Comparisons of multi-marker association methods to detect association between a candidate region and disease

DiGAS: Differential gene allele spectrum as a descriptor in genetic studies

Diagnosing individuals with complex genetic diseases is a challenging task. Computational methodologies exploit information at the genotype level by taking into account single nucleotide polymorphisms (SNPs) leveraging the results of genome-wide association studies analysis to assign a statistical significance to each SNP. Recent methodologies extend such an approach by aggregating SNP significance at the genetic level to identify genes that are related to the condition under study. However, such methodologies still suffer from the initial SNP analysis limitations. Here, we present DiGAS, a tool for diagnosing genetic conditions by computing significance, by means of SNP information, directly at the complex level of genetic regions. Such an approach is based on a generalized notion of allele spectrum, which evaluates the complete genetic alterations of the SNP set belonging to a genetic region at the population level. The statistical significance of a region is then evaluated through a differential allele spectrum analysis between the conditions of individuals belonging to the population. Tests, performed on well-established datasets regarding Alzheimer's disease, show that DiGAS outperforms the state of the art in distinguishing between sick and healthy subjects.

scDR: Predicting Drug Response at Single-Cell Resolution

Heterogeneity exists inter- and intratumorally, which might lead to different drug responses. Therefore, it is extremely important to clarify the drug response at single-cell resolution. Here, we propose a precise single-cell drug response (scDR) prediction method for single-cell RNA sequencing (scRNA-seq) data. We calculated a drug-response score (DRS) for each cell by integrating drug-response genes (DRGs) and gene expression in scRNA-seq data. Then, scDR was validated through internal and external transcriptomics data from bulk RNA-seq and scRNA-seq of cell lines or patient tissues. In addition, scDR could be used to predict prognoses for BLCA, PAAD, and STAD tumor samples. Next, comparison with the existing method using 53,502 cells from 198 cancer cell lines showed the higher accuracy of scDR. Finally, we identified an intrinsic resistant cell subgroup in melanoma, and explored the possible mechanisms, such as cell cycle activation, by applying scDR to time series scRNA-seq data of dabrafenib treatment. Altogether, scDR was a credible method for drug response prediction at single-cell resolution, and helpful in drug resistant mechanism exploration.

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis

The rapid increase in the number of genetic variants identified to be associated with Amyotrophic Lateral Sclerosis (ALS) through genome-wide association studies (GWAS) has created an emerging need to understand the functional pathways that are implicated in the pathology of ALS. Gene-set analysis (GSA) is a powerful method that can provide insight into the associated biological pathways, determining the joint effect of multiple genetic markers. The main contribution of this review is the collection of ALS GSA studies that employ GWAS or individual-based genotype data, investigating their methodology and results related to ALS-associated molecular pathways. Furthermore, the limitations in standard single-gene analyses are summarized, highlighting the power of gene-set analysis, and a brief overview of the statistical properties of gene-set analysis and related concepts is provided. The main aims of this review are to investigate the reproducibility of the collected studies and identify their strengths and limitations, in order to enhance the experimental design and therefore the quality of the results of future studies, deepening our understanding of this devastating disease.

A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.

Imaging Genetics Study Based on a Temporal Group Sparse Regression and Additive Model for Biomarker Detection of Alzheimer's Disease

Imaging genetics is an effective tool used to detect potential biomarkers of Alzheimer's disease (AD) in imaging and genetic data. Most existing imaging genetics methods analyze the association between brain imaging quantitative traits (QTs) and genetic data [e.g., single nucleotide polymorphism (SNP)] by using a linear model, ignoring correlations between a set of QTs and SNP groups, and disregarding the varied associations between longitudinal imaging QTs and SNPs. To solve these problems, we propose a novel temporal group sparsity regression and additive model (T-GSRAM) to identify associations between longitudinal imaging QTs and SNPs for detection of potential AD biomarkers. We first construct a nonparametric regression model to analyze the nonlinear association between QTs and SNPs, which can accurately model the complex influence of SNPs on QTs. We then use longitudinal QTs to identify the trajectory of imaging genetic patterns over time. Moreover, the SNP information of group and individual levels are incorporated into the proposed method to boost the power of biomarker detection. Finally, we propose an efficient algorithm to solve the whole T-GSRAM model. We evaluated our method using simulation data and real data obtained from AD neuroimaging initiative. Experimental results show that our proposed method outperforms several state-of-the-art methods in terms of the receiver operating characteristic curves and area under the curve. Moreover, the detection of AD-related genes and QTs has been confirmed in previous studies, thereby further verifying the effectiveness of our approach and helping understand the genetic basis over time during disease progression.

