Category Archives: Assessment of new methods

Bridging Two Worlds: Lifting Over Your Variants to GRCh38

GRCh38

When the new human reference genome was released over two years ago, it was hailed as a significant step forward for next generation sequencing. Compared to GRCh37, the new GRCH38 reference assembly fixed gaps, repaired incorrect sequences and offered access to sections of the genome that had been previously unaccounted for. Despite these improvements, adoption of the new assembly has… 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 »

Introducing Phenotype Gene Ranking in VarSeq

v_ontop_TM_white_200

Personal genome sequencing is rapidly changing the landscape of clinical genetics. With this development also comes a new set of challenges. For example, every sequenced exome presents the clinical geneticist with thousands of variants. The job at hand is to find out which one might be responsible for the person’s illness. In order to reduce the search space, clinicians use various methods… Read more »

Tips and Tricks for Quality Control Metrics

SVS offers options for performing many different QC functions on genomic data. This blog takes you through some of the most commonly applied filters for various analysis types. Filters for GWAS data vary depending on the type of association tests you are performing. A typical GWAS for a common variant usually requires filters to remove problematic or poorly called variants,… Read more »

New MM-KBAC Method Explained

Last month, June 2014, we announced a new method that Golden Helix developed–the soon to be available MM-KBAC. MM-KBAC, or Mixed Model Kernel Based Adaptive Clustering combines the KBAC method developed by Lui and Leal (2010) with a random effects matrix to adjust for relationships between samples. The KBAC algorithm takes a binary dependent variable and transformations are used to convert… Read more »