QTL mapping with discordant and concordant sibling pairs: new statistics and new design strategies

Extreme Rank Selections for Linkage Analysis of Quantitative Trait Loci Using Selected Sib-Pairs

It is well known that linkage analysis using simple random sib-pairs has relatively low power for detecting quantitative trait loci with small genetic effects. The power can be substantially increased by using samples selected based on their trait values. Usually, samples that are obtained by truncation selection consist of random samples from a truncated trait distribution. In this article we propose an alternative method using extreme ranks for linkage analysis with selected sib-pairs. This approach approximates the truncation selection. With similar screening sizes and the same sample size of selected sib-pairs, the extreme rank selection and truncation method have similar power performance, both of which are substantially more powerful than when using random sib-pairs. Simulation results on the comparison of powers between the truncation selection and the extreme rank selection and/or random selection for linkage analysis are reported.

Identification of distinct quantitative trait Loci affecting length or weight variability at birth in humans

CONTEXT

The variability of human fetal growth is multifactorial. Twin and family studies demonstrate that genetic determinants influence normal fetal growth, but the responsible genetic polymorphisms are unknown.

OBJECTIVE

The objective of the study was the mapping of quantitative trait loci (QTLs) for birth length and weight.

DESIGN AND METHODS

To approach the genetic factors implicated in the normal variation of birth length and weight, we conducted a genome-wide approach of these two quantitative traits in 220 French Caucasian pedigrees (412 sibling pairs) using a variance components method.

RESULTS

We observed evidence for several QTLs influencing birth length or birth weight independently. Whereas birth length and weight showed a close correlation (r = 0.76, P < 0.0001), their genetic variability appeared largely determined by distinct genomic loci. Birth length was influenced by two major QTLs located in 2p21 and 2q11 (LOD scores 2.69 and 3.57). The variability of birth weight was linked to another QTL on 7q35 (LOD score 3.1). Several other regions showed more modest evidence for linkage with LOD score values of 1-2 on chromosomes 7, 8, 10, 13, and 17 for birth length and chromosomes 1, 2, 6, 8, 10, 13, 14, 15, 17, and 20 for birth weight.

CONCLUSION

These preliminary QTLs provide a first step toward the identification of the genomic variants involved in the variability of human fetal growth. Our results should, however, be considered preliminary until they are replicated in other studies.

Incorporating endophenotypes into allele-sharing based linkage tests

For a genetic study in which there are concordant and discordant sibpairs for a complex disease trait and the measurements of other endophenotypes/intermediate phenotypes for each of the individuals are also available, we describe an allele-sharing based multipoint linkage test that utilizes nonparametrically the additional endophenotypes/intermediate phenotypes. The usefulness of this method is evaluated in simulation studies, which show that the gain in power is influenced by not only the endophenotypic value but also the correlation between the diagnosis-based phenotype and the endophenotype. In addition to reporting p values, our method also provides an index C(E), derived from the coefficients of the weight function associated with the endophenotype in the proposed statistic, to indicate the relevance of a specific endophenotype/intermediate phenotype in the genetic study. The simulation study indicates that a larger power, in general, corresponds to a larger value of the index C(E). The index C(E) is thus suggested as a quantity to be used in the choice of endophenotypes in linkage study. Data from the Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe) are used to illustrate the method.

A powerful and robust new linkage statistic for discordant sibling pairs

Previously, Szatkiewicz and colleagues evaluated the performance of a wide variety of statistics for quantitative-trait-locus linkage, using discordant sibling pairs. They found that the most powerful statistics, in general, were a score statistic and a "composite statistic." However, whereas these two statistics have equal power under ideal conditions, each has limitations that reduce its power in certain circumstances. The score statistic depends on estimates of trait parameters and can lose a lot of power if those estimates are incorrect. The composite statistic is not sensitive to trait-parameter estimates but does depend on arbitrary weights that must be chosen on the basis of the ascertainment scheme. In this report, we elucidate the algebraic relationship between the score and composite statistics and then use that relationship to suggest a new statistic that combines the best properties of both. We call our new statistic the "robust discordant pair" (RDP) statistic. We report simulation studies to show that the RDP statistic does, indeed, have all of the strengths and none of the weaknesses of the score and composite statistics.