A Ridge Penalized Principal-Components Approach Based on Heritability for High-Dimensional Data

Wang, Yuanjia; Fang, Yixin; Jin, Man

doi:10.1159/000102991

Cited by 22 publications

(28 citation statements)

References 19 publications

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“…Principal components analysis of heritability (Ott and Rabinowitz, 1999) can be used to extract the eigenvector explaining the most heritability (Lange et al, 2004; Wang et al, 2007; Klei et al, 2008). If the number of traits exceeds the number of individuals, as in a typical gene expression experiment, a ridge penalty can be added to prevent overfitting (Wang et al, 2007).…”

Section: Dimension Reductionmentioning

confidence: 99%

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Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies

Shriner¹

2012

Front. Gene.

186

220

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Association studies are a staple of genotype–phenotype mapping studies, whether they are based on single markers, haplotypes, candidate genes, genome-wide genotypes, or whole genome sequences. Although genetic epidemiological studies typically contain data collected on multiple traits which themselves are often correlated, most analyses have been performed on single traits. Here, I review several methods that have been developed to perform multiple trait analysis. These methods range from traditional multivariate models for systems of equations to recently developed graphical approaches based on network theory. The application of network theory to genetics is termed systems genetics and has the potential to address long-standing questions in genetics about complex processes such as coordinate regulation, homeostasis, and pleiotropy.

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Section: Dimension Reductionmentioning

confidence: 99%

“…If the number of traits exceeds the number of individuals, as in a typical gene expression experiment, a ridge penalty can be added to prevent overfitting (Wang et al, 2007). …”

Section: Dimension Reductionmentioning

confidence: 99%

Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies

Shriner¹

2012

Front. Gene.

186

220

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“…Then we obtain these PCs using the eigenvectors of the generalized eigen system [23]. Due to the high-dimensional and sparse matrices ( p ≫ N ), Wang et al proposed a ridge penalized principal components approach to obtaining the PCs of Heritability to accommodate large number of phenotypes [11]. In his approach one can use either the PCs from multivariate familial covariance matrix (for unstandardized data) or correlation matrix (for standardized data) as proposed by Bilodeau and Duchesne [22].…”

Section: Methodsmentioning

confidence: 99%

“…They showed that the first PCH as a quantitative trait in linkage analysis has a gain in power compared to the standard PCA. Within the PCH framework, Wang et al [11] proposed a ridge penalized PCs based on heritability (PCH λ ) for high dimensional family data by adding a penalty to the subject-specific variation. Oualkacha et al [12] proposed an ANOVA estimator for the variance components for general pedigrees and high dimensional family data using the PCH framework.…”

Section: Introductionmentioning

confidence: 99%

Global Individual Ancestry Using Principal Components for Family Data

Andrade¹,

Ray

Pereira³

et al. 2015

Hum Hered

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Studies of complex human diseases and traits associated with candidate genes are potentially vulnerable to bias (confounding) due to population stratification and inbreeding, especially in admixed population. In GWAS, the principal components (PCs) method provides a global ancestry value per subject, allowing corrections for population stratification. However, these coefficients are typically estimated assuming unrelated individuals, and if family structure is present and ignored, such substructures may induce artifactual PCs. Extensions of the PCs method have been proposed by Konishi and Rao [Biometrika 1992;79:631-641], taking into account only siblings' relatedness, and by Oualkacha et al. [Stat Appl Genet Mol Biol 2012, DOI: 10.2202/1544-6115.1711], taking into account large pedigrees and high-dimensional phenotype data. In this work, we extend these methods to estimate the global individual ancestry coefficients from PCs derived from different variance component matrix estimators using SNPs from two simulated data sets and two real data sets: the GENOA sibship data consisting of European and African-American subjects and the Baependi Heart Study consisting of 80 extended Brazilian families, both with genotyping data from the Affymetrix 6.0 chip. Our results show that the family structure plays an important role in the estimation of the global individual ancestry value for extended pedigrees but not for sibships.

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“…Existing methods include principal components analysis (PCA) where for the first component, a i , i = 1 ,…, K are coefficients that maximize the variance of Ỹ ; principal component of heritability (PCH) with coefficients maximizing the total heritability of Ỹ [18] and penalized PCH applicable to high-dimensional data [19, 20]; and principal components of heritability with coefficients maximizing the quantitative trait locus (QTL) heritability (PCQH) of Ỹ [21–24], that is, the variance explained by the genetic marker. The PCQH approaches are designed to maximize the individual phenotype variation explained by the genetic marker and thus may be more powerful than PCA and PCH in genetic association studies.…”

Section: Methods For Detecting Association Using Multivariate Phenmentioning

confidence: 99%

Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

Yang

Wang

2012

Journal of Probability and Statistics

Self Cite

156

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This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

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A Ridge Penalized Principal-Components Approach Based on Heritability for High-Dimensional Data

Cited by 22 publications

References 19 publications

Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies

Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies

Global Individual Ancestry Using Principal Components for Family Data

Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

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