Genetic improvement in livestock, particularly dairy cattle, has been a priority for both industry and researchers for nearly a century. While the animal itself is the foundation for improvement, our research and the implementation of improvement has progressed with developing technologies and priorities. In terms of genetics, we have evolved from basic measures of heritability to identifying specific mutations and their biological role affecting a trait. My research focuses on population structure and trait association in domestic animals using high-density genome-wide single-nucleotide polymorphism (SNP) array data and fine-mapping sequence data. My end goal is to conduct research that will assist in the genetic improvement of domestic animals through an increased understanding of the genetic mechanisms controlling biological pathways and the development of genomic tools for implementation. Continue reading
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 the logistic regression model to a linear model so that EMMAX (Efficient Mixed Model Association eXpedited) can be used to solve the equations.
We are also very excited that we have been accepted to present this material at ASHG this October (we’ll be in booth 422)! More importantly, we will be making this method available to our customers with the next release of SVS due out in August. Continue reading
With the t-shirt submission deadline behind us, it’s time for the exciting part of the contest – picking the winners! We received a ton of fantastic designs and had a hard time narrowing them down. But, the Golden Helix team has picked seven designs that truly embody the Golden Helix spirit.
We released GenomeBrowse 2.0 earlier this year, allowing users to review all types of genomic data. Since then, it has received rave reviews from thousands of users around the world. Essentially, it’s the Google Earth app for genomic data.
GenomeBrowse allows a user to sift through vast amounts of genomic data, and make it easy to focus on a single part or the whole. GenomeBrowse has reached global adoption; as of July 2014 thousands of users worldwide have begun to use this product.
Do you want to review a few hundred genomes on your desktop computer? No problem. We want to make this process as simple and intuitive as possible. Here are some key aspects of GenomeBrowse:
It is with great excitement that we introduce our next webcast: Population Structure & Genetic Improvement in Livestock, presented by Dr. Heather J. Huson of Cornell University. Huson was one of the first place winners in this year’s research abstract competition. As part of the competition Huson has the opportunity to present her research in a webcast on Tuesday July 22nd.
Heather received a Bachelor’s in Animal Science from Cornell University in 1997 and went on to complete her Ph.D. in Molecular Genetics at the University of Alaska Fairbanks in 2011.
I recently gave a webcast on GWAS in a model organism: Arabidopsis thaliana; a question was brought up about the differences between EMMA and EMMAX and why the results with each would differ. Continue reading
Join us tomorrow, July 9th at 12PM EDT, for Ashley Hintz’s webcast on GWAS in a Model Organism: Arabidopsis Thaliana.
Joining the Golden Helix team as a Field Application Scientist in April of 2014, Hintz is the perfect candidate to present on Arabidopsis Thaliana given her background in zoology and phylogenetics of planigales. Continue reading
It’s coming down to the wire – if you have not submitted your Golden Helix T-shirt design please do so!! We have already had many great submissions but we are missing yours.
Once the contest closes tomorrow, July 3rd, our talented Golden Helix staff will narrow down the choices and display them to our community for voting. The designs with the most votes will be put into production and unveiled at our booth at ASHG in October. Continue reading
Up until a few weeks ago, I thought variant classification was basically a solved problem. I mean, how hard can it be? We look at variants all the time and say things like, “Well that one is probably not too detrimental since it’s a 3 base insertion, but this frameshift is worth looking into.” What we fail to recognize is just how many assumptions went into the above statement. What transcript set are we using? In what part of the gene did the mutation occur? What subfeature of the gene are we looking at? Are there other ontologies for the variant? Why did we use the term we did? In order to develop a tool to annotate variants, rules to answer all these questions have to be codified into software. Enumerating these assumptions means that a process that is subject to a great deal of human interpretation, is now a rigidly defined objective framework. There are currently three major tools that attempt to classify variants: Annovar, SnpEff and Variant Effect Predictor (VEP). It is no surprise that these tools do not always agree since the way the rules have been defined differ slightly between each application. Continue reading
The 2014 MAGES Conference hosted in Philadelphia brought out the shining stars in Statistical Genetics, along with a variety of approaches and difficulties researchers in the field are facing. Being my first MAGES event, I did not know what to expect; however, I was thoroughly impressed and am excited to go back next year.
Some of the topics that seemed to become more prevalent over the day included the use of BioBank data, algorithms taking advantage of Bayesian statistics, and talks addressing how complex disease actually is! Continue reading