Effectiveness of extreme discordant sib pairs to detect oligogenic disease loci

Statistical power of association using the extreme discordant phenotype design

BACKGROUND

Selective genotyping has been proven to be an effective design for mapping quantitative trait loci (QTL), either by linkage or by allelic association, wherein the individual trait values can be used as the indices for phenotype selection. It has also been proposed that association studies of dichotomous traits can benefit from such design. When there is no quantitative measurement for phenotype available, cases and/or controls having extreme discordant phenotypes (EDP) can still be selected, based on their exposure status to a drug toxicity or environmental risk factor. The advantage of EDP design is intuitive and it has been successfully used in a number of studies.

METHODS

In this report, we developed a statistical method to calculate the power of EDP methodology, using a mixture model of genotype-specific distributions of a single biallelic susceptibility locus. We also compared the power of three statistical tests commonly used in association studies - including the chi test of allelic frequencies, the chi test of genotype frequencies, and the Armitage trend test. The power of two different EDP designs was evaluated under a range of scenarios.

RESULTS AND CONCLUSION

Our results indicate that the chi test of genotype frequency is a robust, though less powerful, test for single-locus association, and that EDP methodology is a powerful design for genetic association studies - especially those of common diseases caused by quantifiable drug toxicity or environmental risk factors.

Recent advances in human quantitative-trait-locus mapping: comparison of methods for discordant sibling pairs

Extreme discordant sibling pairs (EDSPs) are theoretically powerful for the mapping of quantitative-trait loci (QTLs) in humans. EDSPs have not been used much in practice, however, because of the need to screen very large populations to find enough pairs that are extreme and discordant. Given appropriate statistical methods, another alternative is to use moderately discordant sibling pairs (MDSPs)--pairs that are discordant but not at the far extremes of the distribution. Such pairs can be powerful yet far easier to collect than extreme discordant pairs. Recent work on statistical methods for QTL mapping in humans has included a number of methods that, though not developed specifically for discordant pairs, may well be powerful for MDSPs and possibly even EDSPs. In the present article, we survey the new statistics and discuss their applicability to discordant pairs. We then use simulation to study the type I error and the power of various statistics for EDSPs and for MDSPs. We conclude that the best statistic(s) for discordant pairs (moderate or extreme) is (are) to be found among the new statistics. We suggest that the new statistics are appropriate for many other designs as well-and that, in fact, they open the way for the exploration of entirely novel designs.

Composite statistics for QTL mapping with moderately discordant sibling pairs

Extreme discordant sibling-pair (EDSP) designs have been shown in theory to be very powerful for mapping quantitative-trait loci (QTLs) in humans. However, their practical applicability has been somewhat limited by the need to phenotype very large populations to find enough pairs that are extremely discordant. In this paper, we demonstrate that there is also substantial power in pairs that are only moderately discordant, and that designs using moderately discordant pairs can yield a more practical balance between phenotyping and genotyping efforts. The power we demonstrate for moderately discordant pairs stems from a new statistical result. Statistical analysis in discordant-pair studies is generally done by testing for reduced identity by descent (IBD) sharing in the pairs. By contrast, the most commonly-used statistical methods for more standard QTL mapping are Haseman-Elston regression and variance-components analysis. Both of these use statistics that are functions of the trait values given IBD information for the pedigree. We show that IBD sharing statistics and "trait value given IBD" statistics contribute complementary rather than redundant information, and thus that statistics of the two types can be combined to form more powerful tests of linkage. We propose a simple composite statistic, and test it with simulation studies. The simulation results show that our composite statistic increases power only minimally for extremely discordant pairs. However, it boosts the power of moderately discordant pairs substantially and makes them a very practical alternative. Our composite statistic is straightforward to calculate with existing software; we give a practical example of its use by applying it to a Genetic Analysis Workshop (GAW) data set.

An extreme-sib-pair genome scan for genes regulating blood pressure

Hypertension, a risk factor for many cardiovascular, cerebrovascular, and renal diseases, affects one in four Americans, at an annual cost of>$30 billion. Although genetic mutations have been identified in rare forms of hypertension, including Liddle syndrome and glucocorticoid-remediable aldosteronism, the abundance of plausible candidate genes and potential environmental risk factors has complicated the genetic dissection of more prevalent essential hypertension. To search systematically for chromosomal regions containing genes that regulate blood pressure, we scanned the entire autosomal genome by using 367 polymorphic markers. Our study population, selected from a blood-pressure screen of >200,000 Chinese adults, comprises rare but highly efficient extreme sib pairs (207 discordant, 258 high concordant, and 99 low concordant) and all but a single parent of these sibs. By virtue of the sampling design, the number of sib pairs, and the availability of genotyped parents, this study represents one of the most powerful of its kind. Although no regions achieved a 5% genomewide significance level, maximum LOD-score values were >2.0 (unadjusted P<.001) for regions containing five markers (D3S2387, D11S2019, D15S657, D16S3396, and D17S1303), in our primary analysis. Other promising regions identified through secondary analyses include loci near D4S3248, D7S2195, D10S1423, D20S470, D20S482, D21S2052, PAH, and AGT.