Have you ever scratched your head when looking up a variant and it seems like the number you have for its position is one off from what it looks like in the file or database? You may be running into the dreaded world of 1-based versus 0-based coordinate representation!
If it’s any consolation, I can promise that all the bioinformaticians and computer scientist who are responsible for building those databases and defining those file formats have also probably scratched their head staring at a similar disparity.
From a tool builders perspective, I can assure you we have the best intentions when making the calls on how to represent variants in files, websites and in backend data stores!
We are pleased to announce that another one of the most asked for features is going to be a part of our SNP & Variation Suite™ software, Gene by Environment Interaction Regression (also known as GxE Regression). Earlier this year other highly asked for features were added to SVS including applying a prediction model to a new dataset, cross-validation for genomic prediction, and meta-analysis. It is safe to say that our customer requests have been instrumental in driving development this year!
Before this latest update, Numeric Regression was already a powerful workhorse, including full versus reduced model regression, moving window regression and step-wise regression. All of these regression models allowed for accounting for covariates of various types, for both logistic and linear regression (binary and quantitative dependent variables). Numeric Regression did allow for the option to add interaction terms, but these interactions had to be explicitly stated and were designed more for covariate by covariate interaction terms and not covariate by SNP or gene interactions.
A prerequisite for clinical NGS interpretation is ensuring that the data being analyzed is of high enough quality to support the test results being returned to the physician. The keystone of this quality control process is coverage analysis. Coverage analysis has two distinct parts.
- Ensure that there is sufficient coverage to be confident in called variants
- Make certain that no variants went undetected in tested regions due to an insufficient number of reads
A common question that comes through support is if there are options in SVS for doing gender inference or checks. There is indeed functionality in SVS for this QC check! This function is under the Genotype Menu for Sample Statistics; there are a lot of great statistics available to check the quality of your data in SVS, but I’ll walk you through the Gender Inference tool.
This functionality takes your specified sex chromosome and calculates the heterozygosity rate, and based on the desired threshold will provide a call for the inferred gender per sample. This functionality is also designed to work for animals with a ZW sex determination system, such as chickens and some species of fish and reptiles. Figure 1 shows the menu options for a chicken GWAS dataset with the Z Chromosome selected for gender inference.
August is off to a great start, especially for some of our customers who have recently published. I wanted to take a minute to share their work with you.
Today I wanted to take a moment to recognize a long-time Golden Helix customer, Dr. Folefac Aminkeng of the Canadian Pharmacogenomics Network for Drug Safety and the University of British Columbia on his recent publication A coding variant in RARG confers susceptibility to anthracycline-induced cardiotoxicity in childhood cancer, in Nature Genetics.
Aminkeng and his colleagues performed a genome-wide association study in 280 patients treated for childhood cancer to investigate the susceptibility to anthracycline-induced cardiotoxicity (ACT). ACT has been previously associated through candidate gene studies in the past, however studies lacked in size, replication and functional validation. However, the group’s work identified a nonsynonymous variant in RARG as being highly associated with ACT, altering function, which provides new insights into this severe drug reaction.
Last month our webcast featured the third place winner of our Annual Abstract Challenge, Dr. Raluca Mateescu, and August’s webcast will feature co-winner, Dr. Vivien Sheehan. Dr. Sheehan’s submission last winter surrounded the pharmacogenomics of hydroxyurea in sickle cell anemia, and we are excited to have her present this research for our Golden Helix community next week (August 12th, register here!). As an introduction to the webcast, here is some background on Dr. Sheehan and her research.
Dr. Vivien Sheehan is an Assistant Professor of Pediatrics at the Baylor College of Medicine and a part of the Cancer and Hematology Center at Texas Children’s. Sheehan specializes in sickle cell disease and her projects range from clinical and translational research to basic science studies. Her current translational research project uses whole exome sequencing to study the pharmacogenomics of hydroxyurea. Currently, hydroxyurea is the only drug treatment for sickle cell disease. Some of her clinical projects include investigations into the effects of hydroxyurea on red cell rheology, including oxygen carrying capacity and red cell density. The investigations are the foundation of a clinical trial studying their effectiveness as a treatment for Hemoglobin SC disease.
Today we wanted to share a recent client case study that demonstrates how our SVS software is being used both in the classroom and laboratory to do livestock genetic analysis. If you have any questions or would like to learn more about SVS, please contact us at firstname.lastname@example.org.
Dr. Heather Huson is a Professor of Dairy Cattle Genetics at Cornell University, where she works with a team studying genetic improvement and diversity in livestock. Over the course of her career, Huson has used a number of different analysis tools in her research, but has found the ease of use and efficiency of Golden Helix’s SNP & Variation Suite (SVS) to be instrumental in her research as well as teaching genetic analysis to her students.
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 the groundbreaking Affymetrix 10k SNP chip even before the chip’s commercial launch, and well before the NHGRI-EBI GWAS catalog keeps records. Among papers included in the GWAS catalog, Golden Helix citations appear as early as 2007.
Posted in Big picture
Congratulations to all of our customers who have recently published! It’s always a pleasure to see the interesting and useful work conducted in part with the aid of our software, and we hope you enjoy reading about it as well.