scholarly journals Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

2019 ◽  
Author(s):  
Yang Wu ◽  
Ting Qi ◽  
Huanwei Wang ◽  
Futao Zhang ◽  
Zhili Zheng ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Analytical Approach ◽  
Human Peripheral Blood ◽  
Complex Trait ◽  
Trait Variation ◽  
Chromatin Interactions ◽  
Level Data ◽  
Trait Locus

AbstractPromoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood (n=1,980), we predicted 34,797 PAIs which showed strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites were enriched in enhancers or near expression QTLs. Genes whose promoters were involved in PAIs were more actively expressed, and gene pairs with promoter-promoter interactions were enriched for co-expression. Integration of the predicted PAIs with GWAS data highlighted interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.

scholarly journals Complex role of space in the crossing of fitness valleys by asexual populations

2014 ◽  
Vol 11 (95) ◽  
pp. 20140014 ◽  
Author(s):  
Natalia L. Komarova ◽  
Leili Shahriyari ◽  
Dominik Wodarz
Keyword(s):  
Complex Traits ◽  
Contact Process ◽  
Complex Trait ◽  
Spatial Interactions ◽  
Wild Type ◽  
Contact Processes ◽  
Rate Of Evolution ◽  
Moran Process ◽  
Asexual Populations

The evolution of complex traits requires the accumulation of multiple mutations, which can be disadvantageous, neutral or advantageous relative to the wild-type. We study two spatial (two-dimensional) models of fitness valley crossing (the constant-population Moran process and the non-constant-population contact process), varying the number of loci involved and the degree of mixing. We find that spatial interactions accelerate the crossing of fitness valleys in the Moran process in the context of neutral and disadvantageous intermediate mutants because of the formation of mutant islands that increase the lifespan of mutant lineages. By contrast, in the contact process, spatial structure can accelerate or delay the emergence of the complex trait, and there can even be an optimal degree of mixing that maximizes the rate of evolution. For advantageous intermediate mutants, spatial interactions always delay the evolution of complex traits, in both the Moran and contact processes. The role of the mutant islands here is the opposite: instead of protecting, they constrict the growth of mutants. We conclude that the laws of population growth can be crucial for the effect of spatial interactions on the rate of evolution, and we relate the two processes explored here to different biological situations.

scholarly journals Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Adrienne Tin ◽  
Pascal Schlosser ◽  
Pamela R. Matias-Garcia ◽  
Chris H. L. Thio ◽  
Roby Joehanes ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Cardiometabolic Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Elevated Serum ◽  
Complex Trait ◽  
Serum Urate ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractElevated serum urate levels, a complex trait and major risk factor for incident gout, are correlated with cardiometabolic traits via incompletely understood mechanisms. DNA methylation in whole blood captures genetic and environmental influences and is assessed in transethnic meta-analysis of epigenome-wide association studies (EWAS) of serum urate (discovery, n = 12,474, replication, n = 5522). The 100 replicated, epigenome-wide significant (p < 1.1E–7) CpGs explain 11.6% of the serum urate variance. At SLC2A9, the serum urate locus with the largest effect in genome-wide association studies (GWAS), five CpGs are associated with SLC2A9 gene expression. Four CpGs at SLC2A9 have significant causal effects on serum urate levels and/or gout, and two of these partly mediate the effects of urate-associated GWAS variants. In other genes, including SLC7A11 and PHGDH, 17 urate-associated CpGs are associated with conditions defining metabolic syndrome, suggesting that these CpGs may represent a blood DNA methylation signature of cardiometabolic risk factors. This study demonstrates that EWAS can provide new insights into GWAS loci and the correlation of serum urate with other complex traits.

scholarly journals Systematic Mendelian randomization framework elucidates hundreds of genetic loci which may influence disease through changes in DNA methylation levels

2017 ◽  
Author(s):  
Tom G. Richardson ◽  
Philip C. Haycock ◽  
Jie Zheng ◽  
Nicholas J. Timpson ◽  
Tom R. Gaunt ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Quantitative Trait Loci ◽  
Genetic Variants ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Complex Disease ◽  
Mendelian Randomization ◽  
High Rate ◽  
Trait Variation ◽  
Trait Loci

AbstractWe have undertaken an extensive Mendelian randomization (MR) study using methylation quantitative trait loci (mQTL) as genetic instruments to assess the potential causal relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we observed 1,191 effects across 62 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-mQTL) and complex trait variation (P<1.39x10−08). Joint likelihood mapping provided evidence that the causal mQTL for 364 of these effects across 58 traits was also likely the causal variant for trait variation. These effects showed a high rate of replication in the UK Biobank dataset for 14 selected traits, as 121 of the attempted 129 effects replicated. Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 319 of the 364 mQTL effects also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in identifying candidate loci for further evaluation and help develop mechanistic insight into the aetiology of complex disease.