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Background

Clinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown.

Methods

Our analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE.

Results

We found there existed a significant positive genetic correlation (r_g = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway.

Conclusion

This study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.

Synaptic processes and immune-related pathways implicated in Tourette syndrome

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.

A principal component approach to improve association testing with polygenic risk scores

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS-PCA approach with a permutation test and an a priori selected p-value threshold. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

Statistical Methods in Genome-Wide Association Studies

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

Statistical Method Based on Bayes-Type Empirical Score Test for Assessing Genetic Association with Multilocus Genotype Data

Simultaneous testing of multiple genetic variants for association is widely recognized as a valuable complementary approach to single-marker tests. As such, principal component regression (PCR) has been found to have competitive power. We focus on exploring a robust test for an unknown genetic mode of all SNPs, an unknown Hardy-Weinberg equilibrium (HWE) in a population, and a large number of all SNPs. First, we propose a new global test by means of the use of codominant codes for all markers and PCR. The new global test is built on an empirical Bayes-type score statistic for testing marginal associations with each single marker. The new global test gains power by robustly exploiting the Hardy-Weinberg equilibrium in the control population and effectively using linkage disequilibrium among test markers. The new global test reduces to PCR when the genotype for each marker is coded as the number of minor alleles. This connection lends insight into the power of the new global test relative to PCR and some other popular multimarker test methods. Second, we propose a robust test method based on the new global test and the ordinary PCR test built on a prospective score statistic for testing marginal associations with each single marker when the genotype for each marker is coded as the number of minor alleles by taking the minimum p value of these two tests. Finally, through extensive simulation studies and analysis of the association between pancreatic cancer and some genes of interest, we show that the proposed robust test method has desirable power and can often identify association signals that may be missed by existing methods.

Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies

Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect measurements for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases.

Two-phase SSU and SKAT in genetic association studies

The sum of squared score (SSU) and sequence kernel association test (SKAT) are the two good alternative tests for genetic association studies in case-control data. Both SSU and SKAT are derived through assuming a dose-response model between the risk of disease and genotypes. However, in practice, the real genetic mode of inheritance is impossible to know. Thus, these two tests might losepower substantially as shown in simulation results when the genetic model is misspecified. Here, to make both the tests suitable in broad situations, we propose two-phase SSU (tpSSU) and two-phase SKAT (tpSKAT), where the Hardy-Weinberg equilibrium test is adopted to choose the genetic model in the first phase and the SSU and SKAT are constructed corresponding to the selected genetic model in the second phase. We found that both tpSSU and tpSKAT outperformed the original SSU and SKAT in most of our simulation scenarios. Byapplying tpSSU and tpSKAT to the study of type 2 diabetes data, we successfully identified some genes that have direct effects on obesity. Besides, we also detected the significant chromosomal region 10q21.22 in GAW16 rheumatoid arthritis dataset, with P<10^-6. These findings suggest that tpSSU and tpSKAT can be effective in identifying genetic variants for complex diseases in case-control association studies.

Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning

Imaging genetics has attracted significant interests in recent studies. Traditional work has focused on mass-univariate statistical approaches that identify important single nucleotide polymorphisms (SNPs) associated with quantitative traits (QTs) of brain structure or function. More recently, to address the problem of multiple comparison and weak detection, multivariate analysis methods such as the least absolute shrinkage and selection operator (Lasso) are often used to select the most relevant SNPs associated with QTs. However, one problem of Lasso, as well as many other feature selection methods for imaging genetics, is that some useful prior information, e.g., the hierarchical structure among SNPs, are rarely used for designing a more powerful model. In this paper, we propose to identify the associations between candidate genetic features (i.e., SNPs) and magnetic resonance imaging (MRI)-derived measures using a tree-guided sparse learning (TGSL) method. The advantage of our method is that it explicitly models the complex hierarchical structure among the SNPs in the objective function for feature selection. Specifically, motivated by the biological knowledge, the hierarchical structures involving gene groups and linkage disequilibrium (LD) blocks as well as individual SNPs are imposed as a tree-guided regularization term in our TGSL model. Experimental studies on simulation data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data show that our method not only achieves better predictions than competing methods on the MRI-derived measures of AD-related region of interests (ROIs) (i.e., hippocampus, parahippocampal gyrus, and precuneus), but also identifies sparse SNP patterns at the block level to better guide the biological interpretation.

Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

Group sparse reduced rank regression for neuroimaging genetic study

The neuroimaging genetic study usually needs to deal with high dimensionality of both brain imaging data and genetic data, so that often resulting in the issue of curse of dimensionality. In this paper, we propose a group sparse reduced rank regression model to take the relations of both the phenotypes and the genotypes for the neuroimaging genetic study. Specifically, we propose designing a graph sparsity constraint as well as a reduced rank constraint to simultaneously conduct subspace learning and feature selection. The group sparsity constraint conducts feature selection to identify genotypes highly related to neuroimaging data, while the reduced rank constraint considers the relations among neuroimaging data to conduct subspace learning in the feature selection model. Furthermore, an alternative optimization algorithm is proposed to solve the resulting objective function and is proved to achieve fast convergence. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method has superiority on predicting the phenotype data by the genotype data, than the alternative methods under comparison.

Studying the effects of haplotype partitioning methods on the RA-associated genomic results from the North American Rheumatoid Arthritis Consortium (NARAC) dataset

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Haplotype blocks methods plays a complementary role to the single-SNP approaches.

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CIT, FGT, SSLD, and single-SNP methods should be applied to discover the markers.

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Selection of the method used for the association has an impact on the biomarkers.

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SSLD method detected more significant SNPs than CIT, FGT, and single-SNP methods.

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The 383 SNPs discovered by all methods are significantly associated with RA.

The human genome, which includes thousands of genes, represents a big data challenge. Rheumatoid arthritis (RA) is a complex autoimmune disease with a genetic basis. Many single-nucleotide polymorphism (SNP) association methods partition a genome into haplotype blocks. The aim of this genome wide association study (GWAS) was to select the most appropriate haplotype block partitioning method for the North American Rheumatoid Arthritis Consortium (NARAC) dataset. The methods used for the NARAC dataset were the individual SNP approach and the following haplotype block methods: the four-gamete test (FGT), confidence interval test (CIT), and solid spine of linkage disequilibrium (SSLD). The measured parameters that reflect the strength of the association between the biomarker and RA were the P-value after Bonferroni correction and other parameters used to compare the output of each haplotype block method. This work presents a comparison among the individual SNP approach and the three haplotype block methods to select the method that can detect all the significant SNPs when applied alone. The GWAS results from the NARAC dataset obtained with the different methods are presented. The individual SNP, CIT, FGT, and SSLD methods detected 541, 1516, 1551, and 1831 RA-associated SNPs respectively, and the individual SNP, FGT, CIT, and SSLD methods detected 65, 156, 159, and 450 significant SNPs respectively, that were not detected by the other methods. Three hundred eighty-three SNPs were discovered by the haplotype block methods and the individual SNP approach, while 1021 SNPs were discovered by all three haplotype block methods. The 383 SNPs detected by all the methods are promising candidates for studying RA susceptibility. A hybrid technique involving all four methods should be applied to detect the significant SNPs associated with RA in the NARAC dataset, but the SSLD method may be preferred because of its advantages when only one method was used.

Accurate and Efficient P-value Calculation via Gaussian Approximation: a Novel Monte-Carlo Method

It is of fundamental interest in statistics to test the significance of a set of covariates. For example, in genome-wide association studies, a joint null hypothesis of no genetic effect is tested for a set of multiple genetic variants. The minimum p-value method, higher criticism, and Berk-Jones tests are particularly effective when the covariates with nonzero effects are sparse. However, the correlations among covariates and the non-Gaussian distribution of the response pose a great challenge towards the p-value calculation of the three tests. In practice, permutation is commonly used to obtain accurate p-values, but it is computationally very intensive, especially when we need to conduct a large amount of hypothesis testing. In this paper, we propose a Gaussian approximation method based on a Monte Carlo scheme, which is computationally more efficient than permutation while still achieving similar accuracy. We derive non-asymptotic approximation error bounds that could vanish in the limit even if the number of covariates is much larger than the sample size. Through real-genotype-based simulations and data analysis of a genome-wide association study of Crohn's disease, we compare the accuracy and computation cost of our proposed method, of permutation, and of the method based on asymptotic distribution.

