Multiple Quantitative Phenotypes Research Articles

Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes.

Previous studies have suggested that gene-environment interactions (GEIs) between a common variant and an environmental factor can influence multiple correlated phenotypes simultaneously, that is, GEI pleiotropy, and that analyzing multiple phenotypes jointly is more powerful than analyzing phenotypes separately by using single-phenotype GEI tests. Methods to test the GEI for rare variants with multiple phenotypes are, however, lacking. In our work, we model the correlation among the GEI effects of a variant on multiple quantitative phenotypes through four kernels and propose four multiphenotype GEI tests for rare variants, which are a test with a homogeneous kernel (Hom-GEI), a test with a heterogeneous kernel (Het-GEI), a test with a projection phenotype kernel (PPK-GEI) and a test with a linear phenotype kernel (LPK-GEI). Through numerical simulations, we show that correlation among phenotypes can enhance the statistical power except for LPK-GEI, which simply combines statistics from single-phenotype GEI tests and ignores the phenotypic correlations. Among almost all considered scenarios, Het-GEI and PPK-GEI are more powerful than Hom-GEI and LPK-GEI. We apply Het-GEI and PPK-GEI in the genome-wide GEI analysis of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank. We analyze 18,101 genes and find that LEUTX is associated with SBP and DBP (p = 2.20×10-6) through its interaction with hemoglobin. The single-phenotype GEI test and our multiphenotype GEI tests Het-GEI and PPK-GEI are also used to evaluate the gene-hemoglobin interactions for 22 genes that were previously reported to be associated with SBP or DBP in a meta-analysis of genetic main effects. MYO1C shows nominal significance (p < 0.05) by the Het-GEI test. NOS3 shows nominal significance in DBP and MYO1C in both SBP and DBP by the single-phenotype GEI test.

Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes.

To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multifactor dimensionality reduction (MDR). Early MDR methods mainly focused on binary traits. More recently, several extensions of MDR have been developed for analyzing various traits such as quantitative traits and survival times. Newer technologies, such as genome-wide association studies (GWAS), have now been developed for assessing multiple traits, to simultaneously identify genetic variants associated with various pathological phenotypes. It has also been well demonstrated that analyzing multiple traits has several advantages over single trait analysis. While there remains a need to find GGIs for multiple traits, such studies have become more difficult, due to a lack of novel methods and software. Herein, we propose a novel multi-CMDR method, by combining fuzzy clustering and MDR, to find GGIs for multiple traits. Multi-CMDR showed similar power to existing methods, when phenotypes followed bivariate normal distributions, and showed better power than others for skewed distributions. The validity of multi-CMDR was confirmed by analyzing real-life Korean GWAS data.

Transmission-based association mapping of triglyceride levels in a longitudinal framework using quasi-likelihood

Complex genetic traits are often characterized by multiple quantitative phenotypes. Because values of such phenotypes vary over time, it is thought that analyses of longitudinal data on the phenotypes may lead to increased power in detecting genetic association. In this paper, we extend a transmission-based association test applying quasi-likelihood that has been developed by us to the longitudinal framework and to carry out a genome-wide association analysis of triglyceride levels based on the data provided in GAW20. We consider different phenotype definitions based on administration of fenofibrate and obtain significant association findings within genes involved in heart diseases.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.

Multivariate Quantitative Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions

Objectives: To determine gene-gene interactions and missing heritability of complex diseases is a challenging topic in genome-wide association studies. The multifactor dimensionality reduction (MDR) method is one of the most commonly used methods for identifying gene-gene interactions with dichotomous phenotypes. For quantitative phenotypes, the generalized MDR or quantitative MDR (QMDR) methods have been proposed. These methods are known as univariate methods because they consider only one phenotype. To date, there are few methods for analyzing multiple phenotypes. Methods: To address this problem, we propose a multivariate QMDR method (Multi-QMDR) for multivariate correlated phenotypes. We summarize the multivariate phenotypes into a univariate score by dimensional reduction analysis, and then classify the samples accordingly into high-risk and low-risk groups. We use different ways of summarizing mainly based on the principal components. Multi-QMDR is model-free and easy to implement. Results: Multi-QMDR is applied to lipid-related traits. The properties of Multi- QMDR were investigated through simulation studies. Empirical studies show that Multi-QMDR outperforms existing univariate and multivariate methods at identifying causal interactions. Conclusions: The Multi-QMDR approach improves the performance of QMDR when multiple quantitative phenotypes are available.

