A powerful likelihood method for the analysis of linkage disequilibrium between trait loci and one or more polymorphic marker loci (original) (raw)
Related papers
High resolution mapping of quantitative trait loci by linkage disequilibrium analysis
European Journal of Human Genetics, 2002
Two methods, linkage analysis and linkage disequilibrium (LD) mapping or association study, are usually utilised for mapping quantitative trait loci (QTL). Linkage mapping is appropriate for low resolution mapping to localise trait loci to broad chromosome regions within a few cM (510 cM), and is based on family data. Linkage disequilibrium mapping, on the other hand, is useful in high resolution or fine mapping, and is based on both population and family data. Using only one marker, one may carry out single-point linkage analysis and linkage disequilibrium mapping. Using two or more markers, it is possible to flank the QTL by multipoint analysis. The development and thus availability of dense marker maps, such as single nucleotide polymorphisms (SNP) in human genome, presents a tremendous opportunity for multipoint fine mapping. In this article, we propose a regression approach of mapping QTL by linkage disequilibrium mapping based on population data. Assuming that two marker loci flank one quantitative trait locus, a two-point linear regression is proposed to analyse population data. We derive analytical formulas of parameter estimations, and non-centrality parameters of appropriate tests of genetic effects and linkage disequilibrium coefficients. The merit of the method is shown by the power calculation and comparison. The two-point regression model can capture much more linkage and linkage disequilibrium information than that derived when only one marker is used. For a complex disease with heritability h 2 50.15, a study with sample size of 250 can provide high power for QTL detection under moderate linkage disequilibria.
Mapping Quantitative Trait Loci Using Linkage Disequilibrium: Marker- versus Trait-based Methods
Behavior Genetics, 2005
Two approaches for mapping quantitative trait loci (QTL) using linkage disequilibrium at the population level were investigated. In the trait-based (TB) approach, the frequencies of marker alleles (or genotypes) are compared in individuals selected from the two tails of the trait distribution. The TB approach uses phenotypic information only in the selection step. In the marker-based (MB) approach, the quantitative trait values for the marker genotypes in the selected individuals are compared. The MB approach uses both the difference in marker allele (or genotype) frequencies and the phenotypic values of each marker genotype in the selected samples. We quantify the power of each approach and show that the power of the MB approach is greater than or equal to that of the TB approach. The advantage of the former is expected to increase with increasing number of traits phenotyped. Our accurate predictions obviate the need for elaborate simulation studies.
Heredity, 2013
An important question arises when mapping quantitative trait loci (QTLs) for genetically correlated traits: is the correlation due to pleiotropy (a single QTL affecting more than one trait) and/or close linkage (different QTLs that are physically close to each other and influence the traits)? In this article, we propose the Close Linkage versus Pleiotropism (CLIP) test, a fast, simple and powerful method to distinguish between these two situations. The CLIP test is based on the comparison of the square of the observed correlation between a combination of apparent effects at the marker level to the minimal value it can take under the pleiotropic assumption. A simulation study was performed to estimate the power and alpha risk of the CLIP test and compare it to a test that evaluated whether the confidence intervals of the two QTLs overlapped or not (CI test). On average, the CLIP test showed a higher power (68%) to detect close-linked QTLs than the CI test (43%) and a same alpha risk (4%).
Pedigree linkage disequilibrium mapping of quantitative trait loci
European Journal of Human Genetics, 2005
In this paper, we propose to use pedigrees of any size and any types of relatives in joint high-resolution linkage disequilibrium (LD) and linkage mapping of quantitative trait loci (QTL) by variance component models. Two or multiple markers can be simultaneously used in modeling association with the trait locus, instead of using one marker a time in the analysis. The proposed method can provide a unified result by using two or multiple markers in the modeling. This may avoid the complications of different results obtained from the separate analysis of marker by marker. The models simultaneously incorporate both linkage and LD information. The measures of LD are modeled by mean coefficients, and linkage information is modeled by variance-covariance matrix. Using analytical formulas to calculate the regression coefficients, the genetic effects are shown to be decomposed into additive and dominance components. The noncentrality parameter approximations of test statistics of LD are provided to make power calculations. Power and type I error rates are explored to investigate the merit of the proposed method by both the analytical formulas and simulations. Comparing with the association between-family and association within-family ('AbAw') approach of Fulker and Abecasis et al, it is evident that the method proposed in this article is more powerful. The method is applied to investigate the relation between polymorphisms in the angiotensin 1-converting enzyme (ACE) genes and circulating ACE levels, with a better result than that of the 'AbAw' approach. Moreover, two markers I/D and 4656(CT)3/2 can fully interpret association with the trait locus at a 0.01 significance level, which provides a unique result for the ACE data.
