Monday, October 11, 2010

IGES first day impression

The theme of first day workshop is “Next Generation Sequencing (NGS) in Genetic Epidemiological Studies”. Besides academic people, the presenting panel also includes people from companies such as Affymetrix, Illumina, and Gold Helix. The talks are interesting especially the ones from Suzanne Leal and Scott Williams. Scott talks about NGS from evolution point of view. Suzanne’s talk is more related to our works.
Suzanne’s talk basically reviewed the papers what we discussed in Journal Club and our GAW 17 submission abstract including
Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs HH. (2004) Multiple rare
alleles contribute to low plasma levels of HDL cholesterol. Science,305,869872.

Madsen BE and Browning SR. (2009) A group-wise association test for rare mutations using
a weighted sum statistic. PLOS Genet, 5(2), e1000384.

Li B and Leal SM. (2008). Methods for detecting associations with rare variants for common dis-
eases: application to analysis of sequence data. Am J Hum Genet., 83(3), 311-321.

Morris AP and Zeggini E. (2010) A evaluation of statistical approaches to rare variant analysis
in genetic association studies. Genet Epidemiol 34(2), 188-193.

Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ, Sunyaev SR.(2009)
Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet., 86(6),
Another one she mentioned is her new work named KBAC (didn’t write down the whole name). In this work, the basic idea is to upweight the causal genotype. She also tested it upon gene interaction. I asked two questions here for her new work. (1). How to distinct causal genotypes from non-causal ones, especially in real data, and how distinct it from functional genotypes since she also mentioned that they are different. (2). How did they simulate gene interactions? For (1), she responded that, in simulation, they know whether the genotypes are causal or not. But in general in real data, there’s no way to distinct. For (2), she used some data that are known to involved gene interactions. That is to say, they use the known information.
She also pointed out several challenging problems in this field. I list some here: (1). Population structure/admixture, (2) gene X gene, gene X environment, (3) Signaling pathways, (4) Jointly analyzing rare and common variants, and (5) qualitatively and quantitatively.

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