POWERFUL TEST BASED ON CONDITIONAL EFFECTS FOR GENOME-WIDE SCREENING

This paper considers testing procedures for screening large genome-wide data, where we examine hundreds of thousands of genetic variants, e.g., single nucleotide polymorphisms (SNP), on a quantitative phenotype. We screen the whole genome by SNP sets and propose a new test that is based on conditional effects from multiple SNPs. The test statistic is developed for weak genetic effects and incorporates correlations among genetic variables, which may be very high due to linkage disequilibrium. The limiting null distribution of the test statistic and the power of the test are derived. Under appropriate conditions, the test is shown to be more powerful than the minimum p-value method, which is based on marginal SNP effects and is the most commonly used method in genome-wide screening. The proposed test is also compared with other existing methods, including the Higher Criticism (HC) test and the sequence kernel association test (SKAT), through simulations and analysis of a real genome data set. For typical genome-wide data, where effects of individual SNPs are weak and correlations among SNPs are high, the proposed test is more advantageous and clearly outperforms the other methods in the literature.

Genotype-phenotype association study via new multi-task learning model

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2, 1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2, 1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs.

Methods and Tools in Genome-wide Association Studies

Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.

Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers

In this paper, we propose a novel sparse regression method for Brain-Wide and Genome-Wide association study. Specifically, we impose a low-rank constraint on the weight coefficient matrix and then decompose it into two low-rank matrices, which find relationships in genetic features and in brain imaging features, respectively. We also introduce a sparse acyclic digraph with sparsity-inducing penalty to take further into account the correlations among the genetic variables, by which it can be possible to identify the representative SNPs that are highly associated with the brain imaging features. We optimize our objective function by jointly tackling low-rank regression and variable selection in a framework. In our method, the low-rank constraint allows us to conduct variable selection with the low-rank representations of the data; the learned low-sparsity weight coefficients allow discarding unimportant variables at the end. The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method could select the important SNPs to more accurately estimate the brain imaging features than the state-of-the-art methods.

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.

Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist-hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.

Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies

Principal component analysis (PCA) is a useful tool to identify important linear combination of correlated variables in multivariate analysis and has been applied to detect association between genetic variants and human complex diseases of interest. How to choose adequate number of principal components (PCs) to represent the original system in an optimal way is a key issue for PCA. Note that the traditional PCA, only using a few top PCs while discarding the other PCs, might significantly lose power in genetic association studies if all the PCs contain non-ignorable signals. In order to make full use of information from all PCs, Aschard and his colleagues have proposed a multi-step combined PCs method (named mCPC) recently, which performs well especially when several traits are highly correlated. However, the power superiority of mCPC has just been illustrated by simulation, while the theoretical power performance of mCPC has not been studied yet. In this work, we attempt to investigate theoretical properties of mCPC and further propose a novel and efficient strategy to combine PCs. Extensive simulation results confirm that the proposed method is more robust than existing procedures. A real data application to detect the association between gene TRAF1-C5 and rheumatoid arthritis further shows good performance of the proposed procedure.

Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection

The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.

Gene set analysis: A step-by-step guide

To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc.

EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles

We previously developed dmGWAS to search for dense modules in a human protein-protein interaction (PPI) network; it has since become a popular tool for network-assisted analysis of genome-wide association studies (GWAS). dmGWAS weights nodes by using GWAS signals. Here, we introduce an upgraded algorithm, EW_dmGWAS, to boost GWAS signals in a node- and edge-weighted PPI network. In EW_dmGWAS, we utilize condition-specific gene expression profiles for edge weights. Specifically, differential gene co-expression is used to infer the edge weights. We applied EW_dmGWAS to two diseases and compared it with other relevant methods. The results suggest that EW_dmGWAS is more powerful in detecting disease-associated signals.

Fine-mapping additive and dominant SNP effects using group-LASSO and fractional resample model averaging

Genomewide association studies (GWAS) sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple single-nucleotide polymorphisms (SNPs) simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow-up studies. Current multi-SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA-dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights. It estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing an SNP prioritization that best identifies underlying true signals, we show the following: our method easily outperforms a single-marker analysis; when additive-only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive-only effects; and when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation.

