Rare and low-frequency coding variants alter human adult height

Human height is a highly heritable, polygenic trait1,2. The contribution of common DNA sequence variation to inter-individual differences in adult height has been systematically evaluated through genome-wide association studies (GWAS). This approach has thus far identified 697 independent variants located within 423 loci that together explain around 20% of the heritability of height3. As is typical of complex traits and diseases, most of the alleles that affect height that have been discovered so far are common (with a minor allele frequency (MAF) > 5%) and are mainly located outside coding regions, complicating the identification of the relevant genes or functional variants. Identifying coding variants associated with a complex trait in new or known loci has the potential to help pinpoint causal genes. Furthermore, the extent to which rare (MAF < 1%) and low-frequency (1% < MAF ≤ 5%) coding variants also influence complex traits and diseases remains an open question. Many recent DNA sequencing studies have identified only a few of these variants4–8, but this limited success could be due to their modest sample size9. Some studies have suggested that common sequence variants may explain the majority of the heritable variation in adult height10. It is therefore timely to assess whether and to what extent rare and low-frequency coding variations contribute to the genetic landscape of this model polygenic trait. In this study, we used an ExomeChip11 to test the association between 241,453 variants (of which 83% are coding variants with a MAF ≤ 5%) and adult height variation in 711,428 individuals (discovery and validation sample sizes were 458,927 and 252,501, respectively). The ExomeChip is a genotyping array designed to query in very large sample sizes coding variants identified by whole-exome DNA sequencing of approximately 12,000 participants. The main goals of our project were to determine whether rare and low-frequency coding variants influence the architecture of a model complex human trait (in this case, adult height) and to discover and characterize new genes and biological pathways implicated in human growth.