全基因组关联分析.ppt

1、关联分析一些问题讨论,1)候选基因策略 2)全基因组策略,Candidate gene selection,Biochemical evidence (clear pathway) QTL evidence Other molecular evidences (eg expression, microassay, etc.),Next potential pathway,VE pathway Oil pathway Disease ABA ,Gene sequencing and alignment,Sequencing the whole gene in a core set look fo

2、r the potential functional SNPs/InDels to develop markers to score the whole panel Sequencing partial gene in whole panel Look for the associations based on LD Estimate the LD of the target gene,Sequencing alignment using Biolign/Bioedit/Cluster See another presentation,Estimate the LD of the target

3、 gene,Software- Tassel As demo by Xiaohong Show results with two way,连锁不平衡,读杨小红等 作物学报, 2007 综述,Population development,Diversity inbreds are the best choice for developing an association mapping panel,群体结构,False positive Power,Population structure,Familial relatedness,a,d,b,c,e,Various association sa

4、mples,Yu et al., Nat Genet 38: 203-208 (2006),Section 2,Type I error rates (false positive),A straight diagonal line indicates an appropriate control of false positives. Q + K model has best Type I error control, most important when trait is related to population structure (e.g., flowering time).,St

5、atistical power,Q + K model had highest power to detect SNPs with true effects.,Phenotyping,Nothing to say! It is the most important thing! Multiple locations/replications,Association analysis -TASSEL,Section 3,几个值得讨论的问题,等位基因频率 Haplotype 分析 LD的影响,等位基因频率,功能位点的频率往往是严重偏离1:1的-符合生物学逻辑 VA基因的例子 抗旱基因的例子,低频位

6、点,需要足够大的群体,同时少数几个异常位点就会严重影响结果. 关联分析之前: 1)处理表型数据,确定是否适合关联 2)MAF0.05 或0.01,Summary of candidate genes in the 17 loci associated with metabolite traits,单倍体型分析,LCYE associations across seasons,Harjes et al, Science, 2008, 319: 330-333,单倍体型分析与LD,Strong LD within the 5 significant loci was found, Haplotyp

7、e analysis is not good for PSY1,单倍体型分析与群体结构,SAD1不同位点的等位基因在各群中的频率分布,SAD1单倍型对硬脂酸含量的效应,关联分析的其它证据,完整故事需要（LCYE是经典案例)： 表达的数据 连锁的数据 其他群体的证据 实践的证据(MAS) ,越是完整的故事(尤其从实验室到实践), 越能吸引大家的兴趣!,关联分析的未来- 全基因组的关联分析(GWAS),取代连锁分析?,GWAS影响因素:,群体大小 标记数目 分析方法 表型选择和调查,Calculate by Genetic Power Calculator Purcell et al. (2003) Bioinformatics, 19:149-150.,Statistical power of detection in GWAS for variants that explain 1-30% of the variation at type I error =0.