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

Hemani G, Yang J, Vinkhuyzen A, Powell JE, Willemsen G, Hottenga J, Abdellaoui A, Mangino M, Valdes AM, Medland SE, Madden PA, Heath AC, Henders AK, Nyholt DR, de Geus EJC, Magnusson PKE, Ingelsson E, Montgomery GW, Spector TD, Boomsma DI, Pedersen NL, Martin NG, Visscher PM

Am. J. Hum. Genet. 93 (5) 865-875 [2013-11-07; online 2013-10-31]

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.

NGI Uppsala (SNP&SEQ Technology Platform) [Service]

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PubMed 24183453

DOI 10.1016/j.ajhg.2013.10.005

Crossref 10.1016/j.ajhg.2013.10.005

S0002-9297(13)00462-X

pmc PMC3965855