals with PRS point estimates in the top decile have their entire 95% credible intervals fully contained in the top decile. We provide an analytical estimator for expected individual PRS variance—a function of SNP-heritability, number of causal SNPs, and sample size. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.

Convergent Evidence Supports TH2LCRR as a Novel Asthma Susceptibility Gene.

Asthma is a common, complex disease with apparent genetic predispositions, and previous genome-wide association studies suggest that rs1295686 within the IL13 (IL-13) gene is significantly associated with asthma. Analysis of the data provided by the 1,000 Genomes Project indicated an additional four SNPs in nearly complete linkage disequilibrium with rs1295686 in White people. However, the causal SNPs and the associated mechanism remain unclear. To investigate this issue, functional genomics approaches were utilized to analyze the functions of these SNPs. Dual-luciferase assays indicated that the functional SNP is not rs1295686 but a haplotype consisting of three other SNPs: rs1295685, rs848, and rs847. Through chromosome conformation capture, it was found that the enhancer containing the three functional SNPs interacts with the promoter of TH2LCRR (T helper type 2 locus control region associated RNA), a recently identified long noncoding RNA. RNA-seq data analysis indicated that TH2LCRR expression is significantly increased in patients with asthma and is dependent on the genotype at this locus, indicating that TH2LCRR is a novel susceptibility gene for asthma and that these SNPs confer asthma risk by regulating TH2LCRR expression. By chromatin immunoprecipitation, the related transcription factors that bind in the region surrounding these three SNPs were identified, and their interactions were investigated by functional genomics approaches. Our effort identified a novel mechanism through which genetic variations at this locus could influence asthma susceptibility.

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits.

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.

Abstract 10314: Post-GWAS Functional Genomics Analysis of CBLN2 Locus to Define the Pathogenic Role of CUX1 in Pulmonary Arterial Hypertension

Introduction: Pulmonary arterial hypertension (PAH) is a rare but morbid disease and highly relevant to genetic susceptibility. Genome-wide Association Studies (GWAS) have identified 12 SNPs on 9 PAH- associated loci. However, GWAS is unable to identify the causative functional SNPs (fSNP) or define the mechanisms by which a fSNP regulates the risk gene expression. Methods: We developed a novel functional genomics analysis to identify PAH-associated fSNPs. It includes Reel-seq (Regulatory element-sequencing), an EMSA-based high-throughput technique, to identify fSNPs in a synthetic DNA library containing PAH-associated SNPs; SDCP-MS (SNP-specific DNA competition pulldown-mass spectrometry) to identify proteins that specifically bind to fSNPs; and AIDP-Wb (allele-imbalanced DNA pulldown-Western blot) to show allele-imbalanced binding of these proteins to fSNPs. The regulation of risk gene expression by these fSNP-binding proteins was determined in PAECs and confirmed in PAH patients. Results: By Reel-seq, rs11151770 is identified a fSNP in the CBLN2 locus, and its risk allele A is associated with a 1.97-fold increase in PAH susceptibility. By using SDCP-MS and AIDP-Wb, transcription factor CUX1 was found to specifically bind to this fSNP, with higher affinity to the risk allele A than non-risk allele G, thus controlling the expression of CBLN2. CUX1 knockdown prevented, but CUX1 overexpression induced, the pathophenotypes in PAECs that are associated with PAH. CUX1-CBLN2 was upregulated by the acquired PAH trigger IL-1β, and was also increased in PAH lungs. Genotyping of this fSNP showed a 2.1-fold risk allele A enrichment in PAH patients associated with connective tissue disorders, supporting a pathogenic mechanism of an inflammation-sensitive pathway (CUX1-CBLN2) combined with a PAH susceptible genotype (risk allele A) for the manifestation of clinical PAH. Conclusion: We demonstrate that CUX1 regulates the expression of target gene CBLN2 via binding to fSNP rs11151770. Alteration of CBLN2 expression, in turn, controls crucial endothelial function relevant to PAH. These results identify a novel role of CUX1 in PAH, clarifying the pathogenic function of this PAH-associated locus and providing a potential target for PAH intervention.

