In the present study, aiming to identify loci associated with osteoporosis, we conducted a joint association study of 2 independent genome-wide association meta-analyses of femoral neck and lumbar spine bone mineral densities (BMDs): 1) an in-house study of 6 samples involving 7484 subjects, and 2) the GEFOS-seq study of 7 samples involving 32,965 subjects. The in-house samples were imputed by the 1000 genomes project phase 3 reference panel. SNP-based association test was applied to 7,998,108 autosomal SNPs in each meta-analysis, and for each SNP the 2 association signals were then combined for joint analysis and for mutual replication. Combining the evidence from both studies, we identified 2 novel loci associated with BMDs at the genome-wide significance level (α=5.0×10): 20p12.1 (rs73100693 p=2.65×10, closest gene MACROD2) and 20q13.33 (rs2380128 p=3.44×10, OSBPL2). We also replicated 7 loci that were reported by two recent studies on heel and total body BMD. Our findings provide useful insights that enhance our understanding of bone development, osteoporosis and fracture pathogenesis.

Accurately estimating the distribution and heritability of SNP effects across the genome could help explain the mystery of missing heritability. In this study, we propose a novel statistical method for estimating the distribution and heritability of SNP effects from genome-wide association studies (GWASs), and compare its performance to several existing methods using both simulations and real data. Specifically, we study the full range of GWAS summary results and link observed p values and unobserved effect sizes by (non-central) Chi-square distribution. By modeling the observed full set of association signals using a multinomial distribution, we build a likelihood function of SNP effect sizes using parametric and non-parametric maximum likelihood frameworks. Simulation studies show that the proposed method can accurately estimate effect sizes and the number of associated SNPs. As real applications, we analyze publicly available GWAS summary results for height, body mass index (BMI), and bone mineral density (BMD). Our analyses show that there are over 10,000 SNPs that might be associated with height, and the total heritability attributable to these SNPs exceeds 70 %. The heritabilities for BMI and BMD are ~10 and ~15 %, respectively. The results indicate that the proposed method has the potential to improve the accuracy of estimates of heritability and effect size for common SNPs in large-scale GWAS meta-analyses. These improved estimates may contribute to an enhanced understanding of the genetic basis of complex traits.

Objective Body fat mass (BFM) is more homogeneous and accurate than body total mass in measuring obesity, but has rarely been studied. Aiming to uncover the genetic basis of fat-induced obesity, we performed a genome-wide association meta-analysis of BFM, after adjustment by body lean mass, in the European population. Methods Three samples of European ancestry were included into the meta-analysis: the Framingham heart study (FHS, N=6,004), the Kansas-city osteoporosis study (KCOS, N=2,207) and the Omaha osteoporosis study (OOS, N=968). Results At genome-wide significance level (α=5.0×10−8), we identified a cluster of 10 SNPs at chromosomal region 20p11 that were associated with BFM (lead SNP rs2069126 p=1.82×10−9, closest gene SLC24A3) in 9,179 subjects. One of the top SNPs rs6046308 (p=3.74×10−8) is nominally significant for body fat percentage in another independent study (p=0.03, N=75,888), and is reported to trans-regulate the expression of MPZ gene at 1q23.3 (unadjusted p=9.78×10−6, N=1,490). Differential gene expression analysis demonstrates that SLC24A3 and CFAP61 at the identified locus are differentially expressed in tissues of people with versus without obesity (p=3.40×10−5 and 8.72×10−4, N=126 and 70), implying their potential role in fat development. Conclusions Our results may provide new insights into the biological mechanism that underlies fat-induced obesity pathology.

Aiming to identify genomic variants associated with osteoporosis, we performed a genome-wide association meta-analysis of bone mineral density (BMD) at Ward's triangle of the hip in 7,175 subjects from 6 samples. We performed in silico replications with femoral neck, trochanter, and inter-trochanter BMDs in 6,912 subjects from the Framingham heart study (FHS), and with forearm, femoral neck and lumbar spine BMDs in 32,965 subjects from the GEFOS summary results. Combining the evidence from all samples, we identified 2 novel loci for areal BMD: 1q43 (rs1414660, discovery p=1.20×10−8, FHS p=0.05 for trochanter BMD; rs9287237, discovery p=3.55×10−7, FHS p=9.20×10−3 for trochanter BMD, GEFOS p=0.02 for forearm BMD, nearest gene FMN2) and 2q32.2 (rs56346965, discovery p=7.48×10−7, FHS p=0.10 for inter-trochanter BMD, GEFOS p=0.02 for spine BMD, nearest gene NAB1). The two lead SNPs rs1414660 and rs56346965 are eQTL sites for the genes GREM2 and NAB1 respectively. Functional annotation of GREM2 and NAB1 illustrated their involvement in BMP signaling pathway and in bone development. We also replicated three previously reported loci: 5q14.3 (rs10037512, discovery p=3.09×10−6, FHS p=8.50×10−3, GEFOS p=1.23×10−24 for femoral neck BMD, nearest gene MEF2C), 6q25.1 (rs3020340, discovery p=1.64×10−6, GEFOS p=1.69×10−3 for SPN-BMD, nearest gene ESR1) and 7q21.3 (rs13310130, discovery p=8.79×10−7, GEFOS p=2.61×10−7 for spine BMD, nearest gene SHFM1). Our findings provide additional insights that further enhance our understanding of bone development, osteoporosis, and fracture pathogenesis.

BackgroundLow lean body mass is the most important predictor of sarcopenia with strong genetic background. The aim of this study was to uncover genetic factors underlying lean mass development.Materials and methodsWe performed a genome-wide association study (GWAS) of fat-adjusted leg lean mass in the Framingham Heart Study (FHS, N = 6587), and replicated in the Women’s Health Initiative–African American sub-sample (WHI-AA, N = 847) and the Kansas City Osteoporosis Study (KCOS, N = 2219). We also cross-validated significant variants in the publicly available body mass index (BMI) summary results (N ~ 700,000). We then performed a series of functional investigations on the identified variants.ResultsFour correlated SNPs at 6p21.1 were identified at the genome-wide significance (GWS, α = 5.0 × 10−8) level in the discovery FHS sample (rs551145, rs524533, rs571770, and rs545970, p = 3.40–9.77 × 10−9), and were successfully replicated in both the WHI-AA and the KCOS samples (one-sided p = 1.61 × 10−3–0.04). They were further cross-validated by the large-scale BMI summary results (p = 7.0–9.8 × 10−3). Cis-eQTL analyses associated these SNPs with the NFKBIE gene expression. Electrophoresis mobility shift assay (EMSA) in mouse C2C12 myoblast cells implied that rs524533 and rs571770 were bound to an unknown transcription factor in an allelic specific manner, while rs551145 and rs545970 did not. Dual-luciferase reporter assay revealed that both rs524533 and rs571770 downregulated luciferase expression by repressing promoter activity. Moreover, the regulation pattern was allelic specific, strengthening the evidence towards their differential regulatory effects.ConclusionsThrough a large-scale GWAS followed by a series of functional investigations, we identified 2 correlated functional variants at 6p21.1 associated with leg lean mass. Our findings not only enhanced our understanding of molecular basis of lean mass development but also provided useful candidate genes for further functional studies.