A robust statistical method for association-based eQTL analysis

Background

It has been well established that theoretical kernel for recently surging genome-wide association study (GWAS) is statistical inference of linkage disequilibrium (LD) between a tested genetic marker and a putative locus affecting a disease trait. However, LD analysis is vulnerable to several confounding factors of which population stratification is the most prominent. Whilst many methods have been proposed to correct for the influence either through predicting the structure parameters or correcting inflation in the test statistic due to the stratification, these may not be feasible or may impose further statistical problems in practical implementation.

Methodology

We propose here a novel statistical method to control spurious LD in GWAS from population structure by incorporating a control marker into testing for significance of genetic association of a polymorphic marker with phenotypic variation of a complex trait. The method avoids the need of structure prediction which may be infeasible or inadequate in practice and accounts properly for a varying effect of population stratification on different regions of the genome under study. Utility and statistical properties of the new method were tested through an intensive computer simulation study and an association-based genome-wide mapping of expression quantitative trait loci in genetically divergent human populations.

Results/Conclusions

The analyses show that the new method confers an improved statistical power for detecting genuine genetic association in subpopulations and an effective control of spurious associations stemmed from population structure when compared with other two popularly implemented methods in the literature of GWAS.     

Handling multiple testing while interpreting microarrays with the Gene Ontology Database

Background  

The development of software tools that analyze microarray data in the context of genetic knowledgebases is being pursued by multiple research groups using different methods. A common problem for many of these tools is how to correct for multiple statistical testing since simple corrections are overly conservative and more sophisticated corrections are currently impractical. A careful study of the nature of the distribution one would expect by chance, such as by a simulation study, may be able to guide the development of an appropriate correction that is not overly time consuming computationally.     

STING Millennium Suite: integrated software for extensive analyses of 3d structures of proteins and their complexes

Background  

The integration of many aspects of protein/DNA structure analysis is an important requirement for software products in general area of structural bioinformatics. In fact, there are too few software packages on the internet which can be described as successful in this respect. We might say that what is still missing is publicly available, web based software for interactive analysis of the sequence/structure/function of proteins and their complexes with DNA and ligands. Some of existing software packages do have certain level of integration and do offer analysis of several structure related parameters, however not to the extent generally demanded by a user.     

MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments

Background  

Integration of multiple results from Quantitative Trait Loci (QTL) studies is a key point to understand the genetic determinism of complex traits. Up to now many efforts have been made by public database developers to facilitate the storage, compilation and visualization of multiple QTL mapping experiment results. However, studying the congruency between these results still remains a complex task. Presently, the few computational and statistical frameworks to do so are mainly based on empirical methods (e.g. consensus genetic maps are generally built by iterative projection).     

Analysis of an optimal hidden Markov model for secondary structure prediction

Background  

Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models.     

PRESTO: Rapid calculation of order statistic distributions and multiple-testing adjusted P-values via permutation for one and two-stage genetic association studies

Background  

Large-scale genetic association studies can test hundreds of thousands of genetic markers for association with a trait. Since the genetic markers may be correlated, a Bonferroni correction is typically too stringent a correction for multiple testing. Permutation testing is a standard statistical technique for determining statistical significance when performing multiple correlated tests for genetic association. However, permutation testing for large-scale genetic association studies is computationally demanding and calls for optimized algorithms and software. PRESTO is a new software package for genetic association studies that performs fast computation of multiple-testing adjusted P-values via permutation of the trait.     

GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits

Background

In designing genome-wide association (GWA) studies it is important to calculate statistical power. General statistical power calculation procedures for quantitative measures often require information concerning summary statistics of distributions such as mean and variance. However, with genetic studies, the effect size of quantitative traits is traditionally expressed as heritability, a quantity defined as the amount of phenotypic variation in the population that can be ascribed to the genetic variants among individuals. Heritability is hard to transform into summary statistics. Therefore, general power calculation procedures cannot be used directly in GWA studies. The development of appropriate statistical methods and a user-friendly software package to address this problem would be welcomed.

Results

This paper presents GWAPower, a statistical software package of power calculation designed for GWA studies with quantitative traits, where genetic effect is defined as heritability. Based on several popular one-degree-of-freedom genetic models, this method avoids the need to specify the non-centrality parameter of the F-distribution under the alternative hypothesis. Therefore, it can use heritability information directly without approximation. In GWAPower, the power calculation can be easily adjusted for adding covariates and linkage disequilibrium information. An example is provided to illustrate GWAPower, followed by discussions.

Conclusions

GWAPower is a user-friendly free software package for calculating statistical power based on heritability in GWA studies with quantitative traits. The software is freely available at: http://dl.dropbox.com/u/10502931/GWAPower.zip     

Modeling expression quantitative trait loci in data combining ethnic populations

Background  

Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive.     

Meta-population structure in a coral reef fish demonstrated by genetic data on patterns of migration,extinction and re-colonisation

Background  

Management strategies for coral reefs are dependant on information about the spatial population structure and connectivity of reef organisms. Genetic tools can reveal important information about population structure, however, this information is lacking for many reef species. We used a mitochondrial molecular marker to examine the population genetic structure and the potential for meta-population dynamics in a direct developing coral reef fish using 283 individuals from 15 reefs on the Great Barrier Reef, Australia. We employed a hierarchical sampling design to test genetic models of population structure at multiple geographical scales including among regions, among shelf position and reefs within regions. Predictions from island, isolation-by-distance and meta-population models, including the potential for asymmetric migration, local extinction and patterns of re-colonisation were examined.     

