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342(6155):253–257īeck T, Shorter T, Brookes AJ (2019) GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies. (2013) An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. ICWSM.īauer DE, Kamran SC, Lessard S, Xu J, Fujiwara Y, Lin C et al.
#HUMAN ANATOMY ATLAS 2018 PC SOFTWARE#
Nat Genet 53(2):195–204īastian M, Heymann S, Jacomy M (2009) Gephi: An Open Source Software for Exploring and Manipulating Networks. (2021) Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Science 360:1313Ītkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ, Santoro ML et al. (2018) Analysis of shared heritability in common disorders of the brain. Perspect Health Inf Manag 14 (Winter): 1-8.Īnttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, Brainstorm Consortium. Sci Rep 9(1):3087Īl-Hablani B (2017) The Use of Automated SNOMED CT Clinical Coding in Clinical Decision Support Systems for Preventive Care. Heredity 124:525–534Īhn J, Wu H, Lee K (2019) Integrative Analysis Revealing Human Adipose-Specific Genes and Consolidating Obesity Loci. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.Īgrawal R, Prabakaran S (2020) Big data in digital healthcare: lessons learnt and recommendations for general practice. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations these challenges can be overcome using post-GWAS analysis. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs).