Incorporating Covariates into Multipoint Association Mapping in the Case-Parent Design

Chiu, Yen Feng; Liang, Kung Yee; Pan, Wen Harn

doi:10.1159/000291986

Cited by 4 publications

(7 citation statements)

References 51 publications

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“…Theoretically, the genetic effect of τ , characterized by C , is the transmission probability that the affected offspring will carry the disease allele, H , at τ . Detailed derivations for case-parent trios in a cross-sectional design can be found in Chiu et al [2, 5]. We will present the details of this proposed methodology elsewhere.…”

Section: Methodsmentioning

confidence: 99%

Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes

Chiu¹,

Lee²,

Hsu

2016

BMC Proc

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It is essential to develop adequate statistical methods to fully utilize information from longitudinal family studies. We extend our previous multipoint linkage disequilibrium approach—simultaneously accounting for correlations between markers and repeat measurements within subjects, and the correlations between subjects in families—to detect loci relevant to disease through gene-based analysis. Estimates of disease loci and their genetic effects along with their 95 % confidence intervals (or significance levels) are reported. Four different phenotypes—ever having hypertension at 4 visits, incidence of hypertension, hypertension status at baseline only, and hypertension status at 4 visits—are studied using the proposed approach. The efficiency of estimates of disease locus positions (inverse of standard error) improves when using the phenotypes from 4 visits rather than using baseline only.

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Section: Methodsmentioning

confidence: 99%

Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes

Chiu¹,

Lee²,

Hsu

2016

BMC Proc

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“…For the trio design, it is not necessary to adjust for population stratification as long as it is independent from location of the disease locus. For a quantitative covariate, Z could be the difference between each case-control combination pair (Chiu et al, 2010a). The unrelated cases and controls can be either matched or unmatched.…”

Section: Case-control Designsmentioning

confidence: 99%

“…We applied parametric models (Chiu et al, 2008(Chiu et al, , 2010a in the association mapping, where C was modeled through logistic regression:…”

Section: Incorporating Covariatesmentioning

confidence: 99%

“…. , β p ), and C(z) could be the genetic effect from the combined study, or C Tr (z) and C CC (z) from the case-parent trios and casecontrols studies, respectively (Chiu et al, 2010a). For a trios design, [C(z) + 1]/2 represents the probability that an affected child receives the risk allele at τ from his or her heterozygous parent.…”

Section: Incorporating Covariatesmentioning

confidence: 99%

“…Therefore, a 95% confidence interval can also be estimated for the disease locus location and for its genetic effect. Chiu et al (2008Chiu et al ( , 2010a have extended this approach to incorporate covariates into the LD mapping, based on either trios or a case-control study. The significance of the covariate indicates that the genetic effect is associated with the covariate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Family‐ and Population‐Based Samples Using Multiple Linkage Disequilibrium Mapping

Chiu

Lee

Kao

et al. 2013

Annals of Human Genetics

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SummaryWe report two methods for linkage disequilibrium mapping that involve incorporation of covariates through parametric modeling to utilize combined case-parent trios and unrelated case and/or control data. The proposed two combined methods were used to map the disease locus of hypertension in the angiotensin-converting enzyme (ACE) gene with incorporation of ACE activity. The efficiencies in estimating the disease locus increased by 351-and 100-fold in the hybrid study with respect to the two proposed methods when compared to the estimates from the trios study; and they changed by 1.4-and 0.4-fold, respectively, when compared to the case-control study. Efficiency of disease locus estimates was greatly improved in both simulations and hypertension studies based on the hybrid data, compared to case-parent trio studies only. These newly developed methods preserve the advantages of the previous methods, including flexible modeling and assessment of gene-gene and gene-covariate effects, while providing more power by using all the data combined. The computing program for analysis using the separate and hybrid data sets is freely available on the author's website.

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SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

Fang

Chiu

2012

Genetic Epidemiology

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Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.

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Incorporating Covariates into Multipoint Association Mapping in the Case-Parent Design

Cited by 4 publications

References 51 publications

Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes

Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes

Analysis of Family‐ and Population‐Based Samples Using Multiple Linkage Disequilibrium Mapping

SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

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