Biology subjects

Yang, J.

Publications and source records attributed to Yang, J..

78 records · Page 5Linked to original sources

bioRxiv2017

A scalable Bayesian method for integrating functional information in genome-wide association studies

Although genome-wide association studies (GWASs) have identified many risk loci for complex traits and common diseases, most of the identified associations reside in noncoding regions and have unknown biological functions. Recent genomic sequencing studies have produced a rich resource of annotations that help characterize the function of genetic variants. Integrative analysis that incorporates these functional annotations into GWAS can help elucidate the biological mechanisms underlying the identified associations and help prioritize causal-variants. Here, we develop a novel, flexible Bayesian variable selection model with efficient computational techniques for such integrative analysis. Different from previous approaches, our method models the effect-size distribution and probability of causality for variants with different annotations and jointly models genome-wide variants to account for linkage disequilibrium (LD), thus prioritizing associations based on the quantification of the annotations and allowing for multiple causal-variants per locus. Our efficient computational algorithm dramatically improves both computational speed and posterior sampling convergence by taking advantage of the block-wise LD structures of human genomes. With simulations, we show that our method accurately quantifies the functional enrichment and performs more powerful for identifying true causal-variants than several competing methods. The power gain brought up by our method is especially apparent in cases when multiple causal-variants in LD reside in the same locus. We also apply our method for an in-depth GWAS of age-related macular degeneration with 33,976 individuals and 9,857,286 variants. We find the strongest enrichment for causality among non-synonymous variants (54x more likely to be causal, 1.4x larger effect-sizes) and variants in active promoter (7.8x more likely, 1.4x larger effect-sizes), as well as identify 5 potentially novel loci in addition to the 32 known AMD risk loci. In conclusion, our method is shown to efficiently integrate functional information in GWASs, helping identify causal variants and underlying biology.\n\nAuthor summaryWe propose a novel Bayesian hierarchical model to account for linkage disequilibrium (LD) and multiple functional annotations in GWAS, paired with an expectation-maximization Markov chain Monte Carlo (EM-MCMC) computational algorithm to jointly analyze genome-wide variants. Our method improves the MCMC convergence property to ensure accurate Bayesian inference of the quantifications of the functional enrichment pattern and fine-mapped association results. By applying our method to the real GWAS of age-related macular degeneration (AMD) with various functional annotations (i.e., gene-based, regulatory, and chromatin states), we find that the variants of non-synonymous, coding, and active promoter annotations have the highest causal probability and the largest effect-sizes. In addition, our method produces fine-mapped association results in the identified risk loci, two of which are shown as examples (C2/CFB/SKIV2L and C3) with justifications by haplotype analysis, model comparison, and conditional analysis. Therefore, we believe our integrative method will be useful for quantifying the enrichment pattern of functional annotations in GWAS, and then prioritizing associations with respect to the learned functional enrichment pattern.

genetics↗

bioRxiv2017

A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. Systematic integration of these heterogeneous data not only serves as a promising tool for identifying new drug-target interactions (DTIs), which is an important step in drug development, but also provides a more complete understanding of the molecular mechanisms of drug action. In this work, we integrate diverse drug-related information, including drugs, proteins, diseases and side-effects, together with their interactions, associations or similarities, to construct a heterogeneous network with 12,015 nodes and 1,895,445 edges. We then develop a new computational pipeline, called DTINet, to predict novel drug-target interactions from the constructed heterogeneous network. Specifically, DTINet focuses on learning a low-dimensional vector representation of features for each node, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then predicts the likelihood of a new DTI based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for DTI prediction. Moreover, we have experimentally validated the novel interactions between three drugs and the cyclooxygenase (COX) protein family predicted by DTINet, and demonstrated the new potential applications of these identified COX inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs. The source code of DTINet and the input heterogeneous network data can be downloaded from http://github.com/luoyunan/DTINet.

bioinformatics↗

bioRxiv2016

Human pancreatic β cell lncRNAs control cell-specific regulatory networks

Recent studies have uncovered thousands of long non-coding RNAs (IncRNAs) in human pancreatic {beta} cells. {beta} cell lncRNAs are often cell type-specific, and exhibit dynamic regulation during differentiation or upon changing glucose concentrations. Although these features hint at a role of lncRNAs in {beta} cell gene regulation and diabetes, the function of {beta} cell lncRNAs remains largely unknown. In this study, we investigated the function of {beta} cell-specific lncRNAs and transcription factors using transcript knockdowns and co-expression network analysis. This revealed lncRNAs that function in concert with transcription factors to regulate {beta} cell-specific transcriptional networks. We further demonstrate that lncRNA PLUTO affects local three-dimensional chromatin structure and transcription of PDX1, encoding a key {beta} cell transcription factor, and that both PLUTO and PDX1 are downregulated in islets from donors with type 2 diabetes or impaired glucose tolerance. These results implicate lncRNAs in the regulation of {beta} cell-specific transcription factor networks.

genomics↗

bioRxiv2016

Multiple Confounders Correction with Regularized Linear Mixed Effect Models, with Application in Biological Processes

In this paper, we inspect the performance of regularized linear mixed effect models, as an extension of linear mixed effect model, when multiple confounding factors coexist. We first review its parameter estimation algorithms before we introduce three different methods for multiple confounding factors correction, namely concatenation, sequence, and interpolation. Then we investigate the performance on variable selection task and predictive task on three different data sets, synthetic data set, semi-empirical synthetic data set based on genome sequences and brain wave data set connecting to confused mental states. Our results suggest that sequence multiple confounding factors corrections behave the best when different confounders contribute equally to response variables. On the other hand, when various confounders affect the response variable unevenly, results mainly rely on the degree of how the major confounder is corrected.

bioinformatics↗

bioRxiv2016

Incomplete dominance of deleterious alleles contribute substantially to trait variation and heterosis in maize

AbstractDeleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring by crossing two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding.\n\nAuthor SummaryA key long-term goal of biology is understanding the genetic basis of phenotypic variation. Although most new mutations are likely disadvantageous, their prevalence and importance in explaining patterns of phenotypic variation is controversial and not well understood. In this study we combine whole genome-sequencing and field evaluation of a maize mapping population to investigate the contribution of deleterious mutations to phenotype. We show that a priori prediction of deleterious alleles correlates well with effect sizes for grain yield and that variants predicted to be more damaging are on average more recessive. We develop a simple model allowing for variation in the heterozygous effects of deleterious mutations and demonstrate its improved ability to predict both phenotypes and hybrid vigor. Our results help reconcile alternative explanations for hybrid vigor and highlight the use of leveraging evolutionary history to facilitate breeding for crop improvement.

genetics↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