scholarly journals Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

Genetics ◽  
2022 ◽  
Author(s):  
Stuart J Macdonald ◽  
Kristen M Cloud-Richardson ◽  
Dylan J Sims-West ◽  
Anthony D Long
Keyword(s):  
Qtl Mapping ◽  
Complex Traits ◽  
Recombinant Inbred Lines ◽  
Attractive Alternative ◽  
Trait Variation ◽  
Large Panels ◽  
Mapping Strategy ◽  
Trait Locus ◽  
Genomic Regions ◽  
And Control

Abstract Despite the value of Recombinant Inbred Lines (RILs) for the dissection of complex traits, large panels can be difficult to maintain, distribute, and phenotype. An attractive alternative to RILs for many traits leverages selecting phenotypically extreme individuals from a segregating population, and subjecting pools of selected and control individuals to sequencing. Under a bulked or extreme segregant analysis paradigm, genomic regions contributing to trait variation are revealed as frequency differences between pools. Here we describe such an extreme quantitative trait locus, or X-QTL, mapping strategy that builds on an existing multiparental population, the DSPR (Drosophila Synthetic Population Resource), and involves phenotyping and genotyping a population derived by mixing hundreds of DSPR RILs. Simulations demonstrate that challenging, yet experimentally tractable X-QTL designs ( &gt; =4 replicates, &gt; =5000 individuals/replicate, and selecting the 5-10% most extreme animals) yield at least the same power as traditional RIL-based QTL mapping and can localize variants with sub-centimorgan resolution. We empirically demonstrate the effectiveness of the approach using a 4-fold replicated X-QTL experiment that identifies 7 QTL for caffeine resistance. Two mapped X-QTL factors replicate loci previously identified in RILs, 6/7 are associated with excellent candidate genes, and RNAi knock-downs support the involvement of 4 genes in the genetic control of trait variation. For many traits of interest to drosophilists, a bulked phenotyping/genotyping X-QTL design has considerable advantages.

scholarly journals Quantifying the contribution of dominance effects to complex trait variation in biobank-scale data

2020 ◽  
Author(s):  
Ali Pazokitoroudi ◽  
Alec M. Chiu ◽  
Kathryn S. Burch ◽  
Bogdan Pasaniuc ◽  
Sriram Sankararaman
Keyword(s):  
Complex Traits ◽  
Complex Trait ◽  
Genetic Effects ◽  
Trait Variation ◽  
Wide Range ◽  
Unbiased Estimates ◽  
The Uk ◽  
Additive Genetic Effects ◽  
Intense Debate ◽  
Scale Data

AbstractThe proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of Biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method that can partition the variation in complex traits into variance that can be attributed to additive (additive heritability) and dominance (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency, MAF > 1%) and 50 quantitative traits measured in 291, 273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no evidence for dominance heritability ( accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.

scholarly journals Genetic control of RNA splicing and its distinctive role in complex trait variation

2021 ◽  
Author(s):  
Jian Yang ◽  
Ting Qi ◽  
Yang Wu ◽  
Futao Zhang ◽  
Jian Zeng
Keyword(s):  
Complex Traits ◽  
Rna Splicing ◽  
Association Studies ◽  
Genetic Regulation ◽  
Complex Trait ◽  
Regulation Of Transcription ◽  
Genome Wide Association Studies ◽  
Trait Variation ◽  
Eqtl Data ◽  
Distinctive Role

Abstract Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Prior studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs), but the links to other genetic regulatory mechanisms such as splicing QTLs (sQTLs) are underexplored. Here, we introduce a transcript-based sQTL method (named THISTLE) with improved power for sQTL detection. Applying THISTLE along with LeafCutter, an event-based sQTL method, to brain transcriptomic data (n=1,073), we identified 7,491 genes with sQTLs with P<5×10^(-8) (the largest brain cis-sQTL collection to date), ~68% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for ten brain-related complex traits (including diseases), we identified 107 genes associated with the traits through the sQTLs, ~68% of which could not be discovered using eQTL data. Our study demonstrates the distinctive role of most sQTLs in genetic regulation of transcription and complex trait variation.

scholarly journals Evolution of quantitative trait locus hotspots in yeast species

2021 ◽  
Author(s):  
Emilien Peltier ◽  
Sabrina Bibi-Triki ◽  
Fabien Dutreux ◽  
Claudia Caradec ◽  
Anne Friedrich ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Basis ◽  
Yeast Species ◽  
Model Organism ◽  
Complex Trait ◽  
Wide Range ◽  
Trait Locus ◽  
Real Challenge ◽  
Drug Free

