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  • 标题:Sample size requirements to detect the effect of a group of genetic variants in case-control studies
  • 本地全文:下载
  • 作者:Ramal Moonesinghe ; Quanhe Yang ; Muin J Khoury
  • 期刊名称:Emerging Themes in Epidemiology
  • 印刷版ISSN:1742-7622
  • 电子版ISSN:1742-7622
  • 出版年度:2008
  • 卷号:5
  • 期号:1
  • 页码:24
  • DOI:10.1186/1742-7622-5-24
  • 语种:English
  • 出版社:BioMed Central
  • 摘要:Because common diseases are caused by complex interactions among many genetic variants along with environmental risk factors, very large sample sizes are usually needed to detect such effects in case-control studies. Nevertheless, many genetic variants act in well defined biologic systems or metabolic pathways. Therefore, a reasonable first step may be to detect the effect of a group of genetic variants before assessing specific variants. We present a simple method for determining approximate sample sizes required to detect the average joint effect of a group of genetic variants in a case-control study for multiplicative models. For a range of reasonable numbers of genetic variants, the sample size requirements for the test statistic proposed here are generally not larger than those needed for assessing marginal effects of individual variants and actually decline with increasing number of genetic variants in many situations considered in the group. When a significant effect of the group of genetic variants is detected, subsequent multiple tests could be conducted to detect which individual genetic variants and their combinations are associated with disease risk. When testing for an effect size in a group of genetic variants, one can use our global test described in this paper, because the sample size required to detect an effect size in the group is comparatively small. Our method could be viewed as a screening tool for assessing groups of genetic variants involved in pathogenesis and etiology of common complex human diseases.
  • 关键词:Probability Density Function ; Single Nucleotide Polymorphism ; Sample Size Requirement ; Multiplicative Model ; Global Test
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