Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues

Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle.

Download Full-text

Mapping short tandem repeats for liver gene expression traits helps prioritize potential causal variants for complex traits in pigs

Journal of Animal Science and Biotechnology ◽

10.1186/s40104-021-00658-z ◽

2022 ◽

Vol 13 (1) ◽

Author(s):

Zhongzi Wu ◽

Huanfa Gong ◽

Zhimin Zhou ◽

Tao Jiang ◽

Ziqi Lin ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Short Tandem Repeats ◽

Tandem Repeats ◽

Genome Wide Association Study ◽

Complex Trait ◽

Validation Population ◽

Causal Variants ◽

Liver Gene ◽

Short Tandem

Abstract Background Short tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds. Results We identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively. Conclusions This study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.

Download Full-text

LDpred-funct: incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets

10.1101/375337 ◽

2018 ◽

Cited By ~ 21

Author(s):

Carla Márquez-Luna ◽

Steven Gazal ◽

Po-Ru Loh ◽

Samuel S. Kim ◽

Nicholas Furlotte ◽

...

Keyword(s):

Complex Traits ◽

Prediction Accuracy ◽

Causal Effect ◽

Complex Trait ◽

Training Data ◽

Data Sets ◽

Uk Biobank ◽

Validation Data ◽

Functional Regions ◽

The Uk

AbstractGenetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a new method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, which includes coding, conserved, regulatory and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. LDpred-funct attained higher prediction accuracy than other polygenic prediction methods in simulations using real genotypes. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank. We used association statistics from British-ancestry samples as training data (avg N=373K) and samples of other European ancestries as validation data (avg N=22K), to minimize confounding. LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R2=0.144; highest R2=0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (total N=1107K; higher heritability in UK Biobank cohort) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.

Download Full-text

Tissue-specific sex differences in human gene expression

Human Molecular Genetics ◽

10.1093/hmg/ddz090 ◽

2019 ◽

Vol 28 (17) ◽

pp. 2976-2986 ◽

Cited By ~ 9

Author(s):

Irfahan Kassam ◽

Yang Wu ◽

Jian Yang ◽

Peter M Visscher ◽

Allan F McRae

Keyword(s):

Gene Expression ◽

Sex Differences ◽

Complex Traits ◽

Tissue Expression ◽

Regulatory Elements ◽

Tissue Specific ◽

Significance Threshold ◽

Expression Of Genes ◽

Causal Variants ◽

Similar Distance

Abstract Despite extensive sex differences in human complex traits and disease, the male and female genomes differ only in the sex chromosomes. This implies that most sex-differentiated traits are the result of differences in the expression of genes that are common to both sexes. While sex differences in gene expression have been observed in a range of different tissues, the biological mechanisms for tissue-specific sex differences (TSSDs) in gene expression are not well understood. A total of 30 640 autosomal and 1021 X-linked transcripts were tested for heterogeneity in sex difference effect sizes in n = 617 individuals across 40 tissue types in Genotype–Tissue Expression (GTEx). This identified 65 autosomal and 66 X-linked TSSD transcripts (corresponding to unique genes) at a stringent significance threshold. Results for X-linked TSSD transcripts showed mainly concordant direction of sex differences across tissues and replicate previous findings. Autosomal TSSD transcripts had mainly discordant direction of sex differences across tissues. The top cis-expression quantitative trait loci (eQTLs) across tissues for autosomal TSSD transcripts are located a similar distance away from the nearest androgen and estrogen binding motifs and the nearest enhancer, as compared to cis-eQTLs for transcripts with stable sex differences in gene expression across tissue types. Enhancer regions that overlap top cis-eQTLs for TSSD transcripts, however, were found to be more dispersed across tissues. These observations suggest that androgen and estrogen regulatory elements in a cis region may play a common role in sex differences in gene expression, but TSSD in gene expression may additionally be due to causal variants located in tissue-specific enhancer regions.

