Heritability and Tissue Specificity of Expression Quantitative Trait Loci

Petretto, Enrico; Mangion, Jonathan; Dickens, Nicholas J.; Cook, Stuart A.; Kumaran, M.K.; Han, Lu; Fischer, Judith; Maatz, Henrike; Křen, Vladimı́r; Pravenec, Michal; Hübner, Norbert; Aitman, Timothy J.

doi:10.1371/journal.pgen.0020172

Cited by 193 publications

(201 citation statements)

References 39 publications

(59 reference statements)

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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

Self Cite

View full text Add to dashboard Cite

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“…The establishment of gene-to-gene relationship revealed the specific regulatory network affecting maize kernel development, although parts of which may be shared between tissues 24 . In the regulatory networks, 455 transcription factors (TFs) were found to regulate gene expression and 44 of these TFs were predicted to regulate the expression of other TFs (Supplementary Table S11).…”

Section: Articlementioning

confidence: 99%

RNA sequencing reveals the complex regulatory network in the maize kernel

et al. 2013

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RNA sequencing can simultaneously identify exonic polymorphisms and quantitate gene expression. Here we report RNA sequencing of developing maize kernels from 368 inbred lines producing 25.8 billion reads and 3.6 million single-nucleotide polymorphisms. Both the MaizeSNP50 BeadChip and the Sequenom MassArray iPLEX platforms confirm a subset of high-quality SNPs. Of these SNPs, we have mapped 931,484 to gene regions with a mean density of 40.3 SNPs per gene. The genome-wide association study identifies 16,408 expression quantitative trait loci. A two-step approach defines 95.1% of the eQTLs to a 10-kb region, and 67.7% of them include a single gene. The establishment of relationships between eQTLs and their targets reveals a large-scale gene regulatory network, which include the regulation of 31 zein and 16 key kernel genes. These results contribute to our understanding of kernel development and to the improvement of maize yield and nutritional quality.

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“…These genes represent members of a regulatory network that might contribute to the flexibility of the organism in regards to expressing low-heritable traits in response to different stimuli. Moreover, it was shown that the expression level of the trans-regulated genes was less heritable than that of cis-regulated genes (Petretto et al, 2006). Overall, this implies that the expression of genes associated with traits that have low heritability, like drip loss or pH, is largely dependent on trans regulation and is low in heritability.…”

Section: S Ponsuksili Et Almentioning

confidence: 99%

Expression quantitative trait loci analysis of genes in porcine muscle by quantitative real-time RT-PCR compared to microarray data

et al. 2010

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Genetic analysis of transcriptional profiling is a promising approach for identifying biological pathways and dissecting the genetics of complex traits. Here, we report on expression quantitative trait loci (eQTL) that were estimated from the quantitative real-time RT-PCR data of 276 F 2 animals and compared with eQTL identified using 74 microarrays. In total, 13 genes were selected that showed trait-dependent expression in microarray experiments and exhibited 21 eQTL. Real-time RT-PCR and microarray data revealed seven cis eQTL in total, of which one was only detected by real-time RT-PCR, one was only detected by microarray analysis, three were consistently found in overlapping intervals and two were in neighbouring intervals on the same chromosome; whereas no trans eQTL was confirmed. We demonstrate that cis regulation is a stable characteristic of individual transcripts. Consequently, a global microarray eQTL analysis of a limited number of samples can be used for exploring functional and regulatory gene networks and scanning for cis eQTL, whereas the subsequent analysis of a subset of likely cis-regulated genes by realtime RT-PCR in a larger number of samples is relevant to narrow down a QTL region by targeting these positional candidate genes. In fact, when modelling SNPs of six genes as fixed effects in the eQTL analysis, eQTL peaks were shifted downwards, experimentally confirming the impact of the respective polymorphic genes, although these SNPs were not located in the regulatory sequence and these shifts occur as a result of linkage disequilibrium in the F 2 population.

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Heritability and Tissue Specificity of Expression Quantitative Trait Loci

Cited by 193 publications

References 39 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

RNA sequencing reveals the complex regulatory network in the maize kernel

Expression quantitative trait loci analysis of genes in porcine muscle by quantitative real-time RT-PCR compared to microarray data

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