Missing Heritability and the Future of GWAS
by Dr. Christophe Lambert, CEO & President
"Where is the missing heritability?" is a question asked frequently in genetic research, usually in the context of diseases that have large heritability estimates, say 60-80%, and yet where only perhaps 5-10% of that heritability has been found. The difficulty seems to come down to the common disease/common variant hypothesis not holding up. Or perhaps more accurately, that the frequency of the assayed markers are not in line with the frequency of the disease (or specific sub-phenotype thereof). Most of the technologies directed towards finding the genetic links to diseases - e.g., the first generation of major microarray platforms used in genome-wide association studies (GWAS) - were developed using this hypothesis as a premise...
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Increase Power and Data Quality with Advanced Genotyping and Imputation Methods
by Dr. Bryce Christensen, Director of Services and Statistical Geneticist
Accuracy and completeness of genotype data are among the most important factors for a successful genome-wide association study (GWAS), and must not be taken lightly. The Golden Helix team is always on the lookout for methods to improve data quality, and we have recently found the BEAGLE and BEAGLECALL software packages to be very useful in this regard. BEAGLE is particularly useful for inferring the genotypes of missing and untested SNPs. BEAGLECALL is a companion to BEAGLE that we have found to be the most accurate genotype calling tool available today... Continue reading on "Our 2 SNPs"
Enhanced ROH Analysis Improves Effectiveness to Identify Rare, Penetrant Recessive Loci
by Gabe Rudy, VP of Software Development
In 2007 Dr. Todd Lencz introduced a new way of doing association testing using SNP microarray platforms. The method, which he termed whole genome homozygosity association, first identifies patterned clusters of SNPs demonstrating extended homozygosity (runs of homozygosity or "ROHs") and then employs both genome-wide and regionally-specific statistical tests on ROH clusters for association to disease. This approach is powerful for identifying chromosomal segments that harbor rare, penetrant recessive loci.
An especially important contribution to this approach has been the addition of more advanced filtering criteria for ensuring that algorithmically detected ROHs are true population variants and not due to: random chance, the result of genotype calling anomalies, or low marker coverage over certain regions of the genome... Continue reading on "Our 2 SNPs"