![]() The compute time discussions do not provide sufficient descriptions of resources to be of any meaning. Does this imply that FarmCPU (upon which BLINK is built) is ineffective? Also, were other genes cloned in maize that control flowering time? What is the possible number of flowering time genes that the 3 "true positives" were drawn from? FOAM found "1003 genes". P8L10-13 I don't understand why BLINK is better than FarmCPU as it detected 9/14 QTNs. P8L4-5 Why does PLINK exhibit "strongly inflated P values"? Is there evidence to support this statement? Please provide a rationale as to why only PLINK was compared. The authors clearly explain that there are newer and more powerful options than PLINK. The authors compare two of their workflows (BLINK and FarmCPU)against PLINK. Please explain the rationale for not using real data when it is clearly available. Why was the real genotype and phenotype data simulated? Why not test on real data and see if you reproduce previous findings? The authors do this a little bit with the maize analysis. Wouldn't it be "better" to amplify with variation? Why was the human dataset replicated in such a severe manner? Each population group is amplified perfectly up to 10 times to make a bigger dataset. Are these Asian Americans? What is the population structure in a general sense? ![]() Which maize datasets was used? Is this the NAM population? If so, it need to be made more clear how this population was constructed. At the very least, a brief description is warranted and not making the reader read another paper for any sense of genetic context. There is no description of the mouse and pig datasets for example. The datasets used for testing are not clearly explained. ![]() The article addresses an ongoing scientific problem of great importance. BLINK is built from their previous GWAS implementation called FarmCPU but they replace REML with a BIC algorithm and they include LD information to remove the previous assumption that QTNs are evenly distributed across the genome (which they are not!). Then they provide a new algorithm that address both issues called BLINK. In "BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in Millions" by Huang et al, the authors provide a detailed background of the evolution of GWAS approaches that improve computation time and statistical power.
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