Abstract
Combining information from multiple SNPs may capture a greater amount of genetic variation than from the sum of individual SNP effects and help identifying missing heritability. Regions may capture variation from multiple common variants of small effect, multiple rare variants or a combination of both. We describe regional heritability mapping of human cognition. Measures of crystallised (g(c)) and fluid intelligence (g(f)) in late adulthood (64-79 years) were available for 1806 individuals genotyped for 549,692 autosomal single nucleotide polymorphisms (SNPs). The same individuals were tested at age 11, enabling us the rare opportunity to measure cognitive change across most of their lifespan. 547,750 SNPs ranked by position are divided into 10, 908 overlapping regions of 101 SNPs to estimate the genetic variance each region explains, an approach that resembles classical linkage methods. We also estimate the genetic variation explained by individual autosomes and by SNPs within genes. Empirical significance thresholds are estimated separately for each trait from whole genome scans of 500 permutated data sets. The 5% significance threshold for the likelihood ratio test of a single region ranged from 17-17.5 for the three traits. This is the equivalent to nominal significance under the expectation of a chi-squared distribution (between 1df and 0) of P <1.44x10(-5). These thresholds indicate that the distribution of the likelihood ratio test from this type of variance component analysis should be estimated empirically. Furthermore, we show that estimates of variation explained by these regions can be grossly overestimated. After applying permutation thresholds, a region for gf on chromosome 5 spanning the PRRC1 gene is significant at a genome-wide 10% empirical threshold. Analysis of gene methylation on the temporal cortex provides support for the association of PRRC1 and fluid intelligence (P = 0.004), and provides a prime candidate gene for high throughput sequencing of these uniquely informative cohorts.
| Original language | English |
|---|---|
| Article number | e81189 |
| Number of pages | 12 |
| Journal | PloS ONE |
| Volume | 9 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 12 Dec 2013 |
Bibliographical note
Copyright: © 2013 Tenesa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Funding: Genotyping of the ABC1936, LBC1921, and LBC1936 cohorts and the analyses conducted here were supported by the UK's Biotechnology and Biological Sciences Research Council (BBSRC). Phenotype collection in the Lothian Birth Cohort 1921 was supported by the BBSRC, The Royal Society, and The Chief Scientist Office of the Scottish Government. Phenotype collection in the Lothian Birth Cohort 1936 was supported by Research Into Ageing (continues as part of Age UK's The Disconnected Mind project). Phenotype collection in the Aberdeen Birth Cohort 1936 was supported by the BBSRC, the Wellcome Trust, and the Alzheimer's Research Trust. SJR, AR and AT are funded by the BBSRC through the Roslin Institute's strategic programme grant and project grant BB/K000195/1. The Brain data was provided by the Division of Aging Biology and the Division of Geriatrics and Clinical Gerontology (NIA) through the NIH GWAS Data Repository (dbGaP Accession Number: phs000249.v1.p1) and funded as part of the Intramural Research Program, NIA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Keywords
- genome-wide association
- missing heritability
- maximum-likelihood
- genetic-variation
- common SNPS
- schizophrenia
- mortality
- trait
- prediction
- disease