Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture
The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF </= 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 x 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 x 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 x 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.
|ISBN||1476-4687 (Electronic) 0028-0836 (Linking)|
|Authors||Zheng, H. F.; Forgetta, V.; Hsu, Y. H.; Estrada, K.; Rosello-Diez, A.; Leo, P. J.; Dahia, C. L.; Park-Min, K. H.; Tobias, J. H.; Kooperberg, C.; Kleinman, A.; Styrkarsdottir, U.; Liu, C. T.; Uggla, C.; Evans, D. S.; Nielson, C. M.; Walter, K.; Pettersson-Kymmer, U.; McCarthy, S.; Eriksson, J.; Kwan, T.; Jhamai, M.; Trajanoska, K.; Memari, Y.; Min, J.; et al.|
|URL link to publisher's version||http://www.ncbi.nlm.nih.gov/pubmed/26367794|
|OpenAccess link to author's accepted manuscript version||https://publications.gimr.garvan.org.au/open-access/13358|