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1. HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS

   https://doi.org/10.1093/bioinformatics/btaa589  

   Slaten, Marianne L; Chan, Yen On; Shrestha, Vivek; Lipka, Alexander E; Angelovici, Ruthie (June 2020, Bioinformatics)

   Luigi Martelli, Pier (Ed.)

   Abstract Motivation Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis, such as outlier removal, data transformation and calculation of Best Linear Unbiased Predictions or Best Linear Unbiased Estimates. In addition, post-GWAS analysis, such as haploblock analysis and candidate gene identification, is lacking. Results Here, we present Holistic Analysis with Pre- and Post-Integration (HAPPI) GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeline using the command-line interface. Availability and implementation HAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). Supplementary information Supplementary data are available at Bioinformatics online. 

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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)

   Abstract MotivationMany 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. ResultsHere, 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. Availability and implementationOur source code is available at https://github.com/lgai/CONFIT. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS

   https://doi.org/10.1093/g3journal/jkad246  

   Corut, Adnan Kivanc; Wallace, Jason G (November 2023, G3: Genes, Genomes, Genetics)

   Lipka, A (Ed.)

   Abstract Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow). 

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4. Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants

   https://doi.org/10.1093/bioinformatics/btab514  

   Song, Shuang; Shan, Nayang; Wang, Geng; Yan, Xiting; Liu, Jun S; Hou, Lin (July 2021, Bioinformatics)

   Schwartz, Russell (Ed.)

   Abstract Motivation Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. Results We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. Availability and implementation The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. Statistical Methods in Genome-Wide Association Studies

   https://doi.org/10.1146/annurev-biodatasci-030320-041026  

   Sun, Ning; Zhao, Hongyu (July 2020, Annual Review of Biomedical Data Science)

   null (Ed.)

   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. 

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