Category Archives: Best practices in genetic analysis

GWAS 3.0

      Andreas Scherer    February 7, 2017    No Comments on GWAS 3.0
GWAS eBook

Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2000 human GWAS reports now appear in scientific journals. In fact, we see its adoption increasing beyond the human-centric research into the world of… Read more »

NGS-based Clinical Testing (Part VI)

reporting incidental genetic findings

With a properly defined wet-lab and bioinformatics process, we are able to zero in on clinically relevant variants. How does a lab report on the outcome of their analysis? We find that most laboratories conduct their variant classification based on the guidelines formulated by the American College of Medical Genetics (ACMG) for inherited diseases. The ACMG guidelines for variant classification… Read more »

ExAC CNVs: The First Large Scale Public Exome CNV Variant Set

ExAC CNVs

ExAC CNVs were released publicly with a recent publication, providing the full set of rare CNVs called on ~60K human exomes. While there are many public CNV databases out there, this is the first one that was derived from exome data, and thus includes both extremely rare and very small CNV events. With the recent release of Golden Helix’s CNV calling… Read more »

WEBCAST: CNV Analysis with VarSeq

      Mary Makris    November 22, 2016    No Comments on WEBCAST: CNV Analysis with VarSeq

December’s webcast will provide the Golden Helix community with a more in-depth look at CNV analysis in VarSeq. On December 7th, Dr. Nathan Fortier will discuss the challenges and metrics surrounding CNV detection and then demonstrate VarSeq’s new capability from VCF to clinical report.  Wednesday, December 7th @ 12:00 PM, EST Numerous studies have documented the role of Copy Number Variations (CNVs)… Read more »

Genotype Imputation and Phasing now in SNP & Variation Suite

Genotype Imputation

One of the tools at the top of the toolbox for researchers working with microarray data is genotype imputation. Genotype imputation is the process of inferring the genotype of one or more markers based on the correlation pattern (aka linkage disequilibrium or LD) of the surrounding markers for which genotypes are known. We have now integrated a natively ported version of BEAGLE into Golden… Read more »