Modeling and testing for joint association using a genetic random field model

Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains largely unknown. Human complex diseases are likely influenced by the joint effect of a large number of genetic variants instead of a single variant. The joint analysis of multiple genetic variants considering linkage disequilibrium (LD) and potential interactions can further enhance the discovery process, leading to the identification of new disease-susceptibility genetic variants. Motivated by development in spatial statistics, we propose a new statistical model based on the random field theory, referred to as a genetic random field model (GenRF), for joint association analysis with the consideration of possible gene-gene interactions and LD. Using a pseudo-likelihood approach, a GenRF test for the joint association of multiple genetic variants is developed, which has the following advantages: (1) accommodating complex interactions for improved performance; (2) natural dimension reduction; (3) boosting power in the presence of LD; and (4) computationally efficient. Simulation studies are conducted under various scenarios. The development has been focused on quantitative traits and robustness of the GenRF test to other traits, for example, binary traits, is also discussed. Compared with a commonly adopted kernel machine approach, SKAT, as well as other more standard methods, GenRF shows overall comparable performance and better performance in the presence of complex interactions. The method is further illustrated by an application to the Dallas Heart Study.

HPA axis genetic variation, cortisol and psychosis in major depression

Genetic variation underlying hypothalamic pituitary adrenal (HPA) axis overactivity in healthy controls (HCs) and patients with severe forms of major depression has not been well explored, but could explain risk for cortisol dysregulation. In total, 95 participants were studied: 40 patients with psychotic major depression (PMD); 26 patients with non-psychotic major depression (NPMD); and 29 HCs. Collection of genetic material was added one third of the way into a larger study on cortisol, cognition and psychosis in major depression. Subjects were assessed using the Brief Psychiatric Rating Scale, the Hamilton Depression Rating Scale and the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders. Blood was collected hourly for determination of cortisol from 1800 to 0900 h and for the assessment of alleles for six genes involved in HPA axis regulation. Two of the six genes contributed significantly to cortisol levels, psychosis measures or depression severity. After accounting for age, depression and psychosis, and medication status, only allelic variation for the glucocorticoid receptor (GR) gene accounted for a significant variance for mean cortisol levels from 1800 to 0100 h (r(2)=0.288) and from 0100 to 0900 h (r(2)=0.171). In addition, GR and corticotropin-releasing hormone receptor 1 (CRHR1) genotypes contributed significantly to psychosis measures and CRHR1 contributed significantly to depression severity rating.

A novel computational biostatistics approach implies impaired dephosphorylation of growth factor receptors as associated with severity of autism

The prevalence of autism spectrum disorders (ASDs) has increased 20-fold over the past 50 years to >1% of US children. Although twin studies attest to a high degree of heritability, the genetic risk factors are still poorly understood. We analyzed data from two independent populations using u-statistics for genetically structured wide-locus data and added data from unrelated controls to explore epistasis. To account for systematic, but disease-unrelated differences in (non-randomized) genome-wide association studies (GWAS), a correlation between P-values and minor allele frequency with low granularity data and for conducting multiple tests in overlapping genetic regions, we present a novel study-specific criterion for 'genome-wide significance'. From recent results in a comorbid disease, childhood absence epilepsy, we had hypothesized that axonal guidance and calcium signaling are involved in autism as well. Enrichment of the results in both studies with related genes confirms this hypothesis. Additional ASD-specific variations identified in this study suggest protracted growth factor signaling as causing more severe forms of ASD. Another cluster of related genes suggests chloride and potassium ion channels as additional ASD-specific drug targets. The involvement of growth factors suggests the time of accelerated neuronal growth and pruning at 9-24 months of age as the period during which treatment with ion channel modulators would be most effective in preventing progression to more severe forms of autism. By extension, the same computational biostatistics approach could yield profound insights into the etiology of many common diseases from the genetic data collected over the last decade.

BDNF and CREB1 genetic variants interact to affect antidepressant treatment outcomes in geriatric depression

AIM

Brain-derived neurotrophic factor (BDNF) is associated with antidepressant response on the cellular level, in animal models, and in clinical studies. A common variant in the BDNF gene results in a substitution of a methionine (Met) for a valine at the amino acid position 66. Previous studies reported that the Met variant results in enhanced response to antidepressant medications. These findings may be at odds with studies indicating that on a cellular level the Met variant impairs the secretion of BDNF.

