Integrating GWAS and Co-expression Network Data Identifies Bone Mineral Density Genes SPTBN1 and MARK3 and an Osteoblast Functional Module

Abstract

Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. Genome-wide association studies (GWAS) for BMD have identified dozens of associations; yet, the genes responsible for most associations remain elusive. Here, we used a bone co-expression network to predict causal genes at BMD GWAS loci based on the premise that genes underlying a disease are often functionally related and functionally related genes are often co-expressed. By mapping genes implicated by BMD GWAS onto a bone co-expression network, we predicted and inferred the function of causal genes for 30 of 64 GWAS loci. We experimentally confirmed that two of the genes predicted to be causal, SPTBN1 and MARK3, are potentially responsible for the effects of GWAS loci on Chromosomes 2p16.2 and 14q32.32, respectively. This approach provides a roadmap for the dissection of additional BMD GWAS associations. Furthermore, it should be applicable to GWAS data for a wide range of diseases.

Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry

“…We used a coexpression network constructed from mouse cortical bone expression profiles . By mapping mouse homologs of human genes located in GWAS regions associated with BMD, the network previously identified an Osteoblast Functional Module containing 33 genes implicated by GWAS .…”

“…We used a coexpression network constructed from mouse cortical bone expression profiles . By mapping mouse homologs of human genes located in GWAS regions associated with BMD, the network previously identified an Osteoblast Functional Module containing 33 genes implicated by GWAS . For the association with hip geometry phenotypes, the following algorithm was applied: Step 1: Using plink2 , we generated LD intervals based on LD data from 1000 Genomes Phase I.…”

“…For the association with hip geometry phenotypes, the following algorithm was applied: Step 1: Using plink2 , we generated LD intervals based on LD data from 1000 Genomes Phase I. The interval was defined by the upstream‐most and downstream‐most SNP in LD with the index SNP at an r 2 threshold of 0.7, as described in Calabrese and colleagues . This resulted in the file “ plink.ld ” that contains all the proxies for the index SNPs based on build hg19. Step 2: To define the set of genes mapped to the intervals, we converted the hg19 LD intervals defined in step one to hg38 intervals using the UCSC lift‐over tool.…”

“…This resulted in the file “ plink.ld ” that contains all the proxies for the index SNPs based on build hg19. Step 2: To define the set of genes mapped to the intervals, we converted the hg19 LD intervals defined in step one to hg38 intervals using the UCSC lift‐over tool. A total of 12 unique genes were identified for the 5 (GWAS Discovery) regions. Step 3: We then identified mouse homologs for all 12 unique human genes and mapped these onto the bone coexpression network …”

“…Step 1: Using plink2 , we generated LD intervals based on LD data from 1000 Genomes Phase I. The interval was defined by the upstream‐most and downstream‐most SNP in LD with the index SNP at an r 2 threshold of 0.7, as described in Calabrese and colleagues . This resulted in the file “ plink.ld ” that contains all the proxies for the index SNPs based on build hg19.…”