Category Archives: General statistical genetics principles

RefSeq Genes: Updated to NCBI Provided Alignments and Why You Care

You probably haven’t spent much time thinking about how we represent genes in a genomic reference sequence context. And by genes, I really mean transcripts since genes are just a collection of transcripts that produce the same product. But in fact, there is more complexity here than you ever really wanted to know about. Andrew Jesaitis covered some of this… Read more »

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, but the authors of the article suggest that the problems often blamed on population structure actually result from the environment… Read more »

What is Bioinformatic Filtering?

Recently I gave a presentation on bioinformatic filtering: the process of using quality scores, annotation databases, and functional prediction scores to intelligently and quickly reduce your variant search space. View the recording here» In this webcast, I mention that filtering is something we have been doing for a long time, and that there are some great examples that use exome… Read more »

Admixture and Blaine Bettinger

      Golden Helix    January 25, 2012    2 Comments on Admixture and Blaine Bettinger

Allow me to introduce you to Blaine Bettinger.  Blaine is a patent attorney who holds a PhD in Biochemistry with a concentration in genetics.  He is also a family history enthusiast who writes the Genetic Genealogist blog, where he gives commentary on applications of genomic science for advancing personal and family history research.  I first learned about Blaine last May… Read more »

Please Help Me Get My Regression Model Set Up!

Golden Helix’ SNP & Variation Suite (SVS) has a Regression Module to enable researchers with varying degrees of statistical knowledge to interrogate their data using regression models to account for potential confounding effects of covariates and interaction terms. While these tools are labeled “basic”, they can be difficult to use and results hard to interpret for those who have only… Read more »