I have been using Tassel for GWAS in wheat for several years. I tried GAPIT too before, but for some reason, GAPIT is quite slow when I have over 20 K markers and 260 lines. So most of the time, I still use Tassel. In recent years, new GWAS methods have been reported, but I have not had a chance to try them yet. Miao et al (2008) compared different GWAS software/methods on the trait-variant association in crops under varying genetic architectures and found that FarmCPU provides the greatest statistical power for moderately complex traits. A few people also recommended FarmCPU to me. So I went to the FarmCPU webiste, but I found Zhiwu’s group has developed a new software called “BLINK”, which supposed to outperform FarmCPU in both speed and accuracy. I tested it with my wheat 90K SNP data and kernel shape data, but I did not get any significant SNPs with the message “The signal from LM is too weak!”. I read the preprint paper of BLINK and found that BLINK only allows examination of SNPs with P values of 0.01 after a multiple test correction. I think that is why BLINK is not working in wheat. I used the R version of BLINK, in which I could change the primary criteria, but I am not confident about this modification. I will contact the authors to confirm.
Wheat is a highly inbred species with very long LD blocks, but most of these software developed for GWAS are for maize, an outcrossing crop with a very short LD decay distance. And a lot of GWAS are also reported in outcrossing species. When I first touched GWAS in wheat, using the Bonferroni criteria or the FDR criteria, I could not find any significant markers. Later, after discussing with my wheat colleagues, I think the problem is that wheat has a long distance of LD decay, which is good for such a huge genome species because I can use a small number of markers to find MTAs, but the bad thing is that the associations are usually not that significant. What I do now is to see consistency rather than just highly significant P values. A few steps for GWAS in my research:
Estimate the overall LD decay distance and critical . In wheat, the average LD is about 10 cM depending on the populations. In our latest GWAS paper with a collection of 262 elite spring wheat materials from North America, the LDs ranged from 0.55 cM to 12.8 cM. In wheat, 1 cM is about 1 Mb on average.
Estimate the number of highly informative markers based on the critical value using Haploview. In our latest GWAS paper, we used 22,226 SNPs, but there are only 1090 highly informative markers. So if we use Bonferroni correction at , the significant raw P value should be , about , which is the P value we used for “highly significant SNPs”.
Significant SNPs were identified using raw P values based on consistency across environments, since we have at least 3 trials for field experiments. In our latest GWAS paper, we defined “significant SNPs” as those that have raw P values < 0.05 in at least 3 environments and raw P values < 0.01 in at least 1 environment (we have 4 to 10 trials for different traits).
To better reduce the false positives, we also validated the significant SNPs using 8 nested association mapping populations (NAMs), which also allows us to have donor materials for gene clonings and breeding.