Iterative pruning PCA improves resolution of highly structured populations

Background  

Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming.     

Plant traits correlated with generation time directly affect inbreeding depression and mating system and indirectly genetic structure

Background  

Understanding the mechanisms that control species genetic structure has always been a major objective in evolutionary studies. The association between genetic structure and species attributes has received special attention. As species attributes are highly taxonomically constrained, phylogenetically controlled methods are necessary to infer causal relationships. In plants, a previous study controlling for phylogenetic signal has demonstrated that Wright's F _ST, a measure of genetic differentiation among populations, is best predicted by the mating system (outcrossing, mixed-mating or selfing) and that plant traits such as perenniality and growth form have only an indirect influence on F _ST via their association with the mating system. The objective of this study is to further outline the determinants of plant genetic structure by distinguishing the effects of mating system on gene flow and on genetic drift. The association of biparental inbreeding and inbreeding depression with population genetic structure, mating system and plant traits are also investigated.     

BEAST: Bayesian evolutionary analysis by sampling trees

Background  

The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.     

GWAMA: software for genome-wide association meta-analysis

Background  

Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies.     

Function2Gene: A gene selection tool to increase the power of genetic association studies by utilizing public databases and expert knowledge

Background  

Many common disorders have multiple genetic components which convey increased susceptibility. SNPs have been used to identify genetic components which are associated with a disease. Unfortunately, many studies using these methods suffer from low reproducibility due to lack of power.     

Efficient Bayesian approach for multilocus association mapping including gene-gene interactions

Background  

Since the introduction of large-scale genotyping methods that can be utilized in genome-wide association (GWA) studies for deciphering complex diseases, statistical genetics has been posed with a tremendous challenge of how to most appropriately analyze such data. A plethora of advanced model-based methods for genetic mapping of traits has been available for more than 10 years in animal and plant breeding. However, most such methods are computationally intractable in the context of genome-wide studies. Therefore, it is hardly surprising that GWA analyses have in practice been dominated by simple statistical tests concerned with a single marker locus at a time, while the more advanced approaches have appeared only relatively recently in the biomedical and statistical literature.     

AWclust: point-and-click software for non-parametric population structure analysis

Background  

Population structure analysis is important to genetic association studies and evolutionary investigations. Parametric approaches, e.g. STRUCTURE and L-POP, usually assume Hardy-Weinberg equilibrium (HWE) and linkage equilibrium among loci in sample population individuals. However, the assumptions may not hold and allele frequency estimation may not be accurate in some data sets. The improved version of STRUCTURE (version 2.1) can incorporate linkage information among loci but is still sensitive to high background linkage disequilibrium. Nowadays, large-scale single nucleotide polymorphisms (SNPs) are becoming popular in genetic studies. Therefore, it is imperative to have software that makes full use of these genetic data to generate inference even when model assumptions do not hold or allele frequency estimation suffers from high variation.     

Genotype determination for polymorphisms in linkage disequilibrium

Background  

Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information.     

Population Genetic Structure of Aedes (Stegomyia) aegypti (L.) at a Micro-Spatial Scale in Thailand: Implications for a Dengue Suppression Strategy

Background

The genetic population structure of Aedes (Stegomyia) aegypti (L.), the main vector of dengue virus, is being investigated in areas where a novel dengue suppression program is to be implemented. The aim of the program is to release and establish mosquito populations with impaired virus transmission capabilities. To model effects of the release and devise protocols for its implementation, information about the genetic structure of populations at a range of spatial scales is required.

Methodology/Principal Findings

This study investigates a potential release site in the Hua Sam Rong Subdistrict of Plaeng Yao District, Chachoengsao Province, in eastern Thailand which comprises a complex of five villages within a 10 km radius. Aedes aegypti resting indoors was sampled at four different times of year from houses within the five villages. Genetic markers were used to screen the mosquitoes: two Exon Primed Intron Crossing (EPIC) markers and five microsatellite markers. The raw allele size was determined using several statistical software packages to analyze the population structure of the mosquito. Estimates of effective population size for each village were low, but there was no evidence of genetic isolation by geographic distance.

Conclusions

The presence of temporary genetic structure is possibly caused by genetic drift due to large contributions of adults from a few breeding containers. This suggests that the introduction of mosquitoes into an area needs to proceed through multiple releases and targeting of sites where mosquitoes are emerging in large numbers.     

eQTL Viewer: visualizing how sequence variation affects genome-wide transcription

Background  

Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression variations. To map eQTL, thousands of expression profiles are related with sequence polymorphisms across the genome through their correlated variations. These eQTL distribute in many chromosomal regions, each of which can include many genes. The large number of mapping results produced makes it difficult to consider simultaneously the relationships between multiple genomic regions and multiple expressional profiles. There is a need for informative bioinformatics tools to assist the visualization and interpretation of these mapping results.