I've 200 semi-synthetic lines derived from three crosses having a common parent. I'll use this population for GWAS but before that I need to use a method which can accurately calculate the relationship (KINSHIP) among thses individuals to control the false positive and false negative in association mapping. Any suggestion on this issue will be appreciated.
Thanks
Since you are describing a NAM population, the R package NAM is the most suitable choice.
If you are just looking for a kinship, NAM::eigX function provides the eigendecomposed kinship matrix. Running the entire GWAS in NAM is easier.
HI Alencar,
Thanks for your promt reply. I am having a population just like NAM but not completely like NAm because in the final stage, out of three, each population was intercrossed for two generations followed by one selfing. So, principal coordinates can easily separate all the three populations but kinship is a problem in this condition. Because, I am not sure that kinship is really important in my case or not because if I use a false kinship coeffecients they may create unnecessary overcompensation.
Thanks,
Both the use of 2-5 principal components as fixed effect and/or a regular kinship will do a good job regarding the control of false positives. Another possibility is to add family as covariate.
I would still suggest using the package NAM for the association analysis (function gwas2) because it allows to include the information about which sub-population each line belongs to.
We know that false positives come from sharing of genome from one parent at multiple locations. GWAS generates a chromosome map for each genotype (individual plant). Thus mapping at multiple location indicating false tagging would correlate with genotypes having genomic contribution by same species at multiple locations. However, in your case because of multiple crosses and selfing chances of false mapping are less but can not be ruled out.
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