- GBS GWAS for complex traits
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- Date 2022-08-09
GWAS for complex traits
다양한 모델(방법론)을 적용하여 QTL 형질 연관 유전자, 마커 탐색의 효율을 높입니다.
씨더스는 다양하고 복잡한 QTL 형질 연관 유전자 탐색을 위한 GWAS 모델에 따른 GWAS 분석 결과 사례를 확보하였습니다.
The development of the mixed model frame-work for GWAS dramatically reduced the number of false positives compared with naive methods.
- GLM (general linear model)
- MLM (mixed linear model)
- SUPER (settlement of mixed linear model under progressively exclusive relationship)
- ECMLM (enriched compressed mixed linear model)
- FarmCPU (fixed and random model circulating probability unification)
- MLMM (multi-locus mixed model)
Attached fil
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2022_SEEDERSWEBZINE+GWAS.pdf (825.55KB)
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