MATERIALS AND METHODS

We examined the effects of BDNF single nucleotide polymorphisms (SNPs) in response to the antidepressants paroxetine and mirtazapine in a sample of 246 geriatric patients with major depression, treated in a double-blind, randomized, 8-week clinical trial. We also examined the effects of genetic variation at the BDNF-related loci neurotrophic tyrosine kinase receptor 2, cyclic AMP responsive element binding protein 1 (CREB1), and CREB binding protein. A total of 53 SNPs were genotyped.

RESULTS

BDNF genetic variation had a significant effect on the efficacy of paroxetine, with patients carrying the Met allele showing impaired response. SNPs at the CREB1 locus, which encodes a transcription factor important in BDNF signaling, also predicted response to paroxetine. Furthermore, we found a significant gene-gene interaction between BDNF and CREB1 that affected response to paroxetine. Because BDNF has been associated with cognitive function, we tested the effects of BDNF SNPs on change in a wide variety of cognitive tests over the 8-week trial, but there were no significant effects of genotype on cognition.

CONCLUSION

These results provide new evidence for the importance of the BDNF pathway in antidepressant response in geriatric patients. The negative effect of the Met66 allele on antidepressant outcomes is consistent with basic science findings indicating a negative effect of this variant on BDNF activity in the brain. Further, the effect of BDNF genetic variation on antidepressant treatment is modified by variation in the gene encoding the downstream effector CREB1.

Integrative gene set analysis: application to platinum pharmacogenomics

Integrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions for cancer patients.

Incorporating prior knowledge to increase the power of genome-wide association studies

Typical methods of analyzing genome-wide single nucleotide variant (SNV) data in cases and controls involve testing each variant's genotypes separately for phenotype association, and then using a substantial multiple-testing penalty to minimize the rate of false positives. This approach, however, can result in low power for modestly associated SNVs. Furthermore, simply looking at the most associated SNVs may not directly yield biological insights about disease etiology. SNVset methods attempt to address both limitations of the traditional approach by testing biologically meaningful sets of SNVs (e.g., genes or pathways). The number of tests run in a SNVset analysis is typically much lower (hundreds or thousands instead of millions) than in a traditional analysis, so the false-positive rate is lower. Additionally, by testing SNVsets that are biologically meaningful finding a significant set may more quickly yield insights into disease etiology.In this chapter we summarize the short history of SNVset testing and provide an overview of the many recently proposed methods. Furthermore, we provide detailed step-by-step instructions on how to perform a SNVset analysis, including a substantial number of practical tips and questions that researchers should consider before undertaking a SNVset analysis. Lastly, we describe a companion R package (snvset) that implements recently proposed SNVset methods. While SNVset testing is a new approach, with many new methods still being developed and many open questions, the promise of the approach is worth serious consideration when considering analytic methods for GWAS.

Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies

Although Genome Wide Association Studies (GWAS) have identified many susceptibility loci for common diseases, they only explain a small portion of heritability. It is challenging to identify the remaining disease loci because their association signals are likely weak and difficult to identify among millions of candidates. One potentially useful direction to increase statistical power is to incorporate functional genomics information, especially gene expression networks, to prioritize GWAS signals. Most current methods utilizing network information to prioritize disease genes are based on the 'guilt by association' principle, in which networks are treated as static, and disease-associated genes are assumed to locate closer with each other than random pairs in the network. In contrast, we propose a novel 'guilt by rewiring' principle. Studying the dynamics of gene networks between controls and patients, this principle assumes that disease genes more likely undergo rewiring in patients, whereas most of the network remains unaffected in disease condition. To demonstrate this principle, we consider the changes of co-expression networks in Crohn's disease patients and controls, and how network dynamics reveals information on disease associations. Our results demonstrate that network rewiring is abundant in the immune system, and disease-associated genes are more likely to be rewired in patients. To integrate this network rewiring feature and GWAS signals, we propose to use the Markov random field framework to integrate network information to prioritize genes. Applications in Crohn's disease and Parkinson's disease show that this framework leads to more replicable results, and implicates potentially disease-associated pathways.

On multi-marker tests for association in case-control studies

Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we establish a theoretical benchmark by quantifying the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (2007) based on the maximum absolute test statistic observed in an LD block is a practical and powerful method in a variety of settings. We also explore the effect on the power that prior biological or functional knowledge used to narrow down the locus of the causal marker can have, and conclude that this prior knowledge has to be very strong and specific for the power to approach the maximum achievable level, or even beat the power observed for methods such as the one proposed by Conneely and Boehnke (2007).

