Multipoint development of the weighted pairwise correlation (WPC) linkage method for pedigrees of arbitrary size and application to the analysis of breast cancer and alcoholism familial data.

The weighted pairwise correlation (WPC) method is a simple and powerful model-free method of linkage analysis that has the advantages of being applicable to binary, ordered categorical, quantitative, or censored traits, and to consider all pairs of relatives in large pedigrees. The originally implemented approach was limited to the use of the identical by state (IBS) information, and we recently extended the WPC method to incorporate the identical by descent (IBD) information for two-point linkage analysis. Here, we develop a multipoint WPC method suitable for pedigrees of arbitrary size and large number of markers. The multipoint IBD estimation procedure for relative pairs is based on the efficient regression approach developed for pedigrees implemented in SOLAR. A robust and fast Monte-Carlo procedure is used to determine reliable P values. Application of the method to the 214 pedigrees from the Breast Cancer Linkage Consortium provided for the Genetic Analysis Workshop (GAW) 9 shows that multipoint WPC statistic values were not far from two-point maximum lod-score values obtained by the classical parametric linkage method and were higher than multipoint variance component analysis lod-scores obtained with SOLAR. The multipoint WPC method is also used to analyze the familial Collaborative Study of the Genetics of Alcoholism data on alcoholism released for GAW11. It allows a better specification of the linkage results previously obtained within the chromosome 4 region.

Genome search for additional human loci controlling infection levels by Schistosoma mansoni.

Schistosomiasis is a major public health problem in many developing countries. Previous studies have shown that infection levels by Schistosoma mansoni in a Brazilian population is controlled by a major gene, denoted as SM1, which was mapped to chromosome 5q31-q33 by use of a model-based (logarithm of the odds [lod] score) analysis method. The present study is an autosome-wide scan searching for additional human loci implicated in the regulation of S. mansoni infection intensities. The weighted pairwise correlation model-free linkage method was used in order to consider large pedigrees and to conduct a 2-locus analysis (i.e., to search for a second locus taking into account linkage to 5q31-q33). The most significant linkage results were again obtained in the 5q31-q33 region. Two additional regions provided linkage results with significance levels around 0.001, 1p21-q23 (results independent of 5q31-q33) and 6p21-q21 (results in interaction with 5q31-q33). The investigation of these regions, which contain some candidate genes, is ongoing in other populations to confirm the role of these regions.

Genome search for alcohol dependence using the weighted pairwise correlation linkage method: interesting findings on chromosome 4.

The nonparametric (model-free) method of linkage analysis Weighted Pairwise Correlation (WPC) has been proposed by Commenges [1994], and extended to incorporate identity-by-descent (IBD) information [Zinn-Justin and Abel, 1999]. We performed an autosome-wide scan in the Collaborative Study on the Genetics of Alcoholism data using the WPC-IBD method, considering two phenotypes related to alcohol dependence defined as residuals of binary traits adjusted for age and sex. Three chromosome 4 markers located in a region of 50 cM spanning from GABRB1 to D4S1651 provided Monte Carlo (MC) p-values lower than 0.005, confirming the possible influence of beta 1 GABA receptor and ADH genes in alcoholism. Furthermore, marker D15S642, not far from the ALDH6 gene, provided an MC p-value of 0.0005 in ethnic groups "White, Hispanic" and "White, non-Hispanic."

Introduction of the IBD information into the weighted pairwise correlation method for linkage analysis.

The Weighted Pairwise Correlation (WPC) approach is a non-parametric method of linkage analysis that allows analysis of any kind of phenotypes (quantitative, binary, binary with age of onset) and to consider all pairs of relatives in a pedigree. The principle of this method is to test whether two relatives having close phenotypes also resemble at the marker locus more than expected under the null hypothesis of no linkage. So far, this marker resemblance was estimated by the proportion of alleles shared Identical By State (IBS) by the two relatives. Here, we propose a method to incorporate the Identical By Descent (IBD) information into the WPC approach. For any kind of relative pairs, the computation of the proportion of alleles shared IBD is based on the identification of the closest couple of ancestors, denoted as the reference couple. The IBD information is obtained for pairs of relatives having the same reference couple using individual genotypic vectors derived from this couple. This reconstruction of the IBD information is performed rapidly even in large pedigrees. Simulation studies conducted under various genetic models demonstrate that the use of IBD instead of IBS information leads to a large increase of power, especially in the situation of poorly informative markers.

Two-locus developments of the weighted pairwise correlation method for linkage analysis.

Different studies of complex traits assumed to be influenced by two unliked loci found that two-locus linkage analysis is more powerful than the classical one-locus strategy. The Weighted Pairwise Correlation (WPC) approach is a nonparametric method for linkage analysis that has the advantage to analyze any kind of phenotypes and to consider extended pairs of relatives. In this report, we propose different two-locus extensions of the WPC method based on an additive or a multiplicative effect of two unlinked marker loci on the phenotype. Both methods and their corresponding statistics are easily derived from the classical WPC approach. Compared to the additive model, the multiplicative approach, which can be understood as a statistical interaction effect of the two markers, does not need to specify any additional parameter and allows one to test both the global effect of the two markers (T(AB)test) and the effect of one marker, e.g., B, taking into account the effect of the other, A (T(AB/A) test). When compared to classical one-locus tests by means of simulations, two-locus tests have comparable 0.05 type I error and are more powerful. In particular, tests based on the multiplicative approach appear to be quite interesting in addition to single locus tests to detect the combined role of two markers (T(AB)), or to investigate the role of a marker taking into account a known linked marker (T(AB/A)), especially when these markers have complex effects on the phenotype (e.g., statistical interaction).