Advanced Genetic Approaches in Discovery and Characterization of Genes Involved With Osteoporosis in Mouse and Human

Abstract

Osteoporosis is a complex condition with contributions from, and interactions between, multiple genetic loci and environmental factors. This review summarizes key advances in the application of genetic approaches for the identification of osteoporosis susceptibility genes. Genome-wide linkage analysis (GWLA) is the classical approach for identification of genes that cause monogenic diseases; however, it has shown limited success for complex diseases like osteoporosis. In contrast, genome-wide association studies (GWAS) have successfully identified over 200 osteoporosis susceptibility loci with genome-wide significance, and have provided most of the candidate genes identified to date. Phenome-wide association studies (PheWAS) apply a phenotype-to-genotype approach which can be used to complement GWAS. PheWAS is capable of characterizing the association between osteoporosis and uncommon and rare genetic variants. Another alternative approach, whole genome sequencing (WGS), will enable the discovery of uncommon and rare genetic variants in osteoporosis. Meta-analysis with increasing statistical power can offer greater confidence in gene searching through the analysis of combined results across genetic studies. Recently, new approaches to gene discovery include animal phenotype based models such as the Collaborative Cross and ENU mutagenesis. Site-directed mutagenesis and genome editing tools such as CRISPR/Cas9, TALENs and ZNFs have been used in functional analysis of candidate genes in vitro and in vivo. These resources are revolutionizing the identification of osteoporosis susceptibility genes through the use of genetically defined inbred mouse libraries, which are screened for bone phenotypes that are then correlated with known genetic variation. Identification of osteoporosis-related susceptibility genes by genetic approaches enables further characterization of gene function in animal models, with the ultimate aim being the identification of novel therapeutic targets for osteoporosis.

Keywords: GWAS; GWLA; PheWAS; WGS; collaborative cross; genome editing; osteoporosis.

FIGURE 1

Genetic predisposition and architecture in osteoporosis. Allele frequency is defined as below: common (<0.5), uncommon (<0.05), and rare (<0.005). Mutations are considered as rare variants, mostly with an allele frequency less than 0.001, often with large effect sizes. Alleles that contribute to regulation of BMD include rare variants with large effects (left top), and common variants with small effects (right bottom). A few genes such as COL1A1 and LRP5 include variants that contribute to the phenotypes in either dominant or recessive mechanisms. However, most common variants within the genes present small effects, including RANK, RANKL, OPG and LRP4. Common variants with large effects are unlikely to exist, while rare variants with small effects are difficult to identify using current technology. Less common variants with moderate effects are likely to exist, and they may explain the majority of the missing heritability of osteoporosis.

FIGURE 2

Workflow of GWAS. A typical GWAS includes three stages: discovery, replication and validation. The discovery stage focuses on identifying associations between SNPs and traits based on a large cohort with either quantitative trait or case/control phenotype data. The second stage focuses on the replication of preliminary associations in an independent cohort. Meta-analysis can be applied to increase the statistical power of individual GWAS at this stage. The final stage focuses on the validation of the detected associations through pathway analyses, determination of mechanism or genetic manipulation in animal models.

FIGURE 3

Important genetic loci associated with BMD. There are key genes identified in GWAS for BMD at various skeletal sites: total hip, femoral neck, lumbar spine, wrist or radius, and heel.

FIGURE 4

Workflow of PheWAS. The PheWAS can start with animal or directly with human cohorts. A typical approach is to identify high-impact variants within the RI strains, then correlate these variants with the mouse phenome, followed by validation of candidate variants in human cohorts.

FIGURE 5

Workflow of the collaborative cross mice screening. The CC study starts with searching for associations among strains and identification of candidate genes within the identified locus, followed by validation of those candidate genes for association with the phenotype. Correlation with human datasets add confidence for the SNPs/genes. Validation can be done by gene expression, pathway analysis and looking at the functions of selected genes through transgenic or knockout mouse models.