Genetics. Published Articles Ahead of Print: July 29, 2007, Copyright © 2007
doi:10.1534/genetics.107.075614


A more recent version of this article appeared on September 1, 2007.

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GRAMMAR: a fast and simple method for genome-wide pedigree-based quantitative trait loci association analysis

Yurii S Aulchenko 1*, Dirk-Jan De Koning 2 and Chris Haley 2

1 Erasmus MC
2 Roslin Institute

* To whom correspondence should be addressed. E-mail: i.aoultchenko{at}erasmusmc.nl.

Submitted on May 7, 2007
Revised on June 8, 2007
Accepted on 2 July 2007


Abstract

For pedigree-based quantitative trait loci (QTL) association analysis, a range of methods utilizing within-family variation (TDT-based methods) has been developed. In scenarios where stratification is not a concern, methods exploiting between-family variation in addition to within-family variation, such as the measured genotype (MG) approach, have greater power. Application of MG methods can be computationally demanding (especially for large pedigrees), making genome-wide scans practically infeasible. Here, we suggest a novel approach for genome-wide pedigree-based QTL association analysis, GRAMMAR (Genome-wide Rapid Association using Mixed Model And Regression). The method first obtains residuals adjusted for family effects and subsequently analyses the association between these residuals and genetic polymorphisms using rapid least squares methods. At the final step, the selected polymorphisms may be followed up with the full MG analysis. In a simulation study, we compared type 1 error, power and operational characteristics of the proposed method with these of MG and TDT-based approaches. For moderately heritable (30%) traits in human pedigrees the power of the GRAMMAR and MG approaches is similar and is much higher than that of TDT-based approaches. When using tabulated thresholds, the proposed method is less powerful than MG for very high heritabilities and pedigrees including large sibships like those observed in livestock pedigrees. However, there is little or no difference in empirical power of MG and the proposed method. In any scenario, GRAMMAR is much faster than MG and enables rapid analysis of hundreds of thousands of markers.

Key Words: Quantitative Trait Loci (QTL), complex pedigrees, genome-wide association (GWA) analysis, regression, variance components