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.
Bryce Christensen

The 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 … Continue reading

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Bryce Christensen

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 … Continue reading

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Bryce Christensen

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 … Continue reading

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Bryce Christensen

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 … Continue reading

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Bryce Christensen

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 … Continue reading

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Bryce Christensen

SVS, Population Genetics, and 1000 Genomes Phase 3

One frequent question I hear from SVS customers is whether whole exome sequence data can be used for principal components analysis (PCA) and other applications in population genetics. The answer is, “yes, but you need to be cautious.” What does … Continue reading

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Bryce Christensen

A few thoughts from IGES

Late last month I had the opportunity to attend one of my favorite events: the annual meeting of the International Genetic Epidemiology Society (IGES).  This year’s conference was held in Vienna, in conjunction with the Genetic Analysis Workshop (GAW) and … Continue reading

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Bryce Christensen

Have you ever had a bad experience with a VCF file?

“Who has ever had a bad experience with a VCF file?” I like to ask that question to the audience when I present data analysis workshops for Golden Helix. The question invariably draws laughter as many people raise their hands … Continue reading

Posted in Best practices in genetic analysis, Bioinformatic support, Customer Questions | Tagged , , , | 5 Comments
Bryce Christensen

“Intriguing Findings Are for Romance Novels”

A report from the World Congress of Psychiatric Genetics Earlier this month, while much of the genetics community was scrambling to edit and print their posters for ASHG, I had the opportunity to attend WCPG, the World Congress of Psychiatric … Continue reading

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Bryce Christensen

Population Structure + Genetic Background + Environment = Mixed Model

A few months ago, our CEO, Christophe Lambert, directed me toward an interesting commentary published in Nature Reviews Genetics by authors Bjarni J. Vilhjalmsson and Magnus Nordborg.  Population structure is frequently cited as a major source of confounding in GWAS, … Continue reading

Posted in Best practices in genetic analysis, General statistical genetics principles, Plant & animal | 3 Comments