Impact of the diagnosis definition on linkage detection

Previous genome scan linkage analyses of the disease Kofendrerd Personality Disorder (KPD) with microsatellites led to detect some regions on chromosomes 1, 3, 5, and 9 that were identical for the three populations AI, KA, and DA but with large differences in significance levels. These differences in results may be explained by the different diagnosis definitions depending on the presence/absence of 12 traits that were used in the 3 populations AI, KA, and DA. Heterogeneity of linkage was thus investigated here according to the absence/presence of each of the 12 traits in the 3 populations. For this purpose, two methods, the triangle test statistic and the predivided sample test were applied to search for genetic heterogeneity. Three regions with a strong heterogeneity of linkage were detected: the region on chromosome 1 according to the presence/absence of the traits a and b, the region on chromosome 3 for the trait b, and the region on chromosome 9 for the traits k and l. These 3 regions were the same as those detected by linkage analyses. No novel region was detected by the heterogeneity tests. Concerning chromosome 1, linkage analyses showed a much stronger evidence of linkage for traits a and b and for a combination of these traits than for KPD. Moreover, there was no indication of linkage to any of the other traits used to define the diagnosis of KPD. A genetic factor located on the chromosome 1 may have been detected here which would be involved specifically in traits a and b or in a combination of these traits

On the choice of linkage statistics

Three LOD score statistics are often used for genome-wide linkage analysis: the maximum LOD score, the LOD score statistic proposed by Kong and Cox, both based on the allele-sharing between affected sib pairs, and the maximization of the LOD score function of Morton on two genetic models and an heterogeneity parameter

Modeling the effect of a genetic factor for a complex trait in a simulated population

Genetic Analysis Workshop 14 simulated data have been analyzed with MASC(marker association segregation chi-squares) in which we implemented a bootstrap procedure to provide the variation intervals of parameter estimates. We model here the effect of a genetic factor, S, for Kofendrerd Personality Disorder in the region of the marker C03R0281 for the Aipotu population. The goodness of fit of several genetic models with two alleles for one locus has been tested. The data are not compatible with a direct effect of a single-nucleotide polymorphism (SNP) (SNP 16, 17, 18, 19 of pack 153) in the region. Therefore, we can conclude that the functional polymorphism has not been typed and is in linkage disequilibrium with the four studied SNPs. We obtained very large variation intervals both of the disease allele frequency and the degree of dominance. The uncertainty of the model parameters can be explained first, by the method used, which models marginal effects when the disease is due to complex interactions, second, by the presence of different sub-criteria used for the diagnosis that are not determined by S in the same way, and third, by the fact that the segregation of the disease in the families was not taken into account. However, we could not find any model that could explain the familial segregation of the trait, namely the higher proportion of affected parents than affected sibs

An ordered subset approach to including covariates in the transmission disequilibrium test

Clinical heterogeneity of a disease may reflect an underlying genetic heterogeneity, which may hinder the detection of trait loci. Consequently, many statistical methods have been developed that allow for the detection of linkage and/or association signals in the presence of heterogeneity

Genetic Analysis Workshop 15: gene expression analysis and approaches to detecting multiple functional loci

National Institutes of Health (AR44422, N01-AR-7-2232, 5R01-HL049609-14, IR01-AG021917-01A1); Genome Canada and Associations AFP; Polyarctique-Groupe Taitbout; Rhumatisme et Travail; Arthritis Research Campaign; Unversity of Minnesota; Minnesota Supercomputing Institute; National Institute of General Medical Sciences (R01 GM31575

Rare and low frequency variant stratification in the UK population: description and impact on association tests.

Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests

Detection of susceptibility loci by genome-wide linkage analysis.

The objective of this study is to evaluate the efficacy of a model-free linkage statistics for finding evidence of linkage using two different maps and to illustrate how the comparison of results from several populations might provide insight into the underlying genetic etiology of the disease of interest. The results obtained in terms of detection of the risk loci and threshold for declaring linkage and power are very similar for a dense SNP map and a sparser microsatellite map. The populations differed in terms of family ascertainment and diagnosis criteria, leading to different power to detect the individual underlying disease loci. Our results for the individual replicates are consistent with the disease model used in the simulation

FSuite: exploiting inbreeding in dense SNP chip and exome data

International audienceFSuite is a user friendly pipeline developed for exploiting inbreeding information derived from human genomic data. It can make use of SNP chip or exome data. Compared to other software, the advantage of FSuite is to provide a complete suite of scripts to describe and use the inbreeding information. It includes a module to detect inbred individuals and estimate their inbreeding coefficient, a module to describe the proportion of different mating types in the population and the individual probability to be offspring of different mating types that can be useful for population genetic studies. It also allows the identification of shared regions of homozygosity between affected individuals (homozygosity mapping) that can be used to identify rare recessive mutations involved in monogenic or multifac-torial diseases

Rare and Low Frequency Variant Stratification in the UK Population: Description and Impact on Association Tests

Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests