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Genome wide association studies (GWAS) are hypothesis-free methods for identifying associations between genetic regions and traits. GWAS analysis are usually used to identify genes involved in human disease.
By applying GWAS analysis to variant data we will be able to identify a given variant (or a set of variants) involved in a given phenotype or disorder. Based on a statistical test, GWAS analysis will provide a level of significance (or p-value) for each variant.
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#chromosome start end strand reference alternate dbSNP gene biotype conseq. types chi square p-value odd ratio 22 16054454 16054454 + C T rs373998521 intergenic_variant 2.4727272727272727 0.11583677431831574 0.0 22 16065809 16065809 + T C ENSG00000233866 lincRNA downstream_gene_variant 0.053968253968253915 0.8162967146689325 0.8 22 16065809 16065809 + T C regulatory_region_variant 0.053968253968253915 0.8162967146689325 0.8 22 16077310 16077310 + T A ENSG00000229286 unprocessed_pseudogene 2KB_upstream_variant 0.9714285714285711 0.3243241555798487 3.0 22 16077310 16077310 + T A regulatory_region_variant 0.9714285714285711 0.3243241555798487 3.0 22 16080499 16080499 + A G rs200119791 ENSG00000229286 unprocessed_pseudogene upstream_gene_variant 1.8888888888888886 0.16932729721206297 Infinity 22 16080499 16080499 + A G rs200119791 ENSG00000235265 unprocessed_pseudogene downstream_gene_variant 1.8888888888888886 0.16932729721206297 Infinity 22 16084621 16084621 + T C ENSG00000235265 unprocessed_pseudogene non_coding_transcript_exon_variant,non_coding_transcript_variant 2.4425287356321843 0.11808572685033702 Infinity |
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