A new statistic for the analysis of association between trait and polymorphic marker loci
Mathematical Biosciences, 2000
Inference for detecting the existence of an association between a diallelic marker and a trait locus is based on the chi-squared statistic with one degree of freedom. For polymorphic markers with m alleles (m b 2), three approaches are mainly used in practice. First, one may use Pearson's chi-squared statistic with m À 1 degrees of freedom (d.f.) but this leads to a loss in test power. Second, one can select an allele to be the most associated and then collapse the other allele categories into a single class. This reduces in a biased way, the locus to a diallelic system. Third, one may use the Terwilliger [J.D. Terwilliger, Am. J. Hum. Genet. 56 (1995) 777] likelihood ratio statistic which has a non-standard unknown limiting probability distribution. In this paper, we propose a new statistic, L D , based on the second testing approach. We derive the asymptotic probability distribution of L D in an easy way. Simulation studies show that L D is more powerful than Pearson's chi-squared statistic with m À 1 d.f.
A comparison between methods for linkage disequilibrium fine mapping of quantitative trait loci
Genetical Research, 2004
We present a maximum likelihood method for mapping quantitative trait loci that uses linkage disequilibrium information from single and multiple markers. We made paired comparisons between analyses using a single marker, two markers and six markers. We also compared the method to single marker regression analysis under several scenarios using simulated data. In general, our method outperformed regression (smaller mean square error and confidence intervals of location estimate) for quantitative trait loci with dominance effects. In addition, the method provides estimates of the frequency and additive and dominance effects of the quantitative trait locus.
The American Journal of Human Genetics, 2000
There is a lot of confusion in the literature about the "differences" between "model-based" and "model-free" methods and about which approach is better suited for detection of the genes predisposing to complex multifactorial phenotypes. By starting from first principles, we demonstrate that the differences between the two approaches have more to do with study design than statistical analysis. When simple data structures are repeatedly ascertained, no assumptions about the genotype-phenotype relationship need to be made for the analysis to be powerful, since simple data structures admit only a small number of df. When more complicated and/or heterogeneous data structures are ascertained, however, the number of df in the underlying probability model is too large to have a powerful, truly "model-free" test. So-called "model-free" methods typically simplify the underlying probability model by implicitly assuming that, in some sense, all meioses connecting two affected individuals are informative for linkage with identical probability and that the affected individuals in a pedigree share as many disease-predisposing alleles as possible. By contrast, "model-based" methods add structure to the underlying parameter space by making assumptions about the genotype-phenotype relationship, making it possible to probabilistically assign disease-locus genotypes to all individuals in the data set on the basis of the observed phenotypes. In this study, we demonstrate the equivalence of these two approaches in a variety of situations and exploit this equivalence to develop more powerful and efficient likelihood-based analogues of "model-free" tests of linkage and/or linkage disequilibrium. Through the use of a "pseudomarker" locus to structure the space of observations, sib-pairs, triads, and singletons can be analyzed jointly, which will lead to tests that are more well-behaved, efficient, and powerful than traditional "model-free" tests such as the affected sib-pair, transmission/disequilibrium, haplotype relative risk, and case-control tests. Also described is an extension of this approach to large pedigrees, which, in practice, is equivalent to affected relative-pair analysis. The proposed methods are equally applicable to two-point and multipoint analysis (using complex-valued recombination fractions).
The American Journal of Human Genetics, 2006
Selective genotyping is used to increase efficiency in genetic association studies of quantitative traits by genotyping only those individuals who deviate from the population mean. However, selection distorts the conditional distribution of the trait given genotype, and such data sets are usually analyzed using case-control methods, quantitative analysis within selected groups, or a combination of both. We show that Hotelling's T 2 test, recently proposed for association studies of one or several tagging single-nucleotide polymorphisms in a prospective (i.e., trait given genotype) design, can also be applied to the retrospective (i.e., genotype given trait) selective-genotyping design, and we use simulation to demonstrate its improved power over existing methods.
Linkage disequilibrium fine mapping of quantitative trait loci: A simulation study
Genetics Selection Evolution, 2003
Recently, the use of linkage disequilibrium (LD) to locate genes which affect quantitative traits (QTL) has received an increasing interest, but the plausibility of fine mapping using linkage disequilibrium techniques for QTL has not been well studied. The main objectives of this work were to (1) measure the extent and pattern of LD between a putative QTL and nearby markers in finite populations and (2) investigate the usefulness of LD in fine mapping QTL in simulated populations using a dense map of multiallelic or biallelic marker loci. The test of association between a marker and QTL and the power of the test were calculated based on singlemarker regression analysis. The results show the presence of substantial linkage disequilibrium with closely linked marker loci after 100 to 200 generations of random mating. Although the power to test the association with a frequent QTL of large effect was satisfactory, the power was low for the QTL with a small effect and/or low frequency. More powerful, multi-locus methods may be required to map low frequent QTL with small genetic effects, as well as combining both linkage and linkage disequilibrium information. The results also showed that multiallelic markers are more useful than biallelic markers to detect linkage disequilibrium and association at an equal distance.