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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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Accuracy of genotype calls for SNPs independently typed inHapMap3.(A) Distribution of correlations between SNPs called usingRNA-seq data and SNPs called independently by HapMap3 (n = 9919variants). (B) Estimated homozygosity levels for n =69 YRI individuals at the same set of sites; outliers (denoted with redstars) reflect those individuals with the lowest correlation betweenRNA-seq-based genotypes and HapMap3 genotypes. The four starred outliersin (B) include the three lowest accuracy individuals in theboxplots in (A).DOI:http://dx.doi.org/10.7554/eLife.04729.008
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fig1s5: Accuracy of genotype calls for SNPs independently typed inHapMap3.(A) Distribution of correlations between SNPs called usingRNA-seq data and SNPs called independently by HapMap3 (n = 9919variants). (B) Estimated homozygosity levels for n =69 YRI individuals at the same set of sites; outliers (denoted with redstars) reflect those individuals with the lowest correlation betweenRNA-seq-based genotypes and HapMap3 genotypes. The four starred outliersin (B) include the three lowest accuracy individuals in theboxplots in (A).DOI:http://dx.doi.org/10.7554/eLife.04729.008

Mentions: To assess the accuracy of the RNA-seq-based genotyping calls we performed in thebaboons, we investigated a similarly sized data set of RNA-seq reads from a humanpopulation (Pickrell et al., 2010a). Becausethis data set focused on samples from the HapMap consortium (n = 69 members ofthe Yoruba population from Ibadan, Nigeria), we were able to compare genotypes calledusing the RNA-seq pipeline to independently collected genotype data from HapMap Phase3 (r27) (International HapMap Consortium,2010). To do so, we focused on 9919 variants that were genotyped in bothdata sets. We then calculated the correlation between genotypes called in theRNA-seq-based pipeline and genotypes from HapMap, for each individual (Figure 1—figure supplement 5A). We alsofound that low accuracy was correlated with the level of apparent homozygosity in thegenotype data (Figure 1—figure supplement5B). In the baboon data, we had no individuals with unusually lowhomozygosity, but six individuals with unusually high homozygosity (>80% ofgenotype calls). These outliers were missing a median of 10.6% of data in theunimputed genotype data set, whereas all other individuals were missing a median of0.6% data. However, removing these six individuals from our analysis resulted in verysimilar results as using the full data set: 87.6% of eQTL genes (n = 1566)identified when using all individuals were also identified with this subset.


The genetic architecture of gene expression levels in wild baboons.

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

Accuracy of genotype calls for SNPs independently typed inHapMap3.(A) Distribution of correlations between SNPs called usingRNA-seq data and SNPs called independently by HapMap3 (n = 9919variants). (B) Estimated homozygosity levels for n =69 YRI individuals at the same set of sites; outliers (denoted with redstars) reflect those individuals with the lowest correlation betweenRNA-seq-based genotypes and HapMap3 genotypes. The four starred outliersin (B) include the three lowest accuracy individuals in theboxplots in (A).DOI:http://dx.doi.org/10.7554/eLife.04729.008
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4383332&req=5

fig1s5: Accuracy of genotype calls for SNPs independently typed inHapMap3.(A) Distribution of correlations between SNPs called usingRNA-seq data and SNPs called independently by HapMap3 (n = 9919variants). (B) Estimated homozygosity levels for n =69 YRI individuals at the same set of sites; outliers (denoted with redstars) reflect those individuals with the lowest correlation betweenRNA-seq-based genotypes and HapMap3 genotypes. The four starred outliersin (B) include the three lowest accuracy individuals in theboxplots in (A).DOI:http://dx.doi.org/10.7554/eLife.04729.008
Mentions: To assess the accuracy of the RNA-seq-based genotyping calls we performed in thebaboons, we investigated a similarly sized data set of RNA-seq reads from a humanpopulation (Pickrell et al., 2010a). Becausethis data set focused on samples from the HapMap consortium (n = 69 members ofthe Yoruba population from Ibadan, Nigeria), we were able to compare genotypes calledusing the RNA-seq pipeline to independently collected genotype data from HapMap Phase3 (r27) (International HapMap Consortium,2010). To do so, we focused on 9919 variants that were genotyped in bothdata sets. We then calculated the correlation between genotypes called in theRNA-seq-based pipeline and genotypes from HapMap, for each individual (Figure 1—figure supplement 5A). We alsofound that low accuracy was correlated with the level of apparent homozygosity in thegenotype data (Figure 1—figure supplement5B). In the baboon data, we had no individuals with unusually lowhomozygosity, but six individuals with unusually high homozygosity (>80% ofgenotype calls). These outliers were missing a median of 10.6% of data in theunimputed genotype data set, whereas all other individuals were missing a median of0.6% data. However, removing these six individuals from our analysis resulted in verysimilar results as using the full data set: 87.6% of eQTL genes (n = 1566)identified when using all individuals were also identified with this subset.

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