Download Full-text

Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples

Genetics ◽

10.1534/genetics.118.301865 ◽

2019 ◽

Vol 212 (3) ◽

pp. 919-929

Author(s):

Daniel A. Skelly ◽

Narayanan Raghupathy ◽

Raymond F. Robledo ◽

Joel H. Graber ◽

Elissa J. Chesler

Keyword(s):

Gene Expression ◽

Canonical Correlation ◽

Complex Traits ◽

Behavioral Genetics ◽

Association Studies ◽

Complex Trait ◽

Integrated Analysis ◽

Data Set ◽

Trait Analysis ◽

Molecular Features

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

Download Full-text

A Genome-Wide Chronological Study of Gene Expression and Two Histone Modifications, H3K4me3 and H3K9ac, during Developmental Leaf Senescence

PLANT PHYSIOLOGY ◽

10.1104/pp.114.252999 ◽

2015 ◽

Vol 168 (4) ◽

pp. 1246-1261 ◽

Cited By ~ 46

Author(s):

Judy A. Brusslan ◽

Giancarlo Bonora ◽

Ana M. Rus-Canterbury ◽

Fayha Tariq ◽

Artur Jaroszewicz ◽

...

Keyword(s):

Gene Expression ◽

Histone Modifications ◽

Leaf Senescence ◽

Genome Wide ◽

A Genome

Download Full-text

Ancestral contributions to contemporary European complex traits

10.1101/2021.08.03.454888 ◽

2021 ◽

Author(s):

Davide Marnetto ◽

Vasili Pankratov ◽

Mayukh Mondal ◽

Francesco Montinaro ◽

Katri Pärna ◽

...

Keyword(s):

Bronze Age ◽

Complex Traits ◽

Phenotypic Variability ◽

Complex Trait ◽

Blood Cholesterol ◽

Hunter Gatherers ◽

Genetic Components ◽

Genome Wide ◽

A Genome ◽

Genomic Regions

The contemporary European genetic makeup formed in the last 8000 years as the combination of three main genetic components: the local Western Hunter-Gatherers, the incoming Neolithic Farmers from Anatolia and the Bronze Age component from the Pontic Steppes. When meeting into the post-Neolithic European environment, the genetic variants accumulated during their three distinct evolutionary histories mixed and came into contact with new environmental challenges. Here we investigate how this genetic legacy reflects on the complex trait landscape of contemporary European populations, using the Estonian Biobank as a case study. For the first time we directly connect the phenotypic information available from biobank samples with the genetic similarity to these ancestral groups, both at a genome-wide level and focusing on genomic regions associated with each of the 27 complex traits we investigated. We also found SNPs connected to pigmentation, cholesterol, sleep, diastolic blood pressure, and body mass index (BMI) to show signals of selection following the post Neolithic admixture events. We recapitulate existing knowledge about pigmentation traits, corroborate the connection between Steppe ancestry and height and highlight novel associations. Among others, we report the contribution of Hunter Gatherer ancestry towards high BMI and low blood cholesterol levels. Our results show that the ancient components that form the contemporary European genome were differentiated enough to contribute ancestry-specific signatures to the phenotypic variability displayed by contemporary individuals in at least 11 out of 27 of the complex traits investigated here.

Download Full-text

Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

10.1101/808295 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yuhua Zhang ◽

Corbin Quick ◽

Ketian Yu ◽

Alvaro Barbeira ◽

Francesca Luca ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Large Scale ◽

Molecular Mechanisms ◽

Association Studies ◽

Complex Trait ◽

Causal Effects ◽

Biological Mechanisms ◽

Integrative Framework ◽

Eqtl Data

AbstractTranscriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

Download Full-text

Waves of chromatin modifications in mouse dendritic cells in response to LPS stimulation

10.1101/066472 ◽

2016 ◽

Cited By ~ 2

Author(s):

Alexis Vandenbon ◽

Yutaro Kumagai ◽

Yutaka Suzuki ◽

Kenta Nakai

Keyword(s):

Gene Expression ◽

Dendritic Cells ◽

Histone Modifications ◽

Transcriptional Activation ◽

Control Of Gene Expression ◽

Time Series Dataset ◽

Binding Data ◽

A Genome ◽

Extracellular Stimuli ◽

Wide Scale

AbstractBackgroundThe importance of transcription factors (TFs) and epigenetic modifications in the control of gene expression is widely accepted. However, causal relationships between changes in TF binding, histone modifications, and gene expression during the response to extracellular stimuli are not well understood. Here, we analyzed the ordering of these events on a genome-wide scale in dendritic cells (DCs) in response to lipopolysaccharide (LPS) stimulation.ResultsUsing a ChIP-seq time series dataset, we found that the LPS-induced accumulation of different histone modifications follow clearly distinct patterns. Increases in H3K4me3 appear to coincide with transcriptional activation. In contrast, H3K9K14ac accumulates early after stimulation, and H3K36me3 at later time points. Integrative analysis with TF binding data revealed potential links between TF activation and dynamics in histone modifications. Especially, LPS-induced increases in H3K9K14ac and H3K4me3 were associated with binding by STAT1/2, and were severely impaired inStat1-/-cells.ConclusionsWhile the timing of short-term changes of some histone modifications coincides with changes in transcriptional activity, this is not the case for others. In the latter case, dynamics in modifications more likely reflect strict regulation by stimulus-induced TFs, and their interactions with chromatin modifiers.

