Many common diseases are highly polygenic, modulated by a large number genetic factors with small effects on susceptibility to disease. These small effects are difficult to map reliably in genetic association studies. To address this problem, researchers have developed methods that aggregate information over sets of related genes, such as biological pathways, to identify gene sets that are enriched for genetic variants associated with disease. However, these methods fail to answer a key question: which genes and genetic variants are associated with disease risk? We develop a method based on sparse multiple regression that simultaneously identifies enriched pathways, and prioritizes the variants within these pathways, to locate additional variants associated with disease susceptibility. A central feature of our approach is an estimate of the strength of enrichment, which yields a coherent way to prioritize variants in enriched pathways. We illustrate the benefits of our approach in a genome-wide association study of Crohn's disease with ~440,000 genetic variants genotyped for ~4700 study subjects. We obtain strong support for enrichment of IL-12, IL-23 and other cytokine signaling pathways. Furthermore, prioritizing variants in these enriched pathways yields support for additional disease-association variants, all of which have been independently reported in other case-control studies for Crohn's disease.
This paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external data sets collected from different populations or measuring related outcomes. The relevance of this methodology is explored in particular within the context of genome-wide association studies, where it can be helpful to address the pressing need for principled ways to suitably account for, and efficiently learn from the genetic variation associated to diverse ancestries. Finally, we apply these methods to analyze sev…
To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding a light on the genetic architecture of complex diseases. Polygenicity of complex diseases, which refers to the phenomenon that a vast number of risk variants collectively contribute to the heritability of complex diseases with modest individual effects, have been widely accepted. This imposes a major challenge towards fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larg…
Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample size n and the number of SNPs p can be very large. Recently, cross-trait polygenic risk score (PRS) method has gained extremely popular for assessing genetic correlation of complex traits based on GWAS summary statistics (e.g., SNP effect size). However, empirical evidence has shown a common bias phenomenon that even highly significant cross-trait PRS can only account for a very small amount of genetic variance (R^2 oft…
Incorporating LLM Embeddings for Variation Across the Human Genome
School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Division of Cancer Epidemiology and Genetics, National Cancer Institute（美国国家癌症研究所癌症流行病学与遗传学部门）；Department of Biostatistics, Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院生物统计学系）
Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in the non-coding region and their biological interpretation is still unclear. Second, accumulating evidence from GWAS suggests the polygenicity of complex traits, i.e., a complex trait is often affected by many variants with small or moderate effects, whereas a large prop…
Genome-wide association studies (GWAS) require accurate cohort phenotyping, but expert labeling can be costly, time-intensive, and variable. Here we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$) hits in 156 loci. The ML-based GWAS replicated 62 of 65…
Background: Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. Results: The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we bu…
Marginal association summary statistics have attracted great attention in statistical genetics, mainly because the primary results of most genome-wide association studies (GWAS) are produced by marginal screening. In this paper, we study the prediction accuracy of marginal estimator in dense (or sparsity free) high-dimensional settings with $(n,p,m) \to \infty$, $m/n \to γ\in (0,\infty)$, and $p/n \to ω\in (0,\infty)$. We consider a general correlation structure among the $p$ features and allow an unknown subset $m$ of them to be signals. As the marginal estimator can be viewed as a ridge esti…
We present an alternative method for genome-wide association studies (GWAS) that is more powerful than the regular GWAS method for locus detection. The regular GWAS method suffers from a substantial multiple-testing burden because of the millions of single nucleotide polymorphisms (SNPs) being tested simultaneously. Furthermore, it does not consider the functional genetic effect on the response variable; i.e., it ignores more complex joint effects of nearby SNPs within a region. Our proposed method screens the entire genome for associations using a sequential sliding-window approach based on w…
Rapid advances in high-throughput technologies have led to considerable interest in analyzing genome-scale data in the context of biological pathways, with the goal of identifying functional systems that are involved in a given phenotype. In the most common approaches, biological pathways are modeled as simple sets of genes, neglecting the network of interactions comprising the pathway and treating all genes as equally important to the pathway's function. Recently, a number of new methods have been proposed to integrate pathway topology in the analyses, harnessing existing knowledge and enabli…
A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integratin…
Genome-wide association studies have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases are unlikely to have a single causative gene. There is thus a pressing need for multi-SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi-SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). Th…
As an increasing number of genome-wide association studies reveal the limitations of attempting to explain phenotypic heritability by single genetic loci, there is growing interest for associating complex phenotypes with sets of genetic loci. While several methods for multi-locus mapping have been proposed, it is often unclear how to relate the detected loci to the growing knowledge about gene pathways and networks. The few methods that take biological pathways or networks into account are either restricted to investigating a limited number of predetermined sets of loci, or do not scale to gen…