A rapid gene-based genome-wide association test with multivariate traits

OBJECTIVES

A gene-based genome-wide association study (GWAS) provides a powerful alternative to the traditional single single nucleotide polymorphism (SNP) association analysis due to its substantial reduction in the multiple testing burden and possible gain in power due to modeling multiple SNPs within a gene. A gene-based association analysis on multivariate traits is often of interest, but it imposes substantial analytical as well as computational challenges to implement it at a genome-wide level.

METHODS

We propose a rapid implementation of the multivariate multiple linear regression (RMMLR) approach in unrelated individuals as well as in families. Our approach allows for covariates. Moreover, the asymptotic distribution of the test statistic is not heavily influenced by the linkage disequilibrium (LD) among the SNPs and hence can be used efficiently to perform a gene-based GWAS. We have developed a corresponding R package to implement such multivariate gene-based GWAS with this RMMLR approach.

RESULTS

Through extensive simulation, we compared several approaches for both single and multivariate traits. Our RMMLR approach maintained a correct type I error level even for sets of SNPs in strong LD. It also demonstrated a substantial gain in power to detect a gene when it is associated with a subset of the traits. We also studied performances of the approaches on the Minnesota Center for Twin Family Research dataset.

CONCLUSIONS

In our overall comparison, our RMMLR approach provides an efficient and powerful tool to perform a gene-based GWAS with single or multivariate traits and maintains the type I error appropriately.

Gene-environment interactions in genome-wide association studies: current approaches and new directions

BACKGROUND

Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge.

METHODS

We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually.

RESULTS

Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research.

CONCLUSIONS

Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting loci

Expression quantitative trait loci (eQTL) studies have generated large amounts of data in different organisms. The analyses of these data have led to many novel findings and biological insights on expression regulations. However, the role of epistasis in the joint regulation of multiple genes has not been explored. This is largely due to the computational complexity involved when multiple traits are simultaneously considered against multiple markers if an exhaustive search strategy is adopted. In this article, we propose a computationally feasible approach to identify pairs of chromosomal regions that interact to regulate co-expression patterns of pairs of genes. Our approach is built on a bivariate model whose covariance matrix depends on the joint genotypes at the candidate loci. We also propose a filtering process to reduce the computational burden. When we applied our method to a yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 225 and 224 modules, with each module consisting of two genes and two eQTLs where the two eQTLs epistatically regulate the co-expression patterns of the two genes. We found that many of these modules have biological interpretations. Under the glucose condition, ribosome biogenesis was co-regulated with the signaling and carbohydrate catabolic processes, whereas silencing and aging related genes were co-regulated under the ethanol condition with the eQTLs containing genes involved in oxidative stress response process.

eQTL studies collect both gene expression and genotype data, and they are highly informative as to how genes regulate expressions. Although much progress has been made in the analysis of such data, most studies have considered one marker at a time. As a result, those markers with weak marginal yet strong interactive effects may not be inferred from these single-marker-based analyses. In this article, using joint expression patterns between two genes (versus one gene) as the primary phenotype, we propose a novel statistical method to conduct an exhaustive search for joint marker analysis. When our method is applied to a well-studied dataset, we were able to identify many novel features that were overlooked by existing methods. Our general strategy has general applicability to other scientific problems.

From single-SNP to wide-locus: genome-wide association studies identifying functionally related genes and intragenic regions in small sample studies

BACKGROUND

Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts.

METHODS

Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (µStat) are not confounded by unrealistic assumptions (e.g., linearity, independence).

RESULTS

By incorporating knowledge about relationships between SNPs, µGWAS (GWAS based on µStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes.

CONCLUSION

With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development.

