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I'm reading a paper that used whole exome sequencing on an African American and European populations to discover novel low frequency and rare variants associated with lipid levels & the risk of coronary heart disease.

They used the Illumina Human exome genotyping array that was designed based on coding variants discovered from sequencing the exomes of 12,000 individuals. Therefore, they selected their participants from a study who were not among the 12,000 individuals utilized to design the array .

My question is: Why would they select individuals who were not among the 12,000 used to design the array? (I'm thinking because maybe it could produce false positives associations? bias?)

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Can you add a citation for this paper? –  Chris Jun 30 at 5:00
    
@Chris the source is from the paper Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks.ncbi.nlm.nih.gov/pubmed/… –  geneteics_diva Jun 30 at 17:09
    
I'll have a look tomorrow, and I have added the link to your post. –  Chris Jun 30 at 19:11

1 Answer 1

When you design the arrays, you need to have probes on the surface complementary to the sequence you want to detect. Depending on what you want to detect, you need to design these probes with known sequence on a known position. If you want to detect single nucleotide polymorphisms (SNP), then you need a library of known SNPs on your ChIP, which are basically the position of the SNP and the surrounding sequence.

SNPs can be roughly grouped into two subgroups, common (I call them like this) and rare (frequency in the population lower than 0.1%). The problem with this ChIP based method is that it can only detect SNPs which are already known (or at least are located close to known SNPs with which they in linkage disequilibrium). So if you want to detect rare SNPs in your population sample, you need a big group of people from which you take the SNPs into you ChIP design. If you look at the numbers, one rare variant will only be present in about 12 of the 12.000 persons used in the design.

Your study group is then different from the group of people, which where used to make the ChIP design. Here you take people with a certain background (high lipid levels for example) and then compare, if people which had coronary heart problems have SNPs in common which might be connected to this disease. This can help to identify a mutation in a protein which is the risk factor.

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