High-dimensional phenotypes hold promise for richer findings in association studies, but testing of several phenotype traits aggravates the grand challenge of association studies, that of multiple testing. Several methods have recently been proposed for testing jointly all traits in a high-dimensional vector of phenotypes, with prospect of increased power to detect small effects that would be missed if tested individually. However, the methods have rarely been compared to the extent of enabling assessment of their relative merits and setting up guidelines on which method to use, and how to use…
Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in Children
To date, efforts to produce high-quality polygenic risk scores from genome-wide studies of common disease have focused on estimating and aggregating the effects of multiple SNPs. Here we propose a novel statistical approach for genetic risk prediction, based on random and mixed effects models. Our approach (termed GeRSI) circumvents the need to estimate the effect sizes of numerous SNPs by treating these effects as random, producing predictions which are consistently superior to current state of the art, as we demonstrate in extensive simulation. When applying GeRSI to seven phenotypes from th…
The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes. However, existing methods for multi-phenotype genome-wide association studies (GWAS) do not model pleiotropy, are only applicable to a small number of phenotypes, or provide no way to perform inference. To add further complication, raw genetic and phenotype data are rarely observed, meaning analyses must be performed on GWAS summary statistics whose statistical properties …
Interpreting artificial neural networks to detect genome-wide association signals for complex traits
University of Tartu（塔尔图大学）；Institute of Genomics（基因组学研究所）；Estonian Genome Centre（爱沙尼亚基因组中心）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；LISN（巴黎萨克雷大学国家科学研究实验室）；Paris-Saclay University（巴黎萨克雷大学）；Estonian Biobank（爱沙尼亚生物银行）
Mutational robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of mutational robustness in the budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs, and higher diversity of functions among…
Discovering all the genetic causes of a phenotype is an important goal in functional genomics. In this paper we combine an experimental design for multiple independent detections of the genetic causes of a phenotype, with a high-throughput sequencing analysis that maximizes sensitivity for comprehensively identifying them. Testing this approach on a set of 24 mutant strains generated for a metabolic phenotype with many known genetic causes, we show that this pathway-based phenotype sequencing analysis greatly improves sensitivity of detection compared with previous methods, and reveals a wide …
College of Software, Nankai University（南开大学软件学院）；College of Computer and Control Engineering, Nankai University（南开大学计算机与控制工程学院）
Background: Mining gene modules from genomic data is an important step to detect gene members of pathways or other relations such as protein-protein interactions. In this work, we explore the plausibility of detecting gene modules by factorizing gene-phenotype associations from a phenotype ontology rather than the conventionally used gene expression data. In particular, the hierarchical structure of ontology has not been sufficiently utilized in clustering genes while functionally related genes are consistently associated with phenotypes on the same path in the phenotype ontology. Results: We …
Genetic pathways usually encode molecular mechanisms that can inform targeted interventions. It is often challenging for existing machine learning approaches to jointly model genetic pathways (higher-order features) and variants (atomic features), and present to clinicians interpretable models. In order to build more accurate and better interpretable machine learning models for genetic medicine, we introduce Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning (PANTHER). PANTHER selects informative genetic pathways that directly encode molecular mechanisms. We a…
Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes. This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcription…
PanGeneHome is a web server dedicated to the analysis of available microbial pangenomes. For any prokaryotic taxon with at least three sequenced genomes, PanGeneHome provides (i) conservation level of genes, (ii) pangenome and core-genome curves, estimated pangenome size and other metrics, (iii) dendrograms based on gene content and average amino acid identity (AAI) for these genomes, and (iv) functional categories and metabolic pathways represented in the core, accessory and unique gene pools of the selected taxon. In addition, the results for these different analyses can be compared for any …
The Cancer Genome Atlas (TCGA) provides researchers with clinicopathological data and genomic characterizations of various carcinomas. These data sets include expression microarrays for genes and microRNAs -- short, non-coding strands of RNA that downregulate gene expression through RNA interference -- as well as days_to_death and days_to_last_followup fields for each tumor sample. Our aim is to develop a software tool that screens TCGA data sets for genes/miRNAs with functional involvement in specific cancers. Furthermore, our computational pipeline is intended to produce a set of visualizati…
Deciphering regulatory events that drive malignant transformation represents a major challenge for systems biology. Here we analyzed genome-wide transcription profiling of an in-vitro transformation process. We focused on a cluster of genes whose expression levels increased as a function of p53 and p16INK4A tumor suppressors inactivation. This cluster predominantly consists of cell cycle genes and constitutes a signature of a diversity of cancers. By linking expression profiles of the genes in the cluster with the dynamic behavior of p53 and p16INK4A, we identified a promoter architecture that…
