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Activity Number:
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406
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #305656 |
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Title:
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Overcoming Data Quality and Copy Number Detection Issues in Genome-Wide Copy Number Variation (CNV) Association Studies
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Author(s):
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Christophe G. Lambert*+ and Greta M. Linse and James E. Grover and Gabriel F. Rudy
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Companies:
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Golden Helix Inc. and Golden Helix Inc. and Golden Helix Inc. and Golden Helix Inc.
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Address:
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P.O. Box 10633, Bozeman, MT, 59719,
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Keywords:
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CNV ; GWAS ; genetics ; copy number ; microarray ; segmentation
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Abstract:
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Though CNV association studies show great promise, data quality issues make this potential gold mine a quagmire. CNV quality issues have been a persistent problem on the 20+ CNV GWAS we have performed on multiple microarray and aCGH platforms. The impact of batch effects and other quality issues leads to complications ranging from poorly defined segments to false and non-replicable findings. Our PCA approach simultaneously corrects for batch and wave effects and population stratification, while significantly improving signal-to-noise ratios. Combining this with novel segmentation-based calling methods gives improved sensitivity and FDR. Several approaches to genome-wide scans for CNV association are shown, leading to significant findings across many studies. Our results suggest there is a wealth of CNV associations that explain much of the heritability not accounted for by SNPs.
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