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A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.

ABSTRACT: Most statistical methods for quantitative trait loci (QTL) mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While methods exist for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. We propose two simple, fast methods that maintain high power and precision and are amenable to extensions with multiple-QTL models using a penalized likelihood approach. After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes. Our methods have been implemented as a package for R, funqtl.

SUBMITTER: Kwak IY

PROVIDER: S-EPMC4125409 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.

Kwak Il-Youp IY Moore Candace R CR Spalding Edgar P EP Broman Karl W KW

Genetics 20140614 4

Most statistical methods for quantitative trait loci (QTL) mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While methods exist for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. We propose two simple, fa ...[more]

PMID: 24931408

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Project description:BACKGROUND:In order to better understand complex diseases, it is important to understand how genetic variation in the regulatory regions affects gene expression. Genetic variants found in these regulatory regions have been shown to activate transcription in a tissue-specific manner. Therefore, it is important to map the aforementioned expression quantitative trait loci (eQTL) using a statistically disciplined approach that jointly models all the tissues and makes use of all the information available to maximize the power of eQTL mapping. In this context, we are proposing a score test-based approach where we model tissue-specificity as a random effect and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants. RESULTS:Our approach has 1) a distinct computational edge, and 2) comparable performance in terms of statistical power over other currently existing joint modeling approaches such as MetaTissue eQTL and eQTL-BMA. Using simulations, we show that our method increases the power to detect eQTLs when compared to a tissue-by-tissue approach and can exceed the performance, in terms of computational speed, of MetaTissue eQTL and eQTL-BMA. We apply our method to two publicly available expression datasets from normal human brains, one comprised of four brain regions from 150 neuropathologically normal samples and another comprised of ten brain regions from 134 neuropathologically normal samples, and show that by using our method and jointly analyzing multiple brain regions, we identify eQTLs within more genes when compared to three often used existing methods. CONCLUSIONS:Since we employ a score test-based approach, there is no need for parameter estimation under the alternative hypothesis. As a result, model parameters only have to be estimated once per genome, significantly decreasing computation time. Our method also accommodates the analysis of next- generation sequencing data. As an example, by modeling gene transcripts in an analogous fashion to tissues in our current formulation one would be able to test for both a variant overall effect across all isoforms of a gene as well as transcript-specific effects. We implement our approach within the R package JAGUAR, which is now available at the Comprehensive R Archive Network repository.

Project description:OBJECTIVE:Deficits in neurocognitive function have been demonstrated in individuals with schizophrenia and in the unaffected family members of these individuals. Genetic studies of such complementary traits, along with traditional analyses of diagnosis, may help to elucidate the biological pathways underlying familial liability to schizophrenia and related disorders. The authors conducted a multiplex, multigenerational family study using a genome-wide screen for schizophrenia and related neurocognitive phenotypes. METHOD:Participants were 1) 676 European American individuals from 43 families, ascertained through an individual with schizophrenia, and 2) 236 healthy comparison subjects. Participants were evaluated clinically and examined through the use of a computerized neurocognitive test battery that provided measures of accuracy and speed on the cognitive domains of abstraction and mental flexibility; attention; verbal, face, and spatial memory; language and reasoning; spatial and emotion processing; and sensorimotor dexterity. A genome-wide linkage screen was also performed. Healthy comparison subjects were included in order to obtain normative phenotypic data but were not genotyped. RESULTS:Significant evidence for linkage of schizophrenia to chromosome 19q was observed. Analysis of cognitive traits revealed significant linkage to chromosome 5q for the domains of abstraction and mental flexibility. A variety of other neurocognitive traits also showed nominal evidence of linkage to the 5q region. Joint analyses with diagnosis suggested that this quantitative trait locus may also influence schizophrenia. CONCLUSIONS:Although chromosome 5 has been implicated in previous linkage studies of schizophrenia, the identification of the chromosome 19 quantitative trait locus is a novel finding. The identification of the chromosome 5 quantitative trait locus through linkage to neurocognitive phenotypes in the present study may inform functional hypotheses pertaining to how genotypes are connected to disease.

Dataset Information

A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.

Publications

A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.

Similar Datasets

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