publication . Article . Preprint . Other literature type . 2019

A general statistic to test an optimally weighted combination of common and/or rare variants

Zhang, Jianjun; Wu, Baolin; Sha, Qiuying; Zhang, Shuanglin; Wang, Xuexia;
Open Access
  • Published: 09 Mar 2019 Journal: Genetic Epidemiology, volume 43, pages 966-979 (issn: 0741-0395, eissn: 1098-2272, Copyright policy)
  • Publisher: Wiley
<jats:title>Abstract</jats:title><jats:p>Both genome-wide association study and next generation sequencing data analyses are widely employed in order to identify disease susceptible common and/or rare genetic variants in many large scale genetic studies. Rare variants generally have large effects though they are hard to detect due to their low frequency. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some ad hoc assumptions (e.g. ignoring dependence between rare variants). In this study, we analytically derive optimal wei...
free text keywords: Genetics(clinical), Epidemiology, Genetic association, Biology, Statistic, Statistics, Schizophrenia, medicine.disease, medicine, FYN, Type I and type II errors, Genetics, Uniformly most powerful test, Covariate, Genetic analysis
Funded by
NIH| Genetic Analysis of Common Diseases: An Evaluation
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01GM031575-22
NIH| Statistical Methods for Rare Variant Association Studies
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R15HG008209-01A1
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publication . Article . Preprint . Other literature type . 2019

A general statistic to test an optimally weighted combination of common and/or rare variants

Zhang, Jianjun; Wu, Baolin; Sha, Qiuying; Zhang, Shuanglin; Wang, Xuexia;