An Overview of Strategies for Detecting Genotype-Phenotype Associations Across Ancestrally Diverse Populations.

Genome-wide association studies (GWAS) have been very successful at identifying genetic variants influencing a large number of traits. Although the great majority of these studies have been performed in European-descent individuals, it has been recognised that including populations with differing ancestries enhances the potential for identifying causal SNPs due to their differing patterns of linkage disequilibrium. However, when individuals from distinct ethnicities are included in a GWAS, it is necessary to implement a number of control steps to ensure that the identified associations are real genotype-phenotype relationships. In this Review, we discuss the analyses that are required when performing multi-ethnic studies, including methods for determining ancestry at the global and local level for sample exclusion, controlling for ancestry in association testing, and post-GWAS interrogation methods such as genomic control and meta-analysis. We hope that this overview provides a primer for those researchers interested in including distinct populations in their studies.

Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits.

The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs.

A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation.

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA’s superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/.

GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes.

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case-control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case-control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype.

Fine Mapping without Phenotyping: Identification of Selection Targets in Secondary Evolve and Resequence Experiments.

Evolve and Resequence (E&R) studies investigate the genomic selection response of populations in an Experimental Evolution setup. Despite the popularity of E&R, empirical studies in sexually reproducing organisms typically suffer from an excess of candidate loci due to linkage disequilibrium, and single gene or SNP resolution is the exception rather than the rule. Recently, so-called “secondary E&R” has been suggested as promising experimental follow-up procedure to confirm putatively selected regions from a primary E&R study. Secondary E&R provides also the opportunity to increase mapping resolution by allowing for additional recombination events, which separate the selection target from neutral hitchhikers. Here, we use computer simulations to assess the effect of different crossing schemes, population size, experimental duration, and number of replicates on the power and resolution of secondary E&R. We find that the crossing scheme and population size are crucial factors determining power and resolution of secondary E&R: A simple crossing scheme with few founder lines consistently outcompetes crossing schemes where evolved populations from a primary E&R experiment are mixed with a complex ancestral founder population. Regardless of the experimental design tested, a population size of at least 4,800 individuals, which is roughly five times larger than population sizes in typical E&R studies, is required to achieve a power of at least 75%. Our study provides an important step toward improved experimental designs aiming to characterize causative SNPs in Experimental Evolution studies.

Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.

Analysis of Skin Pigmentation and Genetic Ancestry in Three Subpopulations from Pakistan: Punjabi, Pashtun, and Baloch.

Skin pigmentation is one of the most prominent and variable phenotypes in humans. We compared the alleles of 163 SNPs and indels from the Human Pigmentation (HuPi) AmpliSeq™ Custom panel, and biogeographic ancestry with the quantitative skin pigmentation levels on the upper arm, lower arm, and forehead of 299 Pakistani individuals from three subpopulations: Baloch, Pashtun, and Punjabi. The biogeographic ancestry of each individual was estimated using the Precision ID Ancestry Panel. All individuals were mainly of mixed South-Central Asian and European ancestry. However, the Baloch individuals also had an average proportion of Sub-Saharan African ancestry of approximately 10%, whereas it was <1% in the Punjabi and Pashtun individuals. The pairwise genetic distances between the Pashtun, Punjabi, and Baloch subpopulations based on the ancestry markers were statistically significantly different. Individuals from the Pashtun subpopulation had statistically significantly lower skin pigmentation than individuals from the Punjabi and Baloch subpopulations (p < 0.05). The proportions of European and Sub-Saharan African ancestry and five SNPs (rs1042602, rs10831496, rs1426654, rs16891982, and rs12913832) were statistically significantly associated with skin pigmentation at either the upper arm, lower arm or forehead in the Pakistani population after correction for multiple testing (p < 10−3). A model based on four of these SNPs (rs1426654, rs1042602, rs16891982, and rs12913832) explained 33% of the upper arm skin pigmentation. The four SNPs and the proportions of European and Sub-Saharan African ancestry explained 37% of the upper arm skin pigmentation. Our results indicate that the four likely causative SNPs, rs1426654, rs1042602, rs16891982, and rs12913832 located in SLC24A5, TYR, SLC45A2, and HERC2, respectively, are essential for skin color variation in the admixed Pakistani subpopulations.

