Journal Article

Statistical power and significance testing in large-scale genetic studies

Sham, Pak C., Purcell, Shaun M.

2014

Overview

Key Points 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. This Review discusses the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants. 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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/114/2415 / 631/208/205/2138 / Agriculture / Animal Genetics and Genomics / Biomedicine / Cancer Research / Case-Control Studies / Data Interpretation, Statistical / Gene Frequency / Gene Function / Genetic Predisposition to Disease - genetics / Genetic Testing - statistics & numerical data / Genetic variation / Genetics / Genome-wide association studies / Genome-Wide Association Study - standards / Genome-Wide Association Study - statistics & numerical data / Genomes / Genotype / Genotype & phenotype / Human Genetics / Humans / Hypotheses / Hypothesis testing / Identification and classification / Methods / Polymorphism, Single Nucleotide / Probability / review-article / Sequence Analysis, DNA - methods / Sequence Analysis, DNA - statistics & numerical data / Statistical analysis / Statistical methods / Statistical power