Comparing Partial Least Square Approaches in a Gene- or Region-Based Association Study for Multiple Quantitative Phenotypes

On thinking quantitatively of complex diseases, there are at least three statistical strategies for association studies: one single-nucleotide polymorphism (SNP) on a single trait, gene or region (with multiple SNPs) on a single trait, and gene or region on multiple traits. The third approach is the most general in dissecting genetic mechanisms underlying complex diseases underpinning multiple quantitative traits. Gene or region association methods based on partial least square (PLS) approaches have been shown to have apparent power advantage. However, few approaches have been developed for multiple quantitative phenotypes or traits underlying a condition or disease, and the performance of various PLS approaches used in association studies for multiple quantitative traits have not been assessed. Here we exploit association between multiple SNPs and multiple phenotypes or traits, from a regression perspective, through exhaustive scan statistics (sliding window) using PLS and sparse PLS regressions. Simulations were conducted to assess the performance of the proposed scan statistics and compare them with existing methods. The proposed methods were applied to 12 regions of genome-wide association study data from the European Prospective Investigation of Cancer–Norfolk study.

Comparing partial least square approaches in a gene- or region-based association study for multiple quantitative phenotypes.

On thinking quantitatively of complex diseases, there are at least three statistical strategies for association studies: one single-nucleotide polymorphism (SNP) on a single trait, gene or region (with multiple SNPs) on a single trait, and gene or region on multiple traits. The third approach is the most general in dissecting genetic mechanisms underlying complex diseases underpinning multiple quantitative traits. Gene or region association methods based on partial least square (PLS) approaches have been shown to have apparent power advantage. However, few approaches have been developed for multiple quantitative phenotypes or traits underlying a condition or disease, and the performance of various PLS approaches used in association studies for multiple quantitative traits have not been assessed. Here we exploit association between multiple SNPs and multiple phenotypes or traits, from a regression perspective, through exhaustive scan statistics (sliding window) using PLS and sparse PLS regressions. Simulations were conducted to assess the performance of the proposed scan statistics and compare them with existing methods. The proposed methods were applied to 12 regions of genome-wide association study data from the European Prospective Investigation of Cancer-Norfolk study.

Comparing Partial Least Square Approaches in a Gene- or Region-Based Association Study for Multiple Quantitative Phenotypes

Comparing Partial Least Square Approaches in a Gene- or Region-Based Association Study for Multiple Quantitative Phenotypes

A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits

BackgroundOn thinking quantitatively of complex diseases, there are at least three statistical strategies for analyzing the gene-gene interaction: SNP by SNP interaction on single trait, gene-gene (each can involve multiple SNPs) interaction on single trait and gene-gene interaction on multiple traits. The third one is the most general in dissecting the genetic mechanism underlying complex diseases underpinning multiple quantitative traits. In this paper, we developed a novel statistic for this strategy through modifying the Partial Least Squares Path Modeling (PLSPM), called mPLSPM statistic.ResultsSimulation studies indicated that mPLSPM statistic was powerful and outperformed the principal component analysis (PCA) based linear regression method. Application to real data in the EPIC-Norfolk GWAS sub-cohort showed suggestive interaction (γ) between TMEM18 gene and BDNF gene on two composite body shape scores (γ = 0.047 and γ = 0.058, with P = 0.021, P = 0.005), and BMI (γ = 0.043, P = 0.034). This suggested these scores (synthetically latent traits) were more suitable to capture the obesity related genetic interaction effect between genes compared to single trait.ConclusionsThe proposed novel mPLSPM statistic is a valid and powerful gene-based method for detecting gene-gene interaction on multiple quantitative phenotypes.

