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Unlinked Markers (unlinked + marker)
Selected AbstractsA critical evaluation of genomic control methods for genetic association studiesGENETIC EPIDEMIOLOGY, Issue 4 2009Tony Dadd Abstract Population stratification is an important potential confounder of genetic case-control association studies. For replication studies, limited availability of samples may lead to imbalanced sampling from heterogeneous populations. Genomic control (GC) can be used to correct ,2 test statistics which are presumed to be inflated by a factor ,; this may be estimated by a summary ,2 value (,median or ,mean) from a set of unlinked markers. Many studies applying GC methods have used fewer than 50 unlinked markers and an important question is whether this can adequately correct for population stratification. We assess the behavior of GC methods in imbalanced case-control studies using simulation. SNPs are sampled from two subpopulations with intra-continental levels of FST (,0.005) and sampling schemata ranging from balanced to completely imbalanced between subpopulations. The sampling properties of ,median and ,mean are explored using 6,1,600 unlinked markers to estimate Type 1 error and power empirically. GC corrections based on the ,2 -distribution (GCmedian or GCmean) can be anti-conservative even when more than 100 single nucleotide polymorphisms (SNPs) are genotyped and realistic levels of population stratification exist. The GCF procedure performs well over a wider range of conditions, only becoming anti-conservative at low levels of , and with fewer than 25 SNPs genotyped. A substantial loss of power can arise when population stratification is present, but this is largely independent of the number of SNPs used. A literature survey shows that most studies applying GC have used GCmedian or GCmean, rather than GCF, which is the most appropriate GC correction method. Genet. Epidemiol. 2009. © 2008 Wiley Liss, Inc. [source] Centralizing the non-central chi-square: a new method to correct for population stratification in genetic case-control association studiesGENETIC EPIDEMIOLOGY, Issue 4 2006Prakash Gorroochurn Abstract We present a new method, the ,-centralization (DC) method, to correct for population stratification (PS) in case-control association studies. DC works well even when there is a lot of confounding due to PS. The latter causes overdispersion in the usual chi-square statistics which then have non-central chi-square distributions. Other methods approach the non-centrality indirectly, but we deal with it directly, by estimating the non-centrality parameter , itself. Specifically: (1) We define a quantity ,, a function of the relevant subpopulation parameters. We show that, for relatively large samples, , exactly predicts the elevation of the false positive rate due to PS, when there is no true association between marker genotype and disease. (This quantity , is quite different from Wright's FST and can be large even when FST is small.) (2) We show how to estimate ,, using a panel of unlinked "neutral" loci. (3) We then show that ,2 corresponds to , the non-centrality parameter of the chi-square distribution. Thus, we can centralize the chi-square using our estimate of ,; this is the DC method. (4) We demonstrate, via computer simulations, that DC works well with as few as 25,30 unlinked markers, where the markers are chosen to have allele frequencies reasonably close (within ±.1) to those at the test locus. (5) We compare DC with genomic control and show that where as the latter becomes overconservative when there is considerable confounding due to PS (i.e. when , is large), DC performs well for all values of ,. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source] Bayesian analyses of admixture in wild and domestic cats (Felis silvestris) using linked microsatellite lociMOLECULAR ECOLOGY, Issue 1 2006R. LECIS Abstract Methods recently developed to infer population structure and admixture mostly use individual genotypes described by unlinked neutral markers. However, Hardy,Weinberg and linkage disequilibria among independent markers decline rapidly with admixture time, and the admixture signals could be lost in a few generations. In this study, we aimed to describe genetic admixture in 182 European wild and domestic cats (Felis silvestris), which hybridize sporadically in Italy and extensively in Hungary. Cats were genotyped at 27 microsatellites, including 21 linked loci mapping on five distinct feline linkage groups. Genotypes were analysed with structure 2.1, a Bayesian procedure designed to model admixture linkage disequilibrium, which promises to assess efficiently older admixture events using tightly linked markers. Results showed that domestic and wild cats sampled in Italy were split into two distinct clusters with average proportions of membership Q > 0.90, congruent with prior morphological identifications. In contrast, free-living cats sampled in Hungary were assigned partly to the domestic and the wild cat clusters, with Q < 0.50. Admixture analyses of individual genotypes identified, respectively, 5/61 (8%), and 16,20/65 (25,31%) hybrids among the Italian wildcats and Hungarian free-living cats. Similar results were obtained in the past using unlinked loci, although the new linked markers identified additional admixed wildcats in Italy. Linkage analyses confirm that hybridization is limited in Italian, but widespread in Hungarian wildcats, a population that is threatened by cross-breeding with free-ranging domestic cats. The total panel of 27 loci performed better than the linked loci alone in the identification of domestic and known hybrid cats, suggesting that a large number of linked plus unlinked markers can improve the results of admixture analyses. Inferred recombination events led to identify the population of origin of chromosomal segments, suggesting that admixture mapping experiments can be designed also in wild populations. [source] INVITED REVIEW: Using genome scans of DNA polymorphism to infer adaptive population divergenceMOLECULAR ECOLOGY, Issue 3 2005JAY F. STORZ Abstract Elucidating the genetic basis of adaptive population divergence is a goal of central importance in evolutionary biology. In principle, it should be possible to identify chromosomal regions involved in adaptive divergence by screening genome-wide patterns of DNA polymorphism to detect the locus-specific signature of positive directional selection. In the case of spatially separated populations that inhabit different environments or sympatric populations that exploit different ecological niches, it is possible to identify loci that underlie divergently selected traits by comparing relative levels of differentiation among large numbers of unlinked markers. In this review I first address the question of whether diversifying selection on polygenic traits can be expected to produce predictable patterns of allelic variation at the underlying quantitative trait loci (QTL), and whether the locus-specific effects of selection can be reliably detected against the genome-wide backdrop of stochastic variability. I then review different approaches that have been developed to identify loci involved in adaptive population divergence and I discuss the relative merits of model-based approaches that rely on assumptions about population structure vs. model-free approaches that are based on empirical distributions of summary statistics. Finally, I consider the evolutionary and functional insights that might be gained by conducting genome scans for loci involved in adaptive population divergence. [source] | ||||||||||