Broadening our understanding of genetic risk for scleroderma/systemic sclerosis by querying the chromatin architecture surrounding the risk haplotypes

BackgroundGenetic variants in the human leukocyte antigen (HLA) locus contribute to the risk for developing scleroderma/systemic sclerosis (SSc). However, there are other replicated loci that also contribute to genetic risk for SSc, and it is unknown whether genetic risk in these non-HLA loci acts primarily on the vasculature, immune system, fibroblasts, or other relevant cell types. We used the Cistrome database to investigate the epigenetic landscapes surrounding 11 replicated SSc associated loci to determine whether SNPs in these loci may affect regulatory elements and whether they are likely to impact a specific cell type.MethodsWe mapped 11 replicated SNPs to haplotypes and sought to determine whether there was significant enrichment for H3K27ac and H3K4me1 marks, epigenetic signatures of enhancer function, on these haplotypes. We queried pathologically relevant cell types: B cells, endothelial cells, fibroblasts, monocytes, and T cells. We then identified the topologically associated domains (TADs) that encompass the SSc risk haplotypes in primary T cells to identify the full range of genes that may be influenced by SSc causal SNPs. We used gene ontology analyses of the genes within the TADs to gain insight into immunologic functions that might be affected by SSc causal SNPs.ResultsThe SSc-associated haplotypes were enriched (p value < 0.01) for H3K4me1/H3K27ac marks in monocytes. Enrichment of one of the two histone marks was found in B cells, fibroblasts, and T cells. No enrichment was identified in endothelial cells. Ontological analyses of genes within the TADs encompassing the risk haplotypes showed enrichment for regulation of transcription, protein binding, activation of T lymphocytes, and proliferation of immune cells.ConclusionsThe 11 non-HLA SSc risk haplotypes queried are highly enriched for H3K4me1/H3K27ac-marked regulatory elements in a broad range of immune cells and fibroblasts. Furthermore, in immune cells, the risk haplotypes belong to larger chromatin structures encompassing genes that regulate a wide array of immune processes associated with SSc pathogenesis. Though importance of the vasculature in the pathobiology of SSc is widely accepted, we were unable to find evidence for genetic influences on endothelial cell function in these regions.

Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies

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 (|rg|=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× 10-7). 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× 10-6 and 1.7× 10-4, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5× 10-5).

Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.

Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction. By incorporating the Hi-C data, we propose a novel and powerful deep learning model of artificial intelligence to classify interacting and non-interacting fragment pairs and predict the functional effects of sequence alteration of single nucleotide on chromatin interaction and thus on gene expression. The changes in chromatin interaction probability between the reference sequence and the altered sequence reflect the degree of functional impact for the variant. The model was effective and efficient with the classification of interacting and non-interacting fragment pairs. The predicted causal SNPs that had a larger impact on chromatin interaction were more likely to be identified by GWAS and eQTL analyses. We demonstrate that an integrative approach combining artificial intelligence-deep learning with high throughput experimental evidence of chromatin interaction leads to prioritizing the functional variants in disease- and phenotype-related loci and thus will greatly expedite uncover of the biological mechanism underlying the association identified in genomic studies. Source code used in data preparing and model training is available at the GitHub website (https://github.com/biocai/DeepHiC). Supplementary data are available at Bioinformatics online.