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The genetic architecture of gene expression levels in wild baboons.

Tung J, Zhou X, Alberts SC, Stephens M, Gilad Y - Elife (2015)

Bottom Line: Primate evolution has been argued to result, in part, from changes in how genes are regulated.We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set.Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, University of Chicago, Chicago, United States.

ABSTRACT
Primate evolution has been argued to result, in part, from changes in how genes are regulated. However, we still know little about gene regulation in natural primate populations. We conducted an RNA sequencing (RNA-seq)-based study of baboons from an intensively studied wild population. We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set. Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species. Further, they establish the feasibility of eQTL mapping using RNA-seq data alone, and represent an important step towards understanding the genetic architecture of gene expression in primates.

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Agreement between eQTL and ASE approaches for identifying functionalvariants.(A) Venn diagram depicting the overlap between genes withsignificant eQTL and ASE, among genes tested in both cases (note that thenumber of genes with eQTL is smaller in this figure than in the overalldata set because we consider only the set of genes that were testable forboth eQTL and ASE, n = 2280 instead of n= 10,409). Genes with significant eQTL are more likely to havesignificantly detectable ASE and vice-versa (n = 2280; p <10−25). (B) eQTL SNPs in exonic regionsthat could also be tested for ASE reveal correlated effect sizes (n= 123; p < 10−20). (C)Similarly, ASE SNPs exhibit effect sizes that are correlated withevidence for eQTL at the same sites (n = 510; p <10−45).DOI:http://dx.doi.org/10.7554/eLife.04729.010
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fig1s7: Agreement between eQTL and ASE approaches for identifying functionalvariants.(A) Venn diagram depicting the overlap between genes withsignificant eQTL and ASE, among genes tested in both cases (note that thenumber of genes with eQTL is smaller in this figure than in the overalldata set because we consider only the set of genes that were testable forboth eQTL and ASE, n = 2280 instead of n= 10,409). Genes with significant eQTL are more likely to havesignificantly detectable ASE and vice-versa (n = 2280; p <10−25). (B) eQTL SNPs in exonic regionsthat could also be tested for ASE reveal correlated effect sizes (n= 123; p < 10−20). (C)Similarly, ASE SNPs exhibit effect sizes that are correlated withevidence for eQTL at the same sites (n = 510; p <10−45).DOI:http://dx.doi.org/10.7554/eLife.04729.010

Mentions: Both analyses converged to reveal extensive segregating genetic variation affectinggene expression levels in the Amboseli population. At a 10% false discovery rate, weidentified eQTL for 1787 (17.2%) of the genes we analyzed, and evidence for ASE for510 (23.4%) of tested genes. Consistent with reports in humans (e.g., Veyrieras et al., 2008; Pickrell et al., 2010a), eQTL were strongly enriched near genetranscription start sites and in gene bodies (Figure1; controlling for the background distribution of sites tested, which werealso enriched in and around genes). Within gene bodies, eQTL were particularly likelyto be detected near transcription end sites; this potentially reflects enrichment in3′ untranslated regions, which are poorly annotated in baboon. Also asexpected, genes with eQTL were more likely to exhibit significant ASE and vice-versa(hypergeometric test: p < 10−25; Figure 1—figure supplement 7). The magnitude anddirection of ASE and eQTL were significantly correlated when an eQTL SNP could alsobe assessed for ASE (n = 123 genes; r = 0.719, p< 10−20, Figure1—figure supplement 7), and when ASE SNPs were assessed as eQTL (n= 510 genes; r = 0.575, p <10−45, Figure 1—figuresupplement 7). Detection of ASE was most strongly favored for highlyexpressed genes (i.e., higher RPKM: Wilcoxon test: p <10−208; Figure 1—figuresupplement 8), whereas detection of eQTL was most strongly favored forgenes with high local SNP density (p < 10−72; Figure 1—figure supplement 8).


