High resolution mapping of quantitative trait loci by linkage disequilibrium analysis

Fan, Ruzong; Xiong, Momiao

doi:10.1038/sj.ejhg.5200843

Cited by 16 publications

(41 citation statements)

References 21 publications

(17 reference statements)

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“…1,7,8 This article generalizes our previous work to extended multigeneration pedigrees of any sizes and any types of relatives. Intuitively, large pedigrees contain more linkage and LD information.…”

Section: Introductionmentioning

confidence: 93%

“…Such as in Appendix B, Fan and Xiong, 1 we can show that the coefficients of regression equation (1) are given by…”

Section: Introductionmentioning

confidence: 99%

“…1 However, population substructures may affect the results and produce false positives. To remedy this, variance component models are proposed to perform joint LD and linkage mapping of QTL using both population and pedigree data.…”

Section: Introductionmentioning

confidence: 99%

“…The notations of the log-likelihood are defined as follows. The mean component X i m is from the following regression equation in a similar way as that of our previous work: 1,7,8 …”

Section: Introductionmentioning

confidence: 99%

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Pedigree linkage disequilibrium mapping of quantitative trait loci

et al. 2004

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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.

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Section: Introductionmentioning

confidence: 93%

“…Such as in Appendix B, Fan and Xiong, 1 we can show that the coefficients of regression equation (1) are given by…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The notations of the log-likelihood are defined as follows. The mean component X i m is from the following regression equation in a similar way as that of our previous work: 1,7,8 …”

Section: Introductionmentioning

confidence: 99%

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Pedigree linkage disequilibrium mapping of quantitative trait loci

et al. 2004

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“…By extending SKAT and SKAT-O to perform meta-analysis, Lee et al (2013) developed meta-analysis SKAT and SKAT-O (MetaSKAT and MetaSKAT-O) to carry out meta-analysis for rare variants in multiple studies. Both SKAT and MetaSKAT are score tests based on mixed-effect models.The third type is tests based on fixed-effect models that include (1) traditional additive effect models that are well studied (Cordell and Clayton 2002;Fan and Xiong 2002;Fan et al 2006) and (2) functional regression models as shown in our previous research (Luo et al 2012;Fan et al 2013Fan et al , 2014Wang et al 2015). Note that functional regression models are fixed-effect models, which extend traditional population genetics models to analyze multiple genetic variants and can analyze rare variants, common variants, or combinations of the two.…”

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confidence: 99%

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

et al. 2015

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Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.KEYWORDS meta-analysis; rare variants; common variants; association mapping; quantitative trait loci; complex traits; functional data analysis M ETA-ANALYSIS is a statistical method to combine multiple studies for a unified analysis and it plays an important role in genetic studies (de Bakker et al. 2008;Zeggini and Ioannidis 2009;Cantor et al. 2010;Evangelou and Ioannidis 2013). One obvious advantage of meta-analysis is that the sample size is large (Liu et al. 2014). Therefore, metaanalysis should lead to more significant results. It is argued that most of the reported complex disease associations came from large-scale meta-analysis of genome-wide association studies (GWASs) (Zeggini and Ioannidis 2009;Evangelou and Ioannidis 2013;Liu et al. 2014). Therefore, there has been great interest in developing novel statistical methods to perform GWAS meta-analysis (Ioannidis et al. 2007; Hu et al. 2013;Liu et al. 2014). Meta-analysis combines studies with different study designs. The genotype data and covariates may vary from study to study. Moreover, the effects of genetic variants in different populations may not be the same, i.e., the heterogeneity (Tang and Lin 2014). Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed.The statistical methods for meta-analysis fall into two classes: (1) single genetic variant-based approaches and (2) gene-based variant analysis approaches. The single genetic variant approaches only use one genetic variant at a time and are usually based ...

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Bivariate combined linkage and association mapping of quantitative trait loci

Jung

Zhong

Lian

et al. 2008

Genetic Epidemiology

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In this paper, bivariate/multivariate variance component models are proposed for high-resolution combined linkage and association mapping of quantitative trait loci (QTL), based on combinations of pedigree and population data. Suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In the region, multiple markers such as single nucleotide polymorphisms are typed. Two regression models, "genotype effect model" and "additive effect model", are proposed to model the association between the markers and the trait locus. The linkage information, i.e., recombination fractions between the QTL and the markers, is modeled in the variance and covariance matrix. By analytical formulae, we show that the "genotype effect model" can be used to model the additive and dominant effects simultaneously; the "additive effect model" only takes care of additive effect. Based on the two models, F-test statistics are proposed to test association between the QTL and markers. By analytical power analysis, we show that bivariate models can be more powerful than univariate models. For moderate-sized samples, the proposed models lead to correct type I error rates; and so the models are reasonably robust. As a practical example, the method is applied to analyze the genetic inheritance of rheumatoid arthritis for the data of The North American Rheumatoid Arthritis Consortium, Problem 2, Genetic Analysis Workshop 15, which confirms the advantage of the proposed bivariate models.

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High resolution mapping of quantitative trait loci by linkage disequilibrium analysis

Cited by 16 publications

References 21 publications

Pedigree linkage disequilibrium mapping of quantitative trait loci

Pedigree linkage disequilibrium mapping of quantitative trait loci

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

Bivariate combined linkage and association mapping of quantitative trait loci

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