Epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in Diversity Outbred mice

ABSTRACTGenetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. Multi-parent populations provide a resource for developing methods to understand these relationships. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Using weighted gene co-expression network analysis (WGCNA), we summarized transcript data into functional modules which we then used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects. We were also able to identify alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

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Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: evidence for “polygenic epistasis”

10.1101/555383 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christoph D. Rau ◽

Natalia M. Gonzales ◽

Joshua S. Bloom ◽

Danny Park ◽

Julien Ayroles ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Statistical Tests ◽

Inbred Mice ◽

Statistical Test ◽

Genetic Ancestry ◽

Human Populations ◽

Epistatic Interactions ◽

Variable Effect ◽

The Impact

AbstractBackgroundThe majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.ResultsWe applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.ConclusionsUnlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.Author SummaryMany statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each other’s effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term “polygenic epistasis” to distinguish these interactions from the more conventional two- or multi-locus interactions.

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Massively parallel identification of causal variants underlying gene expression differences in a yeast cross

10.1101/2020.08.31.276337 ◽

2020 ◽

Author(s):

Kaushik Renganaath ◽

Rocky Cheung ◽

Laura Day ◽

Sriram Kosuri ◽

Leonid Kruglyak ◽

...

Keyword(s):

Gene Expression ◽

Binding Sites ◽

Complex Traits ◽

Massively Parallel ◽

Epistatic Interactions ◽

Regulatory Variants ◽

Causal Variants ◽

Massively Parallel Reporter Assay ◽

Regulatory Dna ◽

Affect Gene Expression

AbstractSequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5,832 natural DNA variants in the promoters of 2,503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

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Widespread Inter-Chromosomal Epistasis Regulates Glucose Homeostasis and Gene Expression

10.1101/132175 ◽

2017 ◽

Author(s):

Anlu Chen ◽

Yang Liu ◽

Scott M. Williams ◽

Nathan Morris ◽

David A. Buchner

Keyword(s):

Gene Expression ◽

Mouse Chromosome ◽

Complex Traits ◽

Large Scale ◽

Allelic Variation ◽

Genetic Regulation ◽

Chromosome Substitution ◽

Epistatic Interactions ◽

Dominant Feature ◽

Chromosome Substitution Strains

AbstractThe relative contributions of additive versus non-additive interactions in the regulation of complex traits remains controversial. This may be in part because large-scale epistasis has traditionally been difficult to detect in complex, multi-cellular organisms. We hypothesized that it would be easier to detect interactions using mouse chromosome substitution strains that simultaneously incorporate allelic variation in many genes on a controlled genetic background. Analyzing metabolic traits and gene expression levels in the offspring of a series of crosses between mouse chromosome substitution strains demonstrated that inter-chromosomal epistasis was a dominant feature of these complex traits. Epistasis typically accounted for a larger proportion of the heritable effects than those due solely to additive effects. These epistatic interactions typically resulted in trait values returning to the levels of the parental CSS host strain. Due to the large epistatic effects, analyses that did not account for interactions consistently underestimated the true effect sizes due to allelic variation or failed to detect the loci controlling trait variation. These studies demonstrate that epistatic interactions are a common feature of complex traits and thus identifying these interactions is key to understanding their genetic regulation.

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Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross

eLife ◽

10.7554/elife.62669 ◽

2020 ◽

Vol 9 ◽

Author(s):

Kaushik Renganaath ◽

Rocky Cheung ◽

Laura Day ◽

Sriram Kosuri ◽

Leonid Kruglyak ◽

...

Keyword(s):

Gene Expression ◽

Binding Sites ◽

Complex Traits ◽

Epistatic Interactions ◽

Regulatory Variants ◽

Causal Variants ◽

Massively Parallel Reporter Assay ◽

Regulatory Dna ◽

Systematic Identification ◽

Affect Gene Expression

Sequence variation in regulatory DNA alters gene expression and shapes genetically complex traits. However, the identification of individual, causal regulatory variants is challenging. Here, we used a massively parallel reporter assay to measure the cis-regulatory consequences of 5832 natural DNA variants in the promoters of 2503 genes in the yeast Saccharomyces cerevisiae. We identified 451 causal variants, which underlie genetic loci known to affect gene expression. Several promoters harbored multiple causal variants. In five promoters, pairs of variants showed non-additive, epistatic interactions. Causal variants were enriched at conserved nucleotides, tended to have low derived allele frequency, and were depleted from promoters of essential genes, which is consistent with the action of negative selection. Causal variants were also enriched for alterations in transcription factor binding sites. Models integrating these features provided modest, but statistically significant, ability to predict causal variants. This work revealed a complex molecular basis for cis-acting regulatory variation.