Identifying the genetic variation of gene expression using gene sets: application of novel gene Set eQTL approach to PharmGKB and KEGG

Genetic variation underlying the regulation of mRNA gene expression in humans may provide key insights into the molecular mechanisms of human traits and complex diseases. Current statistical methods to map genetic variation associated with mRNA gene expression have typically applied standard linkage and/or association methods; however, when genome-wide SNP and mRNA expression data are available performing all pair wise comparisons is computationally burdensome and may not provide optimal power to detect associations. Consideration of different approaches to account for the high dimensionality and multiple testing issues may provide increased efficiency and statistical power. Here we present a novel approach to model and test the association between genetic variation and mRNA gene expression levels in the context of gene sets (GSs) and pathways, referred to as gene set - expression quantitative trait loci analysis (GS-eQTL). The method uses GSs to initially group SNPs and mRNA expression, followed by the application of principal components analysis (PCA) to collapse the variation and reduce the dimensionality within the GSs. We applied GS-eQTL to assess the association between SNP and mRNA expression level data collected from a cell-based model system using PharmGKB and KEGG defined GSs. We observed a large number of significant GS-eQTL associations, in which the most significant associations arose between genetic variation and mRNA expression from the same GS. However, a number of associations involving genetic variation and mRNA expression from different GSs were also identified. Our proposed GS-eQTL method effectively addresses the multiple testing limitations in eQTL studies and provides biological context for SNP-expression associations.

Modelling the contribution of family history and variation in single nucleotide polymorphisms to risk of schizophrenia: a Danish national birth cohort-based study

BACKGROUND

Epidemiological studies indicate that having any family member with schizophrenia increases the risk of schizophrenia in the probands. However, genome-wide association studies (GWAS) have accounted for little of this variation. The aim of this study was to use a population-based sample to explore the influence of single-nucleotide polymorphisms (SNPs) on the excess schizophrenia risk in offspring of parents with a psychotic, bipolar affective or other psychiatric disorder.

METHOD

A nested case-control study with 739 cases with schizophrenia and 800 controls. Their parents and siblings. Information from national health registers and GWAS data from the national neonatal biobank.

RESULTS

Offspring schizophrenia risk was elevated in those whose mother, father or siblings had been diagnosed with schizophrenia or related psychosis, bipolar affective disorder or any other psychiatric disorder. The rate ratio was 9.31 (3.85; 22.44) in offspring whose 1st degree relative was diagnosed with schizophrenia. This rate ranged between 8.31 and 11.34 when adjusted for each SNP individually and shrank to 8.23 (3.13; 21.64) when adjusted for 25% of the SNP-variation in candidate genes. The percentage of the excess risk associated with a family history of schizophrenia mediated through genome-wide SNP-variation ranged between -6.1%(-17.0%;2.6%) and 4.1%(-3.9%;15.2%). Analogous results were seen for each parent and for histories of bipolar affective and other psychiatric diagnoses.

CONCLUSIONS

The excess risk of schizophrenia in offspring of parents who have a psychotic, bipolar affective or other psychiatric disorder is not currently explained by the SNP variation included in this study in accordance with findings from published genetic studies.

Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort

MOTIVATION

Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation.

RESULTS

Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group ℓ(2,1)-norm (G(2,1)-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G(2,1)-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an ℓ(2,1)-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs.

AVAILABILITY

Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.

On the Aggregation of Multimarker Information for Marker-Set and Sequencing Data Analysis: Genotype Collapsing vs. Similarity Collapsing

Methods that collapse information across genetic markers when searching for association signals are gaining momentum in the literature. Although originally developed to achieve a better balance between retaining information and controlling degrees of freedom when performing multimarker association analysis, these methods have recently been proven to be a powerful tool for identifying rare variants that contribute to complex phenotypes. The information among markers can be collapsed at the genotype level, which focuses on the mean of genetic information, or the similarity level, which focuses on the variance of genetic information. The aim of this work is to understand the strengths and weaknesses of these two collapsing strategies. Our results show that neither collapsing strategy outperforms the other across all simulated scenarios. Two factors that dominate the performance of these strategies are the signal-to-noise ratio and the underlying genetic architecture of the causal variants. Genotype collapsing is more sensitive to the marker set being contaminated by noise loci than similarity collapsing. In addition, genotype collapsing performs best when the genetic architecture of the causal variants is not complex (e.g., causal loci with similar effects and similar frequencies). Similarity collapsing is more robust as the complexity of the genetic architecture increases and outperforms genotype collapsing when the genetic architecture of the marker set becomes more sophisticated (e.g., causal loci with various effect sizes or frequencies and potential non-linear or interactive effects). Because the underlying genetic architecture is not known a priori, we also considered a two-stage analysis that combines the two top-performing methods from different collapsing strategies. We find that it is reasonably robust across all simulated scenarios.

Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.