Expression Quantitative Trait Loci: Replication, Tissue- and Sex-Specificity in Mice

Nas, Atila van; Ingram‐Drake, Leslie; Sinsheimer, Janet S.; Wang, Susanna S.; Schadt, Eric E.; Drake, Thomas A.; Lusis, Aldons J.

doi:10.1534/genetics.110.116087

Cited by 93 publications

(90 citation statements)

References 24 publications

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“…Local eQTL had higher mapping scores than distant loci (Wilcoxon rank test, P = 1.7e-07). This is consistent with previous work showing that while both local and distant eQTL exist, local eQTL typically have stronger effects (Petretto et al 2006a;Mackay et al 2009;Van Nas et al 2010). A targeted search for local eQTL where we tested only the marker situated closest to a given gene revealed local eQTL at 12% of the genes tested, for a total of 1161 local eQTL (Table S5).…”

Section: Genomic Architecture Of Mrna Expressionsupporting

confidence: 89%

Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression

Heyne

Lautenschlaeger²,

Nelson

et al. 2014

Preprint

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Interindividual differences in many behaviors are partly due to genetic differences, but the identification of the genes and variants that influence behavior remains challenging. Here, we studied an F 2 intercross of two outbred lines of rats selected for tame and aggressive behavior toward humans for .64 generations. By using a mapping approach that is able to identify genetic loci segregating within the lines, we identified four times more loci influencing tameness and aggression than by an approach that assumes fixation of causative alleles, suggesting that many causative loci were not driven to fixation by the selection. We used RNA sequencing in 150 F 2 animals to identify hundreds of loci that influence brain gene expression. Several of these loci colocalize with tameness loci and may reflect the same genetic variants. Through analyses of correlations between allele effects on behavior and gene expression, differential expression between the tame and aggressive rat selection lines, and correlations between gene expression and tameness in F 2 animals, we identify the genes Gltscr2, Lgi4, Zfp40, and Slc17a7 as candidate contributors to the strikingly different behavior of the tame and aggressive animals. B EHAVIORAL differences among members of a species are in part due to genetic variation. The identification of the genes and variants that influence behavior remains challenging. In human genome-wide association studies of psychiatric and cognitive traits, the identified loci typically explain only a small fraction of the heritability, i.e., the additive genetic contribution to the trait (Watanabe et al. 2007;Hovatta and Barlow 2008;Deary et al. 2009;Otowa et al. 2009;Calboli et al. 2010;Terracciano et al. 2010;Flint and Munafo 2013;Rietveld et al. 2013;Sokolowska and Hovatta 2013). In experimental crosses in model species, especially between inbred lines, the identified loci (termed quantitative trait loci, QTL), often explain much more of the heritability (Lynch and Walsh 1998). However, in this design the spatial resolution is limited-the QTL are wide and contain many, sometimes hundreds of genes. With the exception of a handful of identified genes with quantitative effects on behavioral traits (Yalcin et al. 2004;Watanabe et al. 2007;McGrath et al. 2009;Bendesky et al. 2012;Goodson et al. 2012), gene identification is particularly difficult for behavioral QTL that often have modest effect sizes (Flint 2003;Willis-Owen and Flint 2006;Wright et al. 2006;Hovatta and Barlow 2008).The causal variants within a QTL can alter the protein sequence encoded by a gene or affect gene expression. Such Musunuru et al. 2010). A few eQTL studies have also recently been carried out to identify candidate genes for behavioral QTL (Hitzemann et al. 2004;De Jong et al. 2011;Saba et al. 2011;Kelly et al. 2012).Here, we studied two lines of rats (Rattus norvegicus) that, starting from one wild population, have been selected for increased tameness and increased aggression toward humans, respectively. These experimental...

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Section: Genomic Architecture Of Mrna Expressionsupporting

confidence: 89%

Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression

Heyne

Lautenschlaeger²,

Nelson

et al. 2014

Preprint

View full text Add to dashboard Cite

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“…Studies of natural variation in mice have identified a number of ''hotspot'' loci associated with trans regulation of genes and clinical traits. 18,19 One of the first reported human trans-acting eQTLs involved the KLF14 transcription factor in adipose tissue. 20 The locus associated with T2D and HDL-cholesterol levels showed a cis-acting association with expression of KLF14 and ten distal genes.…”

Section: Introductionmentioning

confidence: 99%

Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits

Civelek

Pan

et al. 2017

The American Journal of Human Genetics

154

211

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Subcutaneous adipose tissue stores excess lipids and maintains energy balance. We performed expression quantitative trait locus (eQTL) analyses by using abdominal subcutaneous adipose tissue of 770 extensively phenotyped participants of the METSIM study. We identified cis-eQTLs for 12,400 genes at a 1% false-discovery rate. Among an approximately 680 known genome-wide association study (GWAS) loci for cardio-metabolic traits, we identified 140 coincident cis-eQTLs at 109 GWAS loci, including 93 eQTLs not previously described. At 49 of these 140 eQTLs, gene expression was nominally associated (p < 0.05) with levels of the GWAS trait. The size of our dataset enabled identification of five loci associated (p < 5 3 10 À8 ) with at least five genes located >5 Mb away. These trans-eQTL signals confirmed and extended the previously reported KLF14-mediated network to 55 target genes, validated the CIITA regulation of class II MHC genes, and identified ZNF800 as a candidate master regulator. Finally, we observed similar expression-clinical trait correlations of genes associated with GWAS loci in both humans and a panel of genetically diverse mice. These results provide candidate genes for further investigation of their potential roles in adipose biology and in regulating cardio-metabolic traits.

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“…An eQTL study is based on treating expression as a quantitative trait and associating it with genetic variation. eQTL studies have been tremendously successful, and have identified many loci involved in gene regulation (Ghazalpour et al 2008;Veyrieras et al 2008;Cookson et al 2009;Farber et al 2009;Hayes et al 2009;Jiménez-Gómez et al 2010;van Nas et al 2010;Steibel et al 2011;Gaffney et al 2012;Kabakchiev and Silverberg 2013;Francesconi and Lehner 2014). However, the traditional eQTL approach has some fundamental limitations.…”

mentioning

confidence: 99%

Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

et al. 2016

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The study of the genetics of gene expression is of considerable importance to understanding the nature of common, complex diseases. The most widely applied approach to identifying relationships between genetic variation and gene expression is the expression quantitative trait loci (eQTL) approach. Here, we increased the computational power of eQTL with an alternative and complementary approach based on analyzing allele specific expression (ASE). We designed a novel analytical method to identify cisacting regulatory variants based on genome sequencing and measurements of ASE from RNA-sequencing (RNA-seq) data. We evaluated the power and resolution of our method using simulated data. We then applied the method to map regulatory variants affecting gene expression in lymphoblastoid cell lines (LCLs) from 77 unrelated northern and western European individuals (CEU), which were part of the HapMap project. A total of 2309 SNPs were identified as being associated with ASE patterns. The SNPs associated with ASE were enriched within promoter regions and were significantly more likely to signal strong evidence for a regulatory role. Finally, among the candidate regulatory SNPs, we identified 108 SNPs that were previously associated with human immune diseases. With further improvements in quantifying ASE from RNA-seq, the application of our method to other datasets is expected to accelerate our understanding of the biological basis of common diseases.

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Expression Quantitative Trait Loci: Replication, Tissue- and Sex-Specificity in Mice

Cited by 93 publications

References 24 publications

Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression

Genetic Influences on Brain Gene Expression in Rats Selected for Tameness and Aggression

Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits

Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

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