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Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice

Genetics ◽

10.1534/genetics.116.198051 ◽

2017 ◽

Vol 206 (2) ◽

pp. 621-639 ◽

Cited By ~ 29

Author(s):

Anna L. Tyler ◽

Bo Ji ◽

Daniel M. Gatti ◽

Steven C. Munger ◽

Gary A. Churchill ◽

...

Keyword(s):

Gene Expression ◽

Metabolic Disease ◽

Diversity Outbred ◽

Outbred Mice

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Genetic factors contributing to extensive variability of sex-specific hepatic gene expression in Diversity Outbred mice

PLoS ONE ◽

10.1371/journal.pone.0242665 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242665

Author(s):

Tisha Melia ◽

David J. Waxman

Keyword(s):

Gene Expression ◽

Growth Hormone ◽

Sex Differences ◽

Genetic Factors ◽

Individual Variability ◽

Specific Gene ◽

Open Chromatin ◽

Diversity Outbred ◽

Outbred Mice ◽

The Individual

Sex-specific transcription characterizes hundreds of genes in mouse liver, many implicated in sex-differential drug and lipid metabolism and disease susceptibility. While the regulation of liver sex differences by growth hormone-activated STAT5 is well established, little is known about autosomal genetic factors regulating the sex-specific liver transcriptome. Here we show, using genotyping and expression data from a large population of Diversity Outbred mice, that genetic factors work in tandem with growth hormone to control the individual variability of hundreds of sex-biased genes, including many long non-coding RNA genes. Significant associations between single nucleotide polymorphisms and sex-specific gene expression were identified as expression quantitative trait loci (eQTLs), many of which showed strong sex-dependent associations. Remarkably, autosomal genetic modifiers of sex-specific genes were found to account for more than 200 instances of gain or loss of sex-specificity across eight Diversity Outbred mouse founder strains. Sex-biased STAT5 binding sites and open chromatin regions with strain-specific variants were significantly enriched at eQTL regions regulating correspondingly sex-specific genes, supporting the proposed functional regulatory nature of the eQTL regions identified. Binding of the male-biased, growth hormone-regulated repressor BCL6 was most highly enriched at trans-eQTL regions controlling female-specific genes. Co-regulated gene clusters defined by overlapping eQTLs included sets of highly correlated genes from different chromosomes, further supporting trans-eQTL action. These findings elucidate how an unexpectedly large number of autosomal factors work in tandem with growth hormone signaling pathways to regulate the individual variability associated with sex differences in liver metabolism and disease.

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Linking circadian time to growth rate quantitatively via carbon metabolism

10.1101/105437 ◽

2017 ◽

Cited By ~ 6

Author(s):

Yin Hoon Chew ◽

Daniel D. Seaton ◽

Virginie Mengin ◽

Anna Flis ◽

Sam T. Mugford ◽

...

Keyword(s):

Gene Expression ◽

Growth Rate ◽

Complex Traits ◽

Systems Approach ◽

Clock Genes ◽

Expression Patterns ◽

Model Organism ◽

Physiological Data ◽

Night Time ◽

Circadian Timing

Summary paragraphPredicting a multicellular organism’s phenotype quantitatively from its genotype is challenging, as genetic effects must propagate up time and length scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour, from sleep/wake cycles in mammals to flowering in plants1–3. Clock genes are rarely essential but appropriate alleles can confer a competitive advantage4,5 and have been repeatedly selected during crop domestication3,6. Here we quantitatively explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for growth of Arabidopsis thaliana7–9. The model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants. Altered night-time metabolism of stored starch accounted for most but not all of the decrease in whole-plant growth rate. Altered mobilisation of a secondary store of organic acids explained the remaining defect. Our results link genotype through specific processes to higher-level phenotypes, formalising our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits.

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