Xue, YuanDing, JuanWang, JinjuanZhang, SanguoPan, Dongdong2020-11-182020-11-182020-01Xue Yuan, Ding Juan, Wang Jinjuan, Zhang Sanguo, Pan Dongdong. Two-phase SSU and SKAT in genetic association studies. Journal of Genetics. 2020 Jan; 99: 1-100022-13330973-7731http://imsear.searo.who.int/handle/123456789/215555The sum of squared score (SSU) and sequence kernel association test (SKAT) are the two good alternative tests for genetic association studies in case–control data. Both SSU and SKAT are derived through assuming a dose-response model between the risk of disease and genotypes. However, in practice, the real genetic mode of inheritance is impossible to know. Thus, these two tests might lose power substantially as shown in simulation results when the genetic model is misspecified. Here, to make both the tests suitable in broad situations, we propose two-phase SSU (tpSSU) and two-phase SKAT (tpSKAT), where the Hardy–Weinberg equilibrium test is adopted to choose the genetic model in the first phase and the SSU and SKAT are constructed corresponding to the selected genetic model in the second phase. We found that both tpSSU and tpSKAT outperformed the original SSU and SKAT in most of our simulation scenarios. By applying tpSSU and tpSKAT to the study of type 2 diabetes data, we successfully identified some genes that have direct effects on obesity. Besides, we also detected the significant chromosomal region 10q21.22 in GAW16 rheumatoid arthritis dataset, with P \10-6 . These findings suggest that tpSSU and tpSKAT can be effective in identifying genetic variants for complex diseases in case–control association studiesmultiple-markers analysisgenetic modelHardy–Weinberg equilibriumpowerJournal ArticleIndiaSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of ChinaKey Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100049, People’s Republic of ChinaSchool of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, People’s Republic of ChinaSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of ChinaLSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People’s Republic of ChinaSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of ChinYunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming 650500, People’s Republic of China