Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs

Abstract

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 × 10(-)(7)) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 × 10(-10)) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average.

Figure 1

Q-Q Plots of Linkage Analyses For each cohort (left five panels), as well as the combined data set and the p values from the meta-analysis (the two right-most panels), Q-Q plots were produced for both BMI (top) and height (bottom) to demonstrate overall genomic inflation as being a departure from the expectation (x = y line). 95% confidence intervals are shaded in gray, and GC values for each cell represent genomic inflation. Markers are pruned to be 20 cM apart for reducing correlations between tests (for Q-Q plots on all markers, see Figure S12). From left to right, cohorts are TwinsUK, Netherlands Twin Registry, TWINGENE, Framingham, and QIMR. The sixth panel (n = 20,240) shows the results from the combined analysis, and the seventh panel represents the meta-analysis of all five cohorts.

Figure 2

Proportion of Markers with Positive Heritability Estimates $P\left(h_m^2>0\right)$ For each cohort (five bars to the left) and for the combined sample set (bar furthest to the right), an independent permutation analysis was performed such that the entire family of tests was rerun with QISP phenotypes randomly relabeled from QISP genomic IBD scores. Thus, each box-and-whisker plot represents the distribution of $P\left(h_m^2>0\right)$ values from the 100 permutations per cohort; red points represent the $P\left(h_m^2>0\right)$ achieved in the true linkage scans.

Figure 3

Simulations of Genetic Effect Models Expected results for various polygenic models are compared to observed results from true linkage analyses. Maximum LOD scores (y axis) from linkage scans are plotted against $P\left(h_m^2>0\right)$ (x axis). Rows of panels correspond to sample sizes, representing different cohort sizes (top five rows) and the combined data set (bottom row). Columns of panels correspond to simulated h²; the left column represents BMI, and the right column represents height. Each model × sample size × heritability combination was replicated 100 times, and error bars represent 95% confidence intervals. Genetic models are as follows: Null, no genetic effects; C1, oligogenic where a single common QTL exists per chromosome; C2, polygenic with common SNPs uniformly distributed throughout the genome; C3, polygenic with common SNPs clustering across the genome; R1, polygenic with rare SNPs uniformly distributed throughout the genome; and R2, polygenic with rare SNPs clustering across the genome. It is shown that different polygenic architectures have nearly identical properties in linkage analysis and that they are consistent with the results for BMI and height.

Figure 4

Linkage Analysis for BMI and Height LOD scores for BMI across all autosomes are shown on the top row, and height is shown on the bottom row. The traditional threshold of LOD = 3.3 is depicted by solid lines, and the more stringent empirical threshold based on simulations of polygenic models is shown by dotted lines (α = 0.05, see Figure S16). Blue points represent GWAS hits from the GIANT study (different shades of blue correspond to different log₁₀ p values). There is no correlation between linkage signals and GWAS signals (Figure S18).