Thank you to those who attended our recent webcast by Gabe Rudy, Large Scale PCA Analysis in SVS. For those who could not attend, you can find a link to the recording here. While this webcast discussed methods for principal components analysis (PCA) in SVS, including the new capability for performing principal components analysis on large sample sizes, it also… Read more »
In this blog post, I am very excited to talk about The Broad Institute’s release of the latest version of gnomAD, v 3.1.2, which is now available for use as an annotation source in your SVS or VarSeq projects. For VarSeq users, I also want to point out that gnomAD v3.1.2 can also be used as a population frequency in… Read more »
The articles we saw this November and December cited a wide range of applications of our product suite. The following publications feature usage of our SNP & Variation Suite, VSClinical, and VarSeq products. We see them being utilized to identify loci associated with facial eczema in New Zealand sheep, somatic mutation response impact, and assisting in estimating breast cancer risk… Read more »
We would like to announce that a new version of SVS has been released! The headlining feature of the SVS 8.9.1 release was new functionality for Large Data Principle Component Analysis. A detailed description of this new feature can be explored in this recent blog post: Finding a Few Principal Components Quickly from Data with Thousands of Samples. However, there… Read more »
As the world is consumed by the ongoing pandemic, it is easy to forget that there are investigators all around the globe that continue to make important discoveries in human medicine. Below are a few examples that remind us there are those that persevere in their chosen fields of study despite the trying times. At Golden Helix, we continue to… Read more »
SVS 8.9.0 was released on August 19th and features a new GBLUP by Bin feature and a new utility to find the LD scores of markers and categorize them into bins, along with several mixed-model upgrades and many other upgrades, fixes, and polishes. The two new features LD Score Computation and Binning and Compute GBLUP Using Bins, while they can… Read more »
The world has been making a shift to use GRCh38 human genome reference coordinates, but the transition has not been fast. Many of the mainstay human catalog projects are changing to use native GRCh38 catalogs, or are remapping their current data to GRCh38 coordinates. While this seems to be the advancing goal, it is leaving researchers and analysts with the… Read more »
Thank you to everyone who joined our webcast, “Whole Genome Trait Association in SVS.” If you missed the live event and are interested in knowing what we talked about, you may access the recorded event below: Our Live Q&A generated a lot of great questions. Unfortunately, we were unable to answer them all, but we have compiled some of the… Read more »
SVS offers several options to conduct genome wide association tests and mixed linear models. At times, it can be challenging to decide which test, model, or adjustments to use when setting up your analysis. I want to briefly explore the options available in SVS for association tests, and mixed linear models to hopefully facilitate in understanding and choosing which options… Read more »
Thank you to everyone who joined me for our latest webcast, “Next-Gen Sequencing of the SARS-CoV-2 Virus with Golden Helix.” If you missed the live event and are interested in knowing what we talked about, you may access the recorded event below: Our Live Q&A generated a lot of great questions. Unfortunately, we were unable to answer them all, but… Read more »
We’re packing our bags and getting ready to head out to San Diego, CA for the International Plant & Animal Genome XXVIII meeting (or PAG 2020). This is the largest ag-genomics meeting in the world, bringing together over 3,000 leading genetic scientists and researchers in plant and animal research, and over 120 exhibits, 140+ workshops, 1000+ posters, and 1700+ abstracts. We… Read more »
Using the K-Fold Cross-Validation Statistics to Understand the Predictive Power of your Data in SVS In cross-validation, a set of data is divided into two parts, the “training set” and the “validation set”. A model for predicting a phenotype from genotypic data and (usually) some fixed effect parameters is “trained” using the training set—that is, the best value(s) of the… Read more »
Thank you all for tuning in to yesterday’s webcast, “Simplify Your GWAS & Genomic Prediction with SVS”. I hope you all enjoyed it as much as I did! If you didn’t get a chance to join us for this live webcast, you can watch the recording below. We covered a lot of topics in so little time, but you all… Read more »
SVS is a project-oriented program that manages and analyzes genomic datasets. This webcast statistically and visually explores the relationships among genetic variants within a cattle dataset. Even further, this webcast evaluates genotypes with corresponding phenotypes to assess how well a model can predict a phenotype of interest. Starting with genotypic data from the microarray and the recorded phenotypic data for… Read more »
As our final part of the ‘Top-Quality GWAS Analysis’ blog series, we will be giving a summary of the values behind GWAS quality control and quality assessment. Performing GWAS can provide insight into the association of genetic variants with traits and complex disorders. Any novel insights into marker-phenotype associations need to be based on performing quality control steps. In this… Read more »
Population Stratification This article is going to cover how to factor for population stratification in your association test to continue our blog series on top quality GWAS analysis (additional articles for this series are located at the bottom of this blog). Quality control steps up to this point have included assessing sample and marker statistics, LD pruning on markers, and… Read more »
Sample Relatedness Pruning your data based on Linkage Disequilibrium (LD) values and filtering for sample “relatedness” are ideal quality assurance steps following the marker and sample quality filtering described in Part II of this blog series. The value of running an Identity by Decent estimation not only allows you to factor family relatedness in your samples but makes screening for… Read more »
Eliminate Low-Quality Samples and Markers In Part I of this GWAS Analysis series, Dr. Eli Sward provided us with a great overview on the value SVS provides in managing the quality of your SNP or NGS data to maintain the high power and accuracy of your GWAS. He also gave a snapshot of what a typical genotype spreadsheet may look… Read more »
The SVS 8.8.3 release was created to incorporate some of the CNV, genome assembly control, and splice site capabilities that are present in VarSeq, as well as clean up and streamline the GWAS workflows (like when using Mixed Linear Model algorithms) for a better user experience. New Product Add-Ons for SVS GoldenHelix SVS now includes in-silico splice site, functional prediction… Read more »
Genome-wide association studies (GWAS) are useful in genetics as they test for the association of a phenotype with common genetic variants. GWAS is “hypothesis-free” and does not require prior knowledge of a gene’s biological impact on a trait. The catch though is that this leads to analyzing hundreds to thousands of genome-wide array samples to elucidate single nucleotide polymorphisms (SNPs) associated with a specific phenotype.