Gene regulation underlies environmental adaptation in house mice.
Katya L MackMallory A BallingerMegan Phifer-RixeyMichael W NachmanPublished in: Genome research (2018)
Changes in cis-regulatory regions are thought to play a major role in the genetic basis of adaptation. However, few studies have linked cis-regulatory variation with adaptation in natural populations. Here, using a combination of exome and RNA-seq data, we performed expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses to study the genetic architecture of regulatory variation in wild house mice (Mus musculus domesticus) using individuals from five populations collected along a latitudinal cline in eastern North America. Mice in this transect showed clinal patterns of variation in several traits, including body mass. Mice were larger in more northern latitudes, in accordance with Bergmann's rule. We identified 17 genes where cis-eQTLs were clinal outliers and for which expression level was correlated with latitude. Among these clinal outliers, we identified two genes (Adam17 and Bcat2) with cis-eQTLs that were associated with adaptive body mass variation and for which expression is correlated with body mass both within and between populations. Finally, we performed a weighted gene co-expression network analysis (WGCNA) to identify expression modules associated with measures of body size variation in these mice. These findings demonstrate the power of combining gene expression data with scans for selection to identify genes involved in adaptive phenotypic evolution, and also provide strong evidence for cis-regulatory elements as essential loci of environmental adaptation in natural populations.
Keyphrases
- poor prognosis
- genome wide
- gene expression
- network analysis
- rna seq
- high fat diet induced
- binding protein
- dna methylation
- copy number
- long non coding rna
- high resolution
- magnetic resonance
- transcription factor
- type diabetes
- genetic diversity
- single cell
- risk assessment
- adipose tissue
- climate change
- metabolic syndrome
- skeletal muscle
- mass spectrometry
- deep learning
- insulin resistance
- south africa
- artificial intelligence
- human health
- bioinformatics analysis