Inferring Gene Function and Network Organization in Drosophila Signaling by Combined Analysis of Pleiotropy and Epistasis

High-throughput genetic interaction screens have enabled functional genomics on a network scale. Groups of cofunctional genes commonly exhibit similar interaction patterns across a large network, leading to novel functional inferences for a minority of previously uncharacterized genes within a group. However, such analyses are often unsuited to cases with a few relevant gene variants or sparse annotation. Here we describe an alternative analysis of cell growth signaling using a computational strategy that integrates patterns of pleiotropy and epistasis to infer how gene knockdowns enhance or suppress the effects of other knockdowns. We analyzed the interaction network for RNAi knockdowns of a set of 93 incompletely annotated genes in a Drosophila melanogaster model of cellular signaling. We inferred novel functional relationships between genes by modeling genetic interactions in terms of knockdown-to-knockdown influences. The method simultaneously analyzes the effects of partially pleiotropic genes on multiple quantitative phenotypes to infer a consistent model of each genetic interaction. From these models we proposed novel candidate Ras inhibitors and their Ras signaling interaction partners, and each of these hypotheses can be inferred independent of network-wide patterns. At the same time, the network-scale interaction patterns consistently mapped pathway organization. The analysis therefore assigns functional relevance to individual genetic interactions while also revealing global genetic architecture.

Genome-wide association studies of quantitative traits with related individuals: little (power) lost but much to be gained

For complex disease genetics research in human populations, remarkable progress has been made in recent times with the publication of a number of genome-wide association scans (GWAS) and subsequent statistical replications. These studies have identified new genes and pathways implicated in disease, many of which were not known before. Given these early successes, more GWAS are being conducted and planned, both for disease and quantitative phenotypes. Many researchers and clinicians have DNA samples available on collections of families, including both cases and controls. Twin registries around the world have facilitated the collection of large numbers of families, with DNA and multiple quantitative phenotypes collected on twin pairs and their relatives. In the design of a new GWAS with a fixed budget for the number of chips, the question arises whether to include or exclude related individuals. It is commonly believed to be preferable to use unrelated individuals in the first stage of a GWAS because relatives are 'over-matched' for genotypes. In this study, we quantify that for GWAS of a quantitative phenotype, relative to a sample of unrelated individuals surprisingly little power is lost when using relatives. The advantages of using relatives are manifold, including the ability to perform more quality control, the choice to perform within-family tests of association that are robust to population stratification, and the ability to perform joint linkage and association analysis. Therefore, the advantages of using relatives in GWAS for quantitative traits may well outweigh the small disadvantage in terms of statistical power.

Genes, maternal smoking, and the offspring brain and body during adolescence: design of the Saguenay Youth Study.

The search for genes of complex traits is aided by the availability of multiple quantitative phenotypes collected in geographically isolated populations. Here we provide rationale for a large-scale study of gene-environment interactions influencing brain and behavior and cardiovascular and metabolic health in adolescence, namely the Saguenay Youth Study (SYS). The SYS is a retrospective study of long-term consequences of prenatal exposure to maternal cigarette smoking (PEMCS) in which multiple quantitative phenotypes are acquired over five sessions (telephone interview, home, hospital, laboratory, and school). To facilitate the search for genes that modify an individual's response to an in utero environment (i.e. PEMCS), the study is family-based (adolescent sibships) and is carried out in a relatively geographically isolated population of the Saguenay Lac-Saint-Jean (SLSJ) region in Quebec, Canada. DNA is acquired in both biological parents and in adolescent siblings. A genome-wide scan will be carried out with sib-pair linkage analyses, and fine mapping of identified loci will be done with family-based association analyses. Adolescent sibships (12-18 years of age; two or more siblings per family) are recruited in high schools throughout the SLSJ region; only children of French-Canadian origin are included. Based on a telephone interview, potential participants are classified as exposed or nonexposed prenatally to maternal cigarette smoking; the two groups are matched for the level of maternal education and the attended school. A total of 500 adolescent participants in each group will be recruited and phenotyped. The following types of datasets are collected in all adolescent participants: (1) magnetic resonance images of brain, abdominal fat, and kidneys, (2) standardized and computer-based neuropsychological tests, (3) hospital-based cardiovascular, body-composition and metabolic assessments, and (4) questionnaire-derived measures (e.g. life habits such as eating and physical activity; drug, alcohol use and delinquency; psychiatric symptoms; personality; home and school environment; academic and vocational attitudes). Parents complete a medical questionnaire, home-environment questionnaire, a handedness questionnaire, and a questionnaire about their current alcohol and drug use, depression, anxiety, and current and past antisocial behavior. To date, we have fully phenotyped a total of 408 adolescent participants. Here we provide the description of the SYS and, using the initial sample, we present information on ascertainment, demographics of the exposed and nonexposed adolescents and their parents, and the initial MRI-based assessment of familiality in the brain size and the volumes of grey and white matter.