Fishing for Pleiotropic QTLs in a Polygenic Sea

Bauman, Lara; Almasy, Laura; Blangero, John; Duggirala, Ravi; Sinsheimer, Janet S.; Lange, Kenneth

doi:10.1111/j.1529-8817.2005.00181.x

Cited by 24 publications

(23 citation statements)

References 52 publications

(53 reference statements)

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“…Our computer program Mendel relies on a quasiNewton algorithm for maximum likelihood estimation. Bauman et al (2005) discusses an alternative EM algorithm as well as factor-analytic parameterizations of variance matrices. Given the presence of covariates and heterogenous pedigree structures, permutation testing is rarely possible.…”

Section: Methodsmentioning

confidence: 99%

“…This is not a trivial matter because combining traits exacerbates the multiple testing problem and may add noise and degrade power (Amos et al 2001;Bauman et al 2005). With outbred populations it is intertwined with the issue of ascertainment (Dawson and Elston 1984); it may also be a problem with inbred populations since strains are often chosen for a particular experiment on the basis of their average phenotype.…”

Section: Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

et al. 2008

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Fixed effects models have dominated the statistical analysis of genetic crosses between inbred strains. In spite of their popularity, the traditional models ignore polygenic background and must be tailored to each specific cross. We reexamine the role of random effect models in gene mapping with inbred strains. The biggest difficulty in implementing random effect models is the lack of a coherent way of calculating trait covariances between relatives. The standard model for outbred populations is based on premises of genetic equilibrium that simply do not apply to crosses between inbred strains since every animal in a strain is genetically identical and completely homozygous. We fill this theoretical gap by introducing novel combinatorial entities called strain coefficients. With an appropriate theory, it is possible to reformulate QTL mapping and QTL association analysis as an application of mixed models involving both fixed and random effects. After developing this theory, our first example compares the mixed effects model to a standard fixed effects model using simulated advanced intercross line (AIL) data. Our second example deals with hormone data. Here multivariate traits and parameter identifiability questions arise. Our final example involves random mating among eight strains and vividly demonstrates the versatility of our models.

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Section: Methodsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

et al. 2008

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“…We also conducted two other sets of simulations to investigate linkage analysis using the first PCA (PCA1) and first PCH (PCH1) as phenotypes. This was done by performing a univariate multipoint variance-component linkage analysis and test for linkage using the Mendel software (Lange et al, 1976, 1983, 2006and Bauman et al, 2005. The two settings we used for linkage analysis were identical to Wang et al (2007a).…”

Section: Comparing the Use Of Pch Versus Pca In Linkage Analysismentioning

confidence: 99%

Principal Components of Heritability for High Dimension Quantitative Traits and General Pedigrees

Oualkacha¹,

Labbe²,

Ciampi³

et al. 2012

Statistical Applications in Genetics and Molecular Biology

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For many complex disorders, genetically relevant disease definition is still unclear. For this reason, researchers tend to collect large numbers of items related directly or indirectly to the disease diagnostic. Since the measured traits may not be all influenced by genetic factors, researchers are faced with the problem of choosing which traits or combinations of traits to consider in linkage analysis. To combine items, one can subject the data to a principal component analysis. However, when family date are collected, principal component analysis does not take family structure into account. In order to deal with these issues, Ott & Rabinowitz (1999) introduced the principal components of heritability (PCH), which capture the familial information across traits by calculating linear combinations of traits that maximize heritability. The calculation of the PCHs is based on the estimation of the genetic and the environmental components of variance. In the genetic context, the standard estimators of the variance components are Lange's maximum likelihood estimators, which require complex numerical calculations. The objectives of this paper are the following: i) to review some standard strategies available in the literature to estimate variance components for unbalanced data in mixed models; ii) to propose an ANOVA method for a genetic random effect model to estimate the variance components, which can be applied to general pedigrees and high dimensional family data within the PCH framework; iii) to elucidate the connection between PCH analysis and Linear Discriminant Analysis. We use computer simulations to show that the proposed method has similar asymptotic properties as Lange's method when the number of traits is small, and we study the efficiency of our method when the number of traits is large. A data analysis involving schizophrenia and bipolar quantitative traits is finally presented to illustrate the PCH methodology.

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“…This capacity can increase the power to detect associations (Bauman et al 2005). To illustrate this, we simulated a single replicate of a CC funnel cross with measured bivariate traits.…”

Section: Qtl Association Testingmentioning

confidence: 99%

Quantitative Trait Loci Association Mapping by Imputation of Strain Origins in Multifounder Crosses

Zhou

Ghazalpour²,

Sobel³

et al. 2012

Genetics

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Although mapping quantitative traits in inbred strains is simpler than mapping the analogous traits in humans, classical inbred crosses suffer from reduced genetic diversity compared to experimental designs involving outbred animal populations. Multiple crosses, for example the Complex Trait Consortium's eight-way cross, circumvent these difficulties. However, complex mating schemes and systematic inbreeding raise substantial computational difficulties. Here we present a method for locally imputing the strain origins of each genotyped animal along its genome. Imputed origins then serve as mean effects in a multivariate Gaussian model for testing association between trait levels and local genomic variation. Imputation is a combinatorial process that assigns the maternal and paternal strain origin of each animal on the basis of observed genotypes and prior pedigree information. Without smoothing, imputation is likely to be ill-defined or jump erratically from one strain to another as an animal's genome is traversed. In practice, one expects to see long stretches where strain origins are invariant. Smoothing can be achieved by penalizing strain changes from one marker to the next. A dynamic programming algorithm then solves the strain imputation process in one quick pass through the genome of an animal. Imputation accuracy exceeds 99% in practical examples and leads to high-resolution mapping in simulated and real data. The previous fastest quantitative trait loci (QTL) mapping software for dense genome scans reduced compute times to hours. Our implementation further reduces compute times from hours to minutes with no loss in statistical power. Indeed, power is enhanced for full pedigree data.

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Fishing for Pleiotropic QTLs in a Polygenic Sea

Cited by 24 publications

References 52 publications

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

Principal Components of Heritability for High Dimension Quantitative Traits and General Pedigrees

Quantitative Trait Loci Association Mapping by Imputation of Strain Origins in Multifounder Crosses

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