a method of combining the data of different studies together to improve the statistical power of the results; used in GWAS by identifying candidate SNPs and then confirming their impact through replication studies

type II diabetes meta-analysis consortium, combining data from smaller regional studies to identify SNPs with association with diabetes

a plot used to show significant SNPs in GWA studies

an SNP with the highest significance of the imputed SNPs; a single SNP identifying a group of SNPs that are likely associated together with the condition of interest

genomic locus associated with phenotypic variation of a specific, quantifiable trait, measured by gene expression

a score of different genetic variants on an individual’s genetic risk, predicting the likelihood of a given trait (such as a disease) from trait-associated alleles

the difference in heritability estimates from genetic data and twin/family studies

genetic variance that is below the threshold for significance in GWA thresholds, but has an impact on the condition; not yet identified but could if the ‘noise’ in GWAS data can be reduced

method asking how effective changing the p-value in a GWAS impacts the accuracy of prediction, while not including too much background noise to decrease accuracy

by splitting GWAS participants into two groups, data analyses can be conducted independently, allowing false positives to be identified as they are randomly allocated between the groups; genuine effect SNPs should be more common than the false positives between the two groups

by pairing every participant together in a GWAS, the genetic and phenotypic data can be compared, identifying the relationship between genetic variants and phenotype

the gap between heritability estimates and heritability identified with GWA studies; not (yet) found