An Overview of Genome-Wide Association Study for Genetics Novices: A Review

Hafsa Tahir, Aniqa Ejaz, Tania Mahmood, Sidra Riaz, Rashid Saif

Abstract


SNP chip-based genome-wide association studies (GWAS) is an inspiring and fast scanning method for mapping variations within the genome and associating them with specific diseases/trait. This association information has enhanced the chances of improvement in disease diagnosis, understanding the causative variants locations and associated gene hunting strategies. GWAS have laid foundation of an era in which both personalized medicine and pharmacogenomics would be reinforced along with better understanding of functional genomics aspects of modern molecular genetics. Since the advent of first GWAS in 2002, thousands of genome wide association studies have been published which have proven GWAS a successful methodology in identifying significant variants in disease/trait association but application of GWAS outcomes to clinical settings demands more evaluation for validity. Here, we have divided the GWAS approach into various aspects including history, development, analysis strategies, approaches, current scenario and different applications with brief description of major methodologies being used in scientific community to get associated SNP hits and narrowing down the search by functional variant filtration involved in subject disease, traits or physiological condition.


Keywords: GWAS, Genetic Association, Linkage Disequilibrium, HapMap, PLINK


Full Text:

PDF

References


International HapMap Consortium. A haplotype map of the human genome. Nature, (2005); 437(7063): 1299-320.

International HapMap Consortium, Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, et al. A second-generation human haplotype map of over 3.1 million SNPs. Nature, (2007); 449(7164): 851-61.

International HapMap 3 Consortium1, Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, Schaffner SF, et al. Integrating common and rare genetic variation in diverse human populations. Nature. (2010); 467(7311): 52-8.

Bush WS, Moore JH. Genome-wide association studies. PLoS Computational Biology, (2012); 8(12): e1002822.

Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics, (2005); 6(2):95-108.

Lee SH, Wray NR. Novel genetic analysis for case-control genome-wide association studies: quantification of power and genomic prediction accuracy. PLoS One, (2013); 8(8): e71494.

Yang J, Wray NR, Visscher PM. Comparing apples and oranges: equating the power of case-control and quantitative trait association studies. Genetic Epidemiology, (2010); 34(3): 254-7.

Ragoussis J. Genotyping technologies for genetic research. Annual Review of Genomics and Human Genetics, (2009); 10: 117-33.

Distefano J, Taverna D. Technological issues and experimental design of gene association studies. Methods in Molecular Biology, (2011); 700: 3-16.

Madore A, Laprise C. Immunological and genetic aspects of asthma and allergy. Journal of Asthma Allergy, (2010); 3: 107-21.

Hanke C, Waide S, Kettler R, et al. Monitoring induced gene expression of single cells in a multilayer microchip. Analytical and Bioanalytical Chemistry, (2012); 402(8): 2577-2585.

Trevino V, Falciani F, Barrera-Saldaña HA. DNA microarrays: a powerful genomic tool for biomedical and clinical research. Molecular Medicine, (2007); 13(9-10): 527-41.

Reed E, Nunez S, Kulp D, Qian J, Reilly MP, Foulkes AS. A guide to genome-wide association analysis and post-analytic interrogation. Statistics in Medicine, (2015); 34(28): 3769-92.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, (2007); 81(3): 559-75.

Howie B, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics, (2009); 5(6): e1000529.

DataPre-processing, http://www.stat-gen.org/tut/tut_preproc.html, 9-05-2019.

Ikram M, Sim X, Xueling S, Jensen R, Cotch M, Hewitt A, et al. Four novel Loci (19q13, 6q24, 12q24, and 5q14) influence the microcirculation in vivo. PLoS Genetics, (2010); 6(10): e1001184.

Nakajima M, Takahashi A, Kou I, Rodriguez-Fontenla C, Gomez-Reino J, et al. New Sequence Variants in HLA Class II/III Region Associated with Susceptibility to Knee Osteoarthritis Identified by Genome-Wide Association Study. PLoS ONE, (2010); 5(3): e9723.

Husby A, Kawakami T, Ronnegard L, Smeds L, Ellegren H, Qvarnstrom A. Genome-wide association mapping in a wild avian population identifies a link between genetic and phenotypic variation in a life-history trait. Proceedings of the Royal Society B: Biological Sciences, (2015); 282(1806): 20150156.

Evangelou E, Ioannidis JP. Meta-analysis methods for genome-wide association studies and beyond. Nature Reviews Genetics, (2013); 14(6): 379-89.

Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genome wide association scans. Bioinformatics, (2010); 26(17): 2190–2191.

Chaimani A, Mavridis D, Salanti G. A hands-on practical tutorial on performing meta-analysis with Stata. Evidence-Based Mental Health, (2014); 17: 111-116.

Sanna S, Jackson AU, Nagaraja R, Willer CJ, Chen WM, et al. Common variants in the GDF5-UQCC region are associated with variation in human height. Nature Genetics, (2008); 40: 198–203.

Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nature Genetics, (2008); 40: 161–169.

Nishizaki SS, Boyle AP. Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms. Trends in Genetics, (2016); 33(1): 34–45.

Cirillo E, Kutmon M, Gonzalez Hernandez M, Hooimeijer T, Adriaens ME, Eijssen LMT, et al. From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results. PLoS One, (2018); 13(4): e0193515.

Zollanvari A, Alterovitz G. SNP by SNP by environment interaction network of alcoholism. BMC Systems Biology, (2017); 11(3): 19.

Lin WY, Huang CC, Liu YL, Tsai SJ, Kuo PH. Polygenic approaches to detect gene-environment interactions when external information is unavailable. Briefings in Bioinformatics, (2018). 10.1093/bib/bby086.

Hernández F, Ávila J. Commentary: Genome-wide association study identifies 74 loci associated with educational attainment. Frontiers in Molecular Neuroscience, (2017); 10: 23.

Okbay A, Beauchamp JP, Fontana MA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature, (2016); 533(7604): 539–542.


Refbacks

  • There are currently no refbacks.