Dissecting the genetic basis of complex trait remains a real challenge. The budding yeast Saccharomyces cerevisiae has become a model organism for studying quantitative traits, successfully increasing our knowledge in many aspects. However, the exploration of the genotype-phenotype relationship in non-model yeast species could provide a deeper insight into the genetic basis of complex traits. Here, we have studied this relationship in the Lachancea waltii species which diverged from the S. cerevisiae lineage prior to the whole-genome duplication. By performing linkage mapping analyses in this species, we identified 86 quantitative trait loci (QTL) affecting growth fitness in a large number of conditions. The distribution of these loci across the genome has revealed two major QTL hotspots. A first hotspot corresponds to a general fitness QTL, impacting a wide range of conditions. By contrast, the second hotspot highlighted a fitness trade-off with a disadvantageous allele for drug-free conditions which proved to be advantageous in the presence of several drugs. Finally, the comparison of the detected QTL in L. waltii with those which had been previously identified for the same traits in a closely related species, Lachancea kluyveri, clearly revealed the absence of interspecific conservation of these loci. Altogether, our results expand our knowledge on the variation of the QTL landscape across different yeast species.

scholarly journals Cell-Specific DNA Methylation Signatures in Asthma

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 932 ◽  
Author(s):  
Andrée-Anne Hudon Thibeault ◽  
Catherine Laprise
Keyword(s):  
Dna Methylation ◽  
Mononuclear Cells ◽  
Association Studies ◽  
Complex Trait ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Global Status ◽  
A Minor

Asthma is a complex trait, often associated with atopy. The genetic contribution has been evidenced by familial occurrence. Genome-wide association studies allowed for associating numerous genes with asthma, as well as identifying new loci that have a minor contribution to its phenotype. Considering the role of environmental exposure on asthma development, an increasing amount of literature has been published on epigenetic modifications associated with this pathology and especially on DNA methylation, in an attempt to better understand its missing heritability. These studies have been conducted in different tissues, but mainly in blood or its peripheral mononuclear cells. However, there is growing evidence that epigenetic changes that occur in one cell type cannot be directly translated into another one. In this review, we compare alterations in DNA methylation from different cells of the immune system and of the respiratory tract. The cell types in which data are obtained influences the global status of alteration of DNA methylation in asthmatic individuals compared to control (an increased or a decreased DNA methylation). Given that several genes were cell-type-specific, there is a great need for comparative studies on DNA methylation from different cells, but from the same individuals in order to better understand the role of epigenetics in asthma pathophysiology.

scholarly journals OSCA: a tool for omic-data-based complex trait analysis

2018 ◽  
Author(s):  
Futao Zhang ◽  
Wenhan Chen ◽  
Zhihong Zhu ◽  
Qian Zhang ◽  
Marta F. Nabais ◽  
...  
Keyword(s):  
Complex Traits ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Random Effect ◽  
Complex Trait ◽  
Mixed Linear Model ◽  
Positive Rate ◽  
Trait Locus ◽  
Estimation Of Variance ◽  
Omic Data

AbstractThe rapid increase of omic data in the past decades has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we proposed a mixed-linear-model-based method (called MOMENT) that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for the effects of unobserved confounders as well as the correlations between distal probes induced by the confounders. We demonstrated by simulations that MOMENT showed a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package (called OSCA) together with a number of other implementations for omic-data-based analysis including the estimation of variance in a trait captured by all measures of multiple omic profiles, omic-data-based quantitative trait locus (xQTL) analysis, and meta-analysis of xQTL data.

scholarly journals Evidence of positive and negative selection associated with DNA methylation

2021 ◽  
Author(s):  
Charlie Hatcher ◽  
Gibran Hemani ◽  
Santiago Rodriguez ◽  
Tom R Gaunt ◽  
Daniel J Lawson ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Functional Role ◽  
Joint Distribution ◽  
Negative Selection ◽  
Selection Coefficient ◽  
Parents And Children ◽  
Selection For ◽  
Avon Longitudinal Study

Signatures of negative selection are pervasive amongst complex traits and diseases. However, it is unclear whether such signatures exist for DNA methylation (DNAm) that has been proposed to have a functional role in disease. We estimate polygenicity, SNP-based heritability and model the joint distribution of effect size and minor allele frequency (MAF) to estimate a selection coefficient (S) for 2000 heritable DNAm sites in 1774 individuals from the Avon Longitudinal Study of Parents and Children. Additionally, we estimate S for meta stable epi alleles and DNAm sites associated with aging and mortality, birthweight and body mass index. Quantification of MAF-dependent genetic architectures estimated from genotype and DNAm reveal evidence of positive (S>0) and negative selection (S<0) and confirm previous evidence of negative selection for birthweight. Evidence of both negative and positive selection highlights the role of DNAm as an intermediary in multiple biological pathways with competing function.

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