The genetic architecture of gene expression levels in wild baboons.

Tung J, Zhou X, Alberts SC, Stephens M, Gilad Y - Elife (2015)

Agreement between eQTL and ASE approaches for identifying functionalvariants.(A) Venn diagram depicting the overlap between genes withsignificant eQTL and ASE, among genes tested in both cases (note that thenumber of genes with eQTL is smaller in this figure than in the overalldata set because we consider only the set of genes that were testable forboth eQTL and ASE, n = 2280 instead of n= 10,409). Genes with significant eQTL are more likely to havesignificantly detectable ASE and vice-versa (n = 2280; p <10−25). (B) eQTL SNPs in exonic regionsthat could also be tested for ASE reveal correlated effect sizes (n= 123; p < 10−20). (C)Similarly, ASE SNPs exhibit effect sizes that are correlated withevidence for eQTL at the same sites (n = 510; p <10−45).DOI:http://dx.doi.org/10.7554/eLife.04729.010
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Related In: Results  -  Collection

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fig1s7: Agreement between eQTL and ASE approaches for identifying functionalvariants.(A) Venn diagram depicting the overlap between genes withsignificant eQTL and ASE, among genes tested in both cases (note that thenumber of genes with eQTL is smaller in this figure than in the overalldata set because we consider only the set of genes that were testable forboth eQTL and ASE, n = 2280 instead of n= 10,409). Genes with significant eQTL are more likely to havesignificantly detectable ASE and vice-versa (n = 2280; p <10−25). (B) eQTL SNPs in exonic regionsthat could also be tested for ASE reveal correlated effect sizes (n= 123; p < 10−20). (C)Similarly, ASE SNPs exhibit effect sizes that are correlated withevidence for eQTL at the same sites (n = 510; p <10−45).DOI:http://dx.doi.org/10.7554/eLife.04729.010
Mentions: Both analyses converged to reveal extensive segregating genetic variation affectinggene expression levels in the Amboseli population. At a 10% false discovery rate, weidentified eQTL for 1787 (17.2%) of the genes we analyzed, and evidence for ASE for510 (23.4%) of tested genes. Consistent with reports in humans (e.g., Veyrieras et al., 2008; Pickrell et al., 2010a), eQTL were strongly enriched near genetranscription start sites and in gene bodies (Figure1; controlling for the background distribution of sites tested, which werealso enriched in and around genes). Within gene bodies, eQTL were particularly likelyto be detected near transcription end sites; this potentially reflects enrichment in3′ untranslated regions, which are poorly annotated in baboon. Also asexpected, genes with eQTL were more likely to exhibit significant ASE and vice-versa(hypergeometric test: p < 10−25; Figure 1—figure supplement 7). The magnitude anddirection of ASE and eQTL were significantly correlated when an eQTL SNP could alsobe assessed for ASE (n = 123 genes; r = 0.719, p< 10−20, Figure1—figure supplement 7), and when ASE SNPs were assessed as eQTL (n= 510 genes; r = 0.575, p <10−45, Figure 1—figuresupplement 7). Detection of ASE was most strongly favored for highlyexpressed genes (i.e., higher RPKM: Wilcoxon test: p <10−208; Figure 1—figuresupplement 8), whereas detection of eQTL was most strongly favored forgenes with high local SNP density (p < 10−72; Figure 1—figure supplement 8).

Bottom Line: Primate evolution has been argued to result, in part, from changes in how genes are regulated.We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set.Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, University of Chicago, Chicago, United States.

ABSTRACT
Primate evolution has been argued to result, in part, from changes in how genes are regulated. However, we still know little about gene regulation in natural primate populations. We conducted an RNA sequencing (RNA-seq)-based study of baboons from an intensively studied wild population. We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set. Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species. Further, they establish the feasibility of eQTL mapping using RNA-seq data alone, and represent an important step towards understanding the genetic architecture of gene expression in primates.

Show MeSH