SAIGE approach (Supported later)

SAIGE and SAIGE-GENE are accurate and efficient approaches to analyze quantitative and binary traits. Users can refer to SAIGE package, which is still in continuously updated.

Citation

• SAIGE: Zhou, Wei, Jonas B. Nielsen, Lars G. Fritsche, Rounak Dey, Maiken E. Gabrielsen, Brooke N. Wolford, Jonathon LeFaive et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nature genetics 50, no. 9 (2018): 1335-1341.

• SAIGE-GENE: Zhou, Wei, Zhangchen Zhao, Jonas B. Nielsen, Lars G. Fritsche, Jonathon LeFaive, Sarah A. Gagliano Taliun, Wenjian Bi et al. Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts. Nature genetics 52, no. 6 (2020): 634-639.

• SAIGE-GENE+: Zhou, Wei, Wenjian Bi, Zhangchen Zhao, Kushal K. Dey, Karthik A. Jagadeesh, Konrad J. Karczewski, Mark J. Daly, Benjamin M. Neale, and Seunggeun Lee. SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests Nature genetics 54, no. 10 (2022): 1466-1469.