Sequence Kernel Association Tests

Sequence Kernel Association Test is a statistical method in rare variant association studies that aggregates the effects of multiple rare genetic variants within a genomic region using a kernel function to test for association with a phenotype while accounting for both risk and protective variants.

117

Publications

8K

Citations

1.3K

Authors

488

Institutions

Publications per year

2017–2026

52

Authors

1.3K

  1. MC
    Michael C. Wu

    Fred Hutch Cancer Center, United States

    8 publications · 3.6K citations

    8

    h-index

  2. SL
    Seunggeun Lee

    University of Michigan, United States

    7 publications · 3.3K citations

    7

    h-index

  3. RF
    Ruzong Fan

    Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States

    6 publications · 215 citations

    6

    h-index

  4. MX
    Momiao Xiong

    The University of Texas Health Science Center at Houston, United States

    6 publications · 215 citations

    6

    h-index

  5. YW
    Yifan Wang

    The University of Texas Health Science Center at Houston, United States

    5 publications · 205 citations

    5

    h-index

1–5 of 1.3K

Venues

  1. Genetic Epidemiology

    25 publications · 1.2K citations

    16

    h-index

  2. PLoS ONE

    6 publications · 132 citations

    6

    h-index

  3. The American Journal of Human Genetics

    AJHG · 5 publications · 3.1K citations

    5

    h-index

  4. Annals of Human Genetics

    4 publications · 94 citations

    4

    h-index

  5. BMC Medical Genomics

    4 publications · 111 citations

    4

    h-index

1–5