Researchers identify two groups: one groups of people with a disease condition (patients) and one group of healthy individuals (controls).
should be 'one group of people'
Next, genomes of diseases individuals are compared to the genomes
should be 'of diseased individuals'
For example, large number of associated SNPS in the gene encoding eye color may not serve as a good candidate gene for liver disease. These SNPs may just be "tagging along" with the actual causal variants.
So there's a typo in there, but I also think it's just conflating a bunch of different issues. There's no reason an eye color gene couldn't be implicated in liver disease, the concern is more about the specificity of a correlation analysis given that genomes tend to have large haplotype blocks. I would just change this to:
"For example, you may find multiple SNPs in a region that are equally well correlated with liver disease, but that only one of those SNPs cause the phenotype. It's also common to find that none of the correlated SNPs are causative, rather they all are just tightly linked to an undetected mutation and are "tagging along" with the actual causal variants.
ACharbonneau
commented
4 years ago
https://cfde-training-and-engagement.readthedocs-hosted.com/en/latest/Bioinformatics-Tutorials/GWAS-in-the-cloud/background/
should be 'one group of people'
should be 'of diseased individuals'
So there's a typo in there, but I also think it's just conflating a bunch of different issues. There's no reason an eye color gene couldn't be implicated in liver disease, the concern is more about the specificity of a correlation analysis given that genomes tend to have large haplotype blocks. I would just change this to:
"For example, you may find multiple SNPs in a region that are equally well correlated with liver disease, but that only one of those SNPs cause the phenotype. It's also common to find that none of the correlated SNPs are causative, rather they all are just tightly linked to an undetected mutation and are "tagging along" with the actual causal variants.