Author Archives: Bryce Christensen

Bryce Christensen

About Bryce Christensen

Dr. Bryce Christensen fills two roles at Golden Helix as he is both the Director of Services as well as a Statistical Geneticist. Bryce joined GHI in 2009 from the University of Utah where he earned his PhD in Genetic Epidemiology and Biomedical Informatics. Before undertaking his graduate studies, Bryce worked for 2 years as a data analyst at Mayo Clinic in the Division of Biostatistics. Outside of work, Bryce has an affinity for restoring motorcycles and is currently in search of his next restoration project.

The 10th Anniversary of GWAS

10th Anniversary of GWAS

GWAS became possible about 10 years ago as the result of several scientific advances. Since then, GWAS has continually developed as a primary method for identification of disease susceptibility genes in humans and other organisms. At Golden Helix we are proud of our history in supporting GWAS analysis from its inception. Our software was used to analyze whole-genome data from… Read more »

Tumor/Normal Pair support now available in VarSeq!


VarSeq now supports analysis of paired Tumor/Normal samples! Tumor/Normal support has been one of the most common feature requests for VarSeq since it was launched late last year, and we are excited to make this functionality available to all of our VarSeq users in the latest update (version 1.1.4). VarSeq is a powerful platform for annotation and filtering of DNA… Read more »

Looking Beyond the Exons: Splice Altering Variants


There are many approaches that one might use to define a variant as potentially deleterious. For example, we often see analysis workflows based on rare, non-synonymous variants, perhaps incorporating additional annotation sources that capture known or predicted consequences of coding variants. Annotations for coding regions of the genome are relatively abundant and familiar to genome scientists. We are comfortable in… Read more »

Cross-Validation for Genomic Prediction in SVS

The SNP and Variation Suite (SVS) software currently supports three methods for genomic prediction: Genomic Best Linear Unbiased Predictors (GBLUP), Bayes C and Bayes C-pi. We have discussed these methods extensively in previous blogs and webcast events.  Although there are extensive applications for these methods, they are primarily used for trait selection in agricultural genetics. Each method can be used… Read more »

To Impute, or not to Impute

      Bryce Christensen    February 5, 2015    No Comments on To Impute, or not to Impute

Genotype imputation is a statistical technique for estimating sample genotypes at loci that were not directly assayed by sequencing or microarray experiments.  There are several reasons why you might want to use imputation in a research study.  For example: Improve call rates in GWAS by imputing sporadic missing genotypes Harmonize the data content from different GWAS genotyping platforms so that… Read more »