Genome partitioning of genetic variation for height from 11,214 sibling pairs

Abstract

Height has been used for more than a century as a model by which to understand quantitative genetic variation in humans. We report that the entire genome appears to contribute to its additive genetic variance. We used genotypes and phenotypes of 11,214 sibling pairs from three countries to partition additive genetic variance across the genome. Using genome scans to estimate the proportion of the genomes of each chromosome from siblings that were identical by descent, we estimated the heritability of height contributed by each of the 22 autosomes and the X chromosome. We show that additive genetic variance is spread across multiple chromosomes and that at least six chromosomes (i.e., 3, 4, 8, 15, 17, and 18) are responsible for the observed variation. Indeed, the data are not inconsistent with a uniform spread of trait loci throughout the genome. Our estimate of the variance explained by a chromosome is correlated with the number of times suggestive or significant linkage with height has been reported for that chromosome. Variance due to dominance was not significant but was difficult to assess because of the high sampling correlation between additive and dominance components. Results were consistent with the absence of any large between-chromosome epistatic effects. Notwithstanding the proposed architecture of complex traits that involves widespread gene-gene and gene-environment interactions, our results suggest that variation in height in humans can be explained by many loci distributed over all autosomes, with an additive mode of gene action.

Figure 1.

Estimate of chromosomal heritability from a joint analysis of all 22 autosomes (labeled on graph) as a function of the genetic length of the chromosome. The figure shows that there is a relationship between the genetic length of a chromosome and the amount of variance it explains. P<.0001 for the regression coefficient from a weighted least-squares analysis if no intercept is fitted. P=.683 if an intercept is fitted after the regression.

Figure 2.

Relationship between heritability estimates from the AU and US data sets. There is a highly significant correlation between the estimates of chromosomal heritability from the two data sets (no-intercept model, P<.0001; model with intercept, P=.009). The relationship remains significant when conditioning on the length of the chromosome (partial correlation 0.540; P=.011, two-sided).