Post by Jian Yang on Sept 15, 2015 2:51:21 GMT
Yang J, Zaitlen NA, Goddard ME, Visscher PM & Price AL (2014) Advantages and pitfalls in the application of mixed model association methods. Nat Genet, 46: 100–106.
This paper provides theories to explain why a mixed linear model (MLM) based association analysis including all the SNPs in the polygenic component loses power compared with linear regression.
It proposes a leave-one-chromosome-out (LOCO) approach that controls for population structure and gains power. The COLO approach has bee implemented in GCTA (see gcta.freeforums.net/thread/173/mixed-linear-model-association-analysis).
Corrigendum
1) Equation 3 should be
2) The text below Equation 3 should be "The ratio of λmean between MLMe
and MLMi is also
"
This paper provides theories to explain why a mixed linear model (MLM) based association analysis including all the SNPs in the polygenic component loses power compared with linear regression.
It proposes a leave-one-chromosome-out (LOCO) approach that controls for population structure and gains power. The COLO approach has bee implemented in GCTA (see gcta.freeforums.net/thread/173/mixed-linear-model-association-analysis).
Corrigendum
1) Equation 3 should be
2) The text below Equation 3 should be "The ratio of λmean between MLMe
and MLMi is also
"