Download Full-text

Effect sizes of causal variants for gene expression and complex traits differ between populations

10.1101/2021.12.06.471235 ◽

2021 ◽

Author(s):

Roshni A. Patel ◽

Shaila A. Musharoff ◽

Jeffrey P. Spence ◽

Harold Pimentel ◽

Catherine Tcheandjieu ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Association Studies ◽

Causal Variant ◽

Effect Sizes ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Polygenic Scores ◽

Causal Variants ◽

Variant Effect

Despite the growing number of genome-wide association studies (GWAS) for complex traits, it remains unclear whether effect sizes of causal genetic variants differ between populations. In principle, effect sizes of causal variants could differ between populations due to gene-by-gene or gene-by-environment interactions. However, comparing causal variant effect sizes is challenging: it is difficult to know which variants are causal, and comparisons of variant effect sizes are confounded by differences in linkage disequilibrium (LD) structure between ancestries. Here, we develop a method to assess causal variant effect size differences that overcomes these limitations. Specifically, we leverage the fact that segments of European ancestry shared between European-American and admixed African-American individuals have similar LD structure, allowing for unbiased comparisons of variant effect sizes in European ancestry segments. We apply our method to two types of traits: gene expression and low-density lipoprotein cholesterol (LDL-C). We find that causal variant effect sizes for gene expression are significantly different between European-Americans and African-Americans; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. Cross-population differences in variant effect sizes highlight the role of genetic interactions in trait architecture and will contribute to the poor portability of polygenic scores across populations, reinforcing the importance of conducting GWAS on individuals of diverse ancestries and environments.

Download Full-text

Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits

10.1101/242792 ◽

2018 ◽

Author(s):

Min Wang ◽

Timothy P Hancock ◽

Amanda J. Chamberlain ◽

Christy J. Vander Jagt ◽

Jennie E Pryce ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Bovine Genome ◽

Search Space ◽

Ctcf Binding ◽

P Value ◽

Trna Genes ◽

Specific Expression ◽

Binding Motifs ◽

Regulatory Variants

AbstractBackgroundTopological association domains (TADs) are chromosomal domains characterised by frequent internal DNA-DNA interactions. The transcription factor CTCF binds to conserved DNA sequence patterns called CTCF binding motifs to either prohibit or facilitate chromosomal interactions. TADs and CTCF binding motifs control gene expression, but they are not yet well defined in the bovine genome. In this paper, we sought to improve the annotation of bovine TADs and CTCF binding motifs, and assess whether the new annotation can reduce the search space for cis-regulatory variants.ResultsWe used genomic synteny to map TADs and CTCF binding motifs from humans, mice, dogs and macaques to the bovine genome. We found that our mapped TADs exhibited the same hallmark properties of those sourced from experimental data, such as housekeeping gene, tRNA genes, CTCF binding motifs, SINEs, H3K4me3 and H3K27ac. Then we showed that runs of genes with the same pattern of allele-specific expression (ASE) (either favouring paternal or maternal allele) were often located in the same TAD or between the same conserved CTCF binding motifs. Analyses of variance showed that when averaged across all bovine tissues tested, TADs explained 14% of ASE variation (standard deviation, SD: 0.056), while CTCF explained 27% (SD: 0.078). Furthermore, we showed that the quantitative trait loci (QTLs) associated with gene expression variation (eQTLs) or ASE variation (aseQTLs), which were identified from mRNA transcripts from 141 lactating cows’ white blood and milk cells, were highly enriched at putative bovine CTCF binding motifs. The most significant aseQTL and eQTL for each genic target were located within the same TAD as the gene more often than expected (Chi-Squared test P-value ≤ 0.001).ConclusionsOur results suggest that genomic synteny can be used to functionally annotate conserved transcriptional components, and provides a tool to reduce the search space for causative regulatory variants in the bovine genome.

Download Full-text