Implications of small effect sizes of individual genetic variants on the design and interpretation of genetic association studies of complex diseases.
Accumulated evidence from searching for candidate gene-disease associations of complex diseases can offer some insights as the field moves toward discovery-oriented approaches with massive genome-wide testing. Meta-analyses of 50 non-human lymphocyte antigen gene-disease associations with documented overall statistical significance (752 studies) show summary odds ratios with a median of 1.43 (interquartile range, 1.28-1.65). Many different biases may operate in this field, for both single studies and meta-analyses, and these biases could invalidate some of these seemingly "validated" associations. Studies with a sample size of >500 show a median odds ratio of only 1.15. The median sample size required to detect the observed summary effects in each population addressed in the 752 studies is estimated to be 3,535 (interquartile range, 1,936-9,119 for cases and controls combined). These estimates are steeply inflated in the presence of modest bias. Population heterogeneity, as well as gene-gene and gene-environment interactions, could steeply increase these estimates and may be difficult to address even by very large biobanks and observational cohorts. The one visible solution is for a large number of teams to join forces on the same research platforms. These collaborative studies ideally should be designed up front to also assess more complex gene-gene and gene-environment interactions.
Ioannidis
John P A
JP
Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. jioannid@cc.uoi.gr
Trikalinos
Thomas A
TA
Khoury
Muin J
MJ
D000483:Alleles; D003627:Data Interpretation, Statistical; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005828:Genetics, Population; D015894:Genome, Human; D006801:Humans; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide; D012107:Research Design; D012307:Risk Factors