AGGREGATION-BASED ASSOCIATION TESTS FOR IDENTIFICATION OF RARE VARIANTS

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  • Otani Takahiro
    Nagoya University Graduate School of Medicine
  • Sugasawa Shonosuke
    Center for Spatial Information Science, The University of Tokyo
  • Noma Hisashi
    Department of Data Science, The Institute of Statistical Mathematics CREST, Japan Science and Technology Agency, Tachikawa

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Other Title
  • 希少な遺伝的変異を対象とする集約型関連解析
  • キショウ ナ イデンテキ ヘンイ オ タイショウ ト スル シュウヤクガタ カンレン カイセキ

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Abstract

Genome-wide association studies (GWASs) conducted over the past decade have identified a few thousand single nucleotide polymorphisms (SNPs) associated with certain traits or diseases. Conventional GWASs have focused on identifying SNPs with relatively high frequency in a population; however, several recent studies have focused on discovering rarer genetic variants, enabled by rapid advances in high-throughput DNA sequencing technology. Nonetheless, identifying trait- or disease-associated rare variants is more challenging from a statistical perspective, because the statistical power of ordinary association tests is extremely small. Therefore, as alternatives to conventional single-variant association tests, gene- or region-based association tests that aggregate information of multiple variants in a specified gene or region have been discussed. This article provides a comprehensive review of aggregation-based rare-variant association tests proposed in recent methodological studies.

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