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1. OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

   https://doi.org/10.1038/s41598-018-32876-3  

   Boudellioua, Imane ; Kulmanov, Maxat ; Schofield, Paul N. ; Gkoutos, Georgios V. ; Hoehndorf, Robert ( October 2018 , Scientific Reports)

   An increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene–phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.

    

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2. Finding associated variants in genome-wide association studies on multiple traits

   https://doi.org/10.1093/bioinformatics/bty249  

   Gai, Lisa ; Eskin, Eleazar ( June 2018 , Bioinformatics)

   Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. There is currently a wealth of GWAS data collected in numerous phenotypes, and analyzing multiple traits at once can increase power to detect shared variant effects. However, traditional meta-analysis methods are not suitable for combining studies on different traits. When applied to dissimilar studies, these meta-analysis methods can be underpowered compared to univariate analysis. The degree to which traits share variant effects is often not known, and the vast majority of GWAS meta-analysis only consider one trait at a time.

   Here, we present a flexible method for finding associated variants from GWAS summary statistics for multiple traits. Our method estimates the degree of shared effects between traits from the data. Using simulations, we show that our method properly controls the false positive rate and increases power when an effect is present in a subset of traits. We then apply our method to the North Finland Birth Cohort and UK Biobank datasets using a variety of metabolic traits and discover novel loci.

   Our source code is available at https://github.com/lgai/CONFIT.

   Supplementary data are available at Bioinformatics online.

    

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3. Regional imaging genetic enrichment analysis

   https://doi.org/10.1093/bioinformatics/btz948  

   Yao, Xiaohui ; Cong, Shan ; Yan, Jingwen ; Risacher, Shannon L. ; Saykin, Andrew J. ; Moore, Jason H. ; Shen, Li ; UK Brain Expression Consortium ; for the Alzheimer’s Disease Neuroimaging Initiative ; Murphy, ed., Robert ( December 2019 , Bioinformatics)

   Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals.

   In this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer’s Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain.

   The R code and sample data are freely available at https://github.com/lshen/RIGEA.

   Supplementary data are available at Bioinformatics online.

    

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4. Efficient and effective control of confounding in eQTL mapping studies through joint differential expression and Mendelian randomization analyses

   https://doi.org/10.1093/bioinformatics/btaa715  

   Fan, Yue ; Zhu, Huanhuan ; Song, Yanyi ; Peng, Qinke ; Zhou, Xiang ( February 2021 , Bioinformatics)

   Robinson, Peter (Ed.)

   Identifying cis-acting genetic variants associated with gene expression levels—an analysis commonly referred to as expression quantitative trait loci (eQTLs) mapping—is an important first step toward understanding the genetic determinant of gene expression variation. Successful eQTL mapping requires effective control of confounding factors. A common method for confounding effects control in eQTL mapping studies is the probabilistic estimation of expression residual (PEER) analysis. PEER analysis extracts PEER factors to serve as surrogates for confounding factors, which is further included in the subsequent eQTL mapping analysis. However, it is computationally challenging to determine the optimal number of PEER factors used for eQTL mapping. In particular, the standard approach to determine the optimal number of PEER factors examines one number at a time and chooses a number that optimizes eQTLs discovery. Unfortunately, this standard approach involves multiple repetitive eQTL mapping procedures that are computationally expensive, restricting its use in large-scale eQTL mapping studies that being collected today.

   Here, we present a simple and computationally scalable alternative, Effect size Correlation for COnfounding determination (ECCO), to determine the optimal number of PEER factors used for eQTL mapping studies. Instead of performing repetitive eQTL mapping, ECCO jointly applies differential expression analysis and Mendelian randomization analysis, leading to substantial computational savings. In simulations and real data applications, we show that ECCO identifies a similar number of PEER factors required for eQTL mapping analysis as the standard approach but is two orders of magnitude faster. The computational scalability of ECCO allows for optimized eQTL discovery across 48 GTEx tissues for the first time, yielding an overall 5.89% power gain on the number of eQTL harboring genes (eGenes) discovered as compared to the previous GTEx recommendation that does not attempt to determine tissue-specific optimal number of PEER factors.

   Our method is implemented in the ECCO software, which, along with its GTEx mapping results, is freely available at www.xzlab.org/software.html. All R scripts used in this study are also available at this site.

   Supplementary data are available at Bioinformatics online.

    

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5. Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome

   https://doi.org/10.1002/pld3.139  

   Fikas, Alexandra Asaro ; Dilkes, Brian P. ; Baxter, Ivan ( May 2019 , Plant Direct)

   The integrated responses of biological systems to genetic and environmental variation result in substantial covariance in multiple phenotypes. The resultant pleiotropy, environmental effects, and genotype‐by‐environmental interactions (GxE) are foundational to our understanding of biology and genetics. Yet, the treatment of correlated characters, and the identification of the genes encoding functions that generate this covariance, has lagged. As a test case for analyzing the genetic basis underlying multiple correlated traits, we analyzed maize kernel ionomes from Intermated B73 x Mo17 (IBM) recombinant inbred populations grown in 10 environments. Plants obtain elements from the soil through genetic and biochemical pathways responsive to physiological state and environment. Most perturbations affect multiple elements which leads theionome, the full complement of mineral nutrients in an organism, to vary as an integrated network rather than a set of distinct single elements. We compared quantitative trait loci (QTL) determining single‐element variation toQTLthat predict variation in principal components (PCs) of multiple‐element covariance. Single‐element and multivariate approaches detected partially overlapping sets of loci.QTLinfluencing trait covariation were detected at loci that were not found by mapping single‐element traits. Moreover, this approach permitted testing environmental components of trait covariance, and identified multi‐element traits that were determined by both genetic and environmental factors as well as genotype‐by‐environment interactions. Growth environment had a profound effect on the elemental profiles and multi‐element phenotypes were significantly correlated with specific environmental variables.

    

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