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. 2015 Dec 2:5:17267.
doi: 10.1038/srep17267.

Quantifying the heritability of glioma using genome-wide complex trait analysis

Ben Kinnersley  1 , Jonathan S Mitchell  1 , Konstantinos Gousias  2 , Johannes Schramm  2 , Ahmed Idbaih  3   4 , Marianne Labussière  3 , Yannick Marie  3 , Amithys Rahimian  3   5 , H-Erich Wichmann  6   7   8 , Stefan Schreiber  9   10 , Khe Hoang-Xuan  3   4   5 , Jean-Yves Delattre  3   4   5 , Markus M Nöthen  11 , Karima Mokhtari  12 , Mark Lathrop  13   14 , Melissa Bondy  15 , Matthias Simon  2 , Marc Sanson  3   4   5 , Richard S Houlston  1
Affiliations

Quantifying the heritability of glioma using genome-wide complex trait analysis

Ben Kinnersley et al. Sci Rep. .

Abstract

Genome-wide association studies (GWAS) have successfully identified a number of common single-nucleotide polymorphisms (SNPs) influencing glioma risk. While these SNPs only explain a small proportion of the genetic risk it is unclear how much is left to be detected by other, yet to be identified, common SNPs. Therefore, we applied Genome-Wide Complex Trait Analysis (GCTA) to three GWAS datasets totalling 3,373 cases and 4,571 controls and performed a meta-analysis to estimate the heritability of glioma. Our results identify heritability estimates of 25% (95% CI: 20-31%, P = 1.15 × 10(-17)) for all forms of glioma - 26% (95% CI: 17-35%, P = 1.05 × 10(-8)) for glioblastoma multiforme (GBM) and 25% (95% CI: 17-32%, P = 1.26 × 10(-10)) for non-GBM tumors. This is a substantial increase from the genetic variance identified by the currently identified GWAS risk loci (~6% of common heritability), indicating that most of the heritable risk attributable to common genetic variants remains to be identified.

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Figures

Figure 1
Figure 1. Estimate of the variance explained by each chromosome in the combined dataset as a function of chromosome size.
The regression R2 was 0.29 (P = 0.010).
See this image and copyright information in PMC

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