An Integrative Genetics Approach to Identify Candidate Genes Regulating BMD: Combining Linkage, Gene Expression, and Association

Abstract

Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J × C3H/HeJ (BXH) F 2 mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F 2 mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.

Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density

“…We recently described the genome-wide identification of femoral BMD QTLs in a C57BL/6J × C3H/HeJ (BXH) F2 cross and the identification of candidate genes using eQTL analysis and causality modeling (25). Here, we extend these initial observations and identify bicaudal C homolog 1 (Bicc1) as the gene tionally, Bicc1 levels were 35% higher in male F2 mice, independent of Bmd43 genotype ( Figure 1G).…”

A high-resolution association mapping panel for the dissection of complex traits in mice

“…Clearly, association analysis in outbred, heterogeneous stocks of mice can be used (Valdar et al 2006b;Flint and Mott 2008;Ghazalpour et al 2008;Farber et al 2009). One of these studies found hundreds of significant associations for 97 typed traits with an average 95% confidence interval of 2.8 Mb, which is similar to the resolution in the HMDP (Valdar et al 2006b).…”

Exploiting Differential Gene Expression and Epistasis to Discover Candidate Genes for Drought-Associated QTLs in Arabidopsis thaliana

“…Many approaches permit inference of potential candidate genes underlying QTLs, including CIT (Schadt et al, 2005), differential expression analyses (Drake et al, 2006;Farber et al, 2009), and partial regressions (Bing and Hoeschele, 2005); however, it is difficult to rank or infer the effect of each without significant additional data, such as reverse genetics experiments. By combining gene expression data with genetic mapping approaches, we presented a method to define and rank sets of candidate genes for any QTL.…”

“…Through analysis of colocalization between differentially expressed genes and phenotypic trait QTLs, it is possible to produce lists of candidate genes (Swamy et al, 2013); however, the researcher is often left with long and unwieldy lists of candidates without direction regarding which genes to pursue further. Several approaches have been developed in mouse and human model systems to solve this problem by ranking candidate genes underlying QTL regions through the joint analysis of genome-wide transcript abundance data with trait and genotype data of QTL studies (Schadt et al, 2005;Drake et al, 2006;Farber et al, 2009).…”