Significance testing, with appropriate multiple testing correction, is currently the most convenient method for summarizing the evidence for association between a disease and a genetic variant.
Inadequate statistical power increases not only the probability of missing genuine associations but also the probability that significant associations represent false-positive findings.
Statistical power declines rapidly with decreasing allele frequency and effect size, but it can be enhanced by increasing sample size and by selecting appropriate subjects (for example, family history positive cases and 'super normal' controls).
Exome sequencing studies can often identify the mutation responsible for a Mendelian disease by filtering out common variants, synonymous variants or variants that do not co-segregate with disease, and then assigning priority to the remaining variants using bioinformatic tools.
Adequate statistical power for rare-variant association analyses in complex diseases requires the aggregation of the effects of multiple rare variants within a defined portion of the genome (for example, a set of related genes).
Various computational tools are available for calculating the statistical power of genetic studies.
Significance testing was developed as an objective method for summarizing statistical evidence for a hypothesis. It has been widely adopted in genetic studies, including genome-wide association studies and, more recently, exome sequencing studies. However, significance testing in both genome-wide and exome-wide studies must adopt stringent significance thresholds to allow multiple testing, and it is useful only when studies have adequate statistical power, which depends on the characteristics of the phenotype and the putative genetic variant, as well as the study design. Here, we review the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.
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This work was supported by The University of Hong Kong Strategic Research Theme on Genomics; Hong Kong Research Grants Council (HKRGC) General Research Funds 777511M, 776412M and 776513M; HKRGC Theme-Based Research Scheme T12-705/11 and T12-708/12-N; and the European Community Seventh Framework Programme Grant on European Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI); and the US National Institutes of Health grants R01 MH099126 and R01 HG005827 (to S.M.P.). The authors thank R. Porsch and S.-W. Choi for technical assistance with the manuscript.
The authors declare no competing financial interests.
Probabilities (or probability densities) of observed data under an assumed statistical model as a function of model parameters.
- Family-wise error rate
(FWER). The probability of at least one false-positive significant finding from a family of multiple tests when the null hypothesis is true for all the tests.
- C-alpha test
A rare-variant association test based on the distribution of variants in cases and controls (that is, whether such a distribution has inflated variance compared with a binomial distribution).
- Sequence kernel association test
(SKAT). A test based on score statistics for testing the association of rare variants from sequence data with either a continuous or a discontinuous genetic trait.
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Sham, P., Purcell, S. Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet 15, 335–346 (2014). https://doi.org/10.1038/nrg3706
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