Hundreds of variants clustered in genomic loci and biological pathways affect human height

Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence phenotype. Genome-wide association (GWA) studies have identified >600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the utility of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P=0.016), and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants, and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented amongst variants that alter amino acid structure of proteins and expression levels of nearby genes. Our data explain ∼10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ∼16% of phenotypic variation (∼20% of heritable variation). Although additional approaches are needed to fully dissect the genetic architecture of polygenic human traits, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

“…We also examined the associations of the IGF‐I‐ and IGFBP‐3‐associated SNPs with anthropometric traits (height, BMI, waist‐to‐hip ratio, and fat percentage) (Heid et al ., 2010; Lango Allen et al ., 2010; Speliotes et al ., 2010), bone mineral density (Estrada et al ., 2012), risk of type 2 diabetes (Voight et al ., 2010; Morris et al ., 2012) and related traits (fasting glucose, 2‐h glucose, HbA1c, fasting insulin, proinsulin, HOMA‐IR, and HOMA‐B) (Dupuis et al ., 2010; Saxena et al ., 2010; Soranzo et al ., 2010), and coronary artery disease (Coronary Artery Disease Genetics C, 2011; Schunkert et al ., 2011; Consortium CAD and Deloukas, 2013) (Table S9, Supporting information). Many nominal associations were expected because of the known influence of the IGF system on these traits.…”

“…Top SNPs associated with levels of IGF‐I and IGFBP‐3 were examined in relationship to other phenotypes using published data on serum metabolites (Suhre et al ., 2011; Shin et al ., 2014), anthropometric traits (Heid et al ., 2010; Lango Allen et al ., 2010; Speliotes et al ., 2010), bone mineral density (Estrada et al ., 2012), diabetes (Voight et al ., 2010; Morris et al ., 2012) and glycemic traits (Dupuis et al ., 2010; Saxena et al ., 2010; Soranzo et al ., 2010), coronary artery disease (Coronary Artery Disease Genetics C, 2011; Schunkert et al ., 2011; Consortium CAD and Deloukas, 2013), and survival beyond 90 years (Broer et al ., 2015). Detailed information of the published datasets used including its references is given in Table S9 (Supporting information).…”

Genetic architecture of body size in mammals

“…Shown are the allele effect size (x-axis) and frequency (y-axis) for GWAS results from Lango Allen et al [7] and for the sample of mutations described in Table 1. Assumptions for frequencies and effect of mutations from Table 1 are noted in the table footer.…”

“…Lango Allen et al [7] discovered 180 loci for height in humans that were estimated to explain about 10% of the phenotypic variance. When they allowed for the lack of power of their experiment, they concluded there were 700 loci associated with height but these would still only explain 16% of the variance.…”

Joint association analysis of a binary and a quantitative trait in family samples

“…Three SNVs reach the GWAS significance threshold of 5 × 10 − 8 , including the top 2 SNVs from chromosome 4, near the height-associated gene HHIP. 23 The chromosome 4-associated SNVs are also near TMEM154, a T2D-associated gene identified by the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium in 2014. 24 Among the remaining top 20 SNVs, chromosome 16 SNVs (rs8059849, rs9931529, rs13332434, rs9783765) are near FTO, a gene known for its association with both BMI and T2D.…”