Genetic Fine‐Mapping and Identification of Candidate Genes and Variants for Adiposity Traits in Outbred Rats

Abstract

ObjectiveObesity is a major risk factor for multiple diseases and is in part heritable, yet the majority of causative genetic variants that drive excessive adiposity remain unknown. Here, we used outbred heterogeneous stock (HS) rats in controlled environmental conditions to fine-map novel genetic modifiers of adiposity.MethodsBody weight and visceral fat pad weights were measured in male HS rats that were also genotyped genome-wide. Quantitative trait loci (QTL) were identified by genome-wide association of imputed single nucleotide polymorphism (SNP) genotypes using a linear mixed effect model that accounts for unequal relatedness between the HS rats. Candidate genes were assessed by protein modeling and mediation analysis of expression for coding and noncoding variants, respectively.ResultsHS rats exhibited large variation in adiposity traits, which were highly heritable and correlated with metabolic health. Fine-mapping of fat pad weight and body weight revealed three QTL and prioritized five candidate genes. Fat pad weight was associated with missense SNPs in Adcy3 and Prlhr and altered expression of Krtcap3 and Slc30a3, whereas Grid2 was identified as a candidate within the body weight locus.ConclusionsThese data demonstrate the power of HS rats for identification of known and novel heritable mediators of obesity traits.

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation

“…Alternatively, a residual variation estimate, i.e. RSS, could be calculated from the shrunken haplotype effects to identify likely false positives, but not be as aggressively reduced as the variance component-based estimates, representing a middle ground approach that was found to be effective [51]. We primarily reported fixed effects estimates due to their consistency with reported expectations [26] for a study of this size.…”

“…Variant association. Variant association has been used previously in MPP (e.g., [51,77]), and uses the same underlying model as the haplotype-based mapping (Eq 2), with the QTL term now representing imputed dosages of the minor allele. Variant association can more powerfully detect QTL than haplotype mapping if the simpler variant model is closer to the underlying biological mechanism [78], though it will struggle to detect multi-allelic QTL.…”

“…Mediation analysis has recently been used with genomic data, including in humans (e.g., [10,15]) and rodents (e.g., [51,81]), to identify and refine potential intermediates of causal paths underlying phenotypes. We use a similar genome-wide mediation analysis as used with the DO [22, 33] to detect mediators of eQTL effects on gene expression.…”

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation

“…Alternatively, a residual variation estimate, i.e. RSS, could be calculated 436 from the shrunken haplotype effects to identify likely false positives, but not be as 437 aggressively reduced as the variance component-based estimates, representing a middle 438 ground approach that was found to be effective [54]. We primarily reported fixed effects 439 estimates due to their consistency with reported expectations [46] for a study of this Adult male mice (8-12 weeks old) from 47 CC strains were acquired from the University 453 of North Carolina Systems Genetics Core (listed in S1 Appendix) and maintained on an 454 NTP 2000 wafer diet (Zeigler Brothers, Inc., Gardners, PA) and water ad libitum.…”

“…Comparing QTL effects across tissues 663 To summarize patterns of the genetic regulation of gene expression and chromatin 664 accessibility across tissues, we calculated correlations between the haplotype effects of 665 QTL that map to approximately the same genomic region of the genome for the same 666 traits but in different tissues. For cross-tissue pairs of local-QTL, we required that both 667 be detected within the 20Mb window around the gene TSS or the chromatin window Variant association has been used previously in MPP (e.g., [53,54]), and uses the same 682 underlying model as the haplotype-based mapping (Eq 2), with the QTL term now 683 representing imputed dosages of the minor allele. Variant association can more 684 powerfully detect QTL than haplotype mapping if the simpler variant model is closer to 685 the underlying biological mechanism [55], though it will struggle to detect multi-allelic 686 QTL.…”