Tag Archives: Golden Helix

The New Human Genome Reference and Clinical Grade Annotations: It’s All About the Coordinates

On my flight back from this year’s Molecular Tri-Conference in San Francisco, I couldn’t help but ruminate over the intriguing talks, engaging round table discussions, and fabulous dinners with fellow speakers. And I kept returning to the topic of how we aggregate, share, and update data in the interest of understanding our genomes. Of course, there were many examples of… Read more »

All I Want for Christmas Is a New File Format for Genomics

Tis the season of quiet, productive hours. I’ve been spending a lot of mine thinking about file formats. Actually, I’ve been spending mine implementing a new one, but more on that later. File formats are amazingly important in big data science. In genomics, it is hard not to be awed by how successful the BAM file format is. I thought… Read more »

Comparing BEAGLE, IMPUTE2, and Minimac Imputation Methods for Accuracy, Computation Time, and Memory Usage

Genotype imputation is a common and useful practice that allows GWAS researchers to analyze untyped SNPs without the cost of genotyping millions of additional SNPs. In the Services Department at Golden Helix, we often perform imputation on client data, and we have our own software preferences for a variety of reasons. However, other imputation software packages have their own advantages… Read more »

More Mixed Model Methods!

      Golden Helix    June 6, 2013    4 Comments on More Mixed Model Methods!

Thanks to everyone for the great webcast yesterday. We had over 850 people register for the event and actually broke the record! Take that Bryce and Gabe! If you would like to see the recording, view it at: Mixed Models: How to Effectively Account for Inbreeding and Population Structure in GWAS. While preparing for this webcast, we chose to focus… Read more »

The Murky Waters of Variant Nomenclature – You Could Be Missing Vital Information

When researchers realized they needed a way to report genetic variants in scientific literature using a consistent format, the Human Genome Variation Society (HGVS) mutation nomenclature was developed and quickly became the standard method for describing sequence variations. Increasingly, HGVS nomenclature is being used to describe variants in genetic variant databases as well. There are some practical issues that researchers… Read more »

The State of NGS Variant Calling: DON’T PANIC!!

I’m a believer in the signal. Whole genomes and exomes have lots of signal. Man, is it cool to look at a pile-up and see a mutation as clear as day that you arrived at after filtering through hundreds of thousands or even millions of candidates. When these signals sit right in the genomic “sweet spot” of mappable regions with… 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 Can Exomes Tell Us About the Pathology of Complex Disorders?

My investigation into my wife’s rare autoimmune disease I recently got invited to speak at the plenary session of AGBT about my experience in receiving and interpreting my Direct to Consumer (DTC) exomes. I’ve touched on this before in my post discussing my own exome and a caution for clinical labs setting up a GATK pipeline based on buggy variants… Read more »

Is Illumina Aiming to Compete with its Customers?

In a recent GenomeWeb article by Tony Fong, “Sequenom’s CEO ‘Puzzled’ by Illumina’s Buy of Verinata, Lays out 2013 Goals at JP Morgan,” Harry Hixson, Sequenom’s CEO, expresses puzzlement over why its major supplier, Illumina, is acquiring a Sequenom competitor in Non-Invasive Prenatal Testing (NIPT), and thus apparently competing with one of its major customers. In a JP Morgan interview… Read more »

GATK is a Research Tool. Clinics Beware.

In preparation for a webcast I’ll be giving on Wednesday on my own exome, I’ve been spending more time with variant callers and the myriad of false-positives one has to wade through to get to interesting, or potentially significant, variants. So recently, I was happy to see a message in my inbox from the 23andMe exome team saying they had… Read more »

Learning vs. Doing (or why that Ph.D. took 10 years)

What prevents scientists from being more productive and if we knew, could we do anything about it? I’d like to look at an often overlooked, but huge productivity inhibitor — bad multitasking. Many people put “excellent multitasker” on their resume as a badge of honor. We laud the efficiency of a good multitasker — they are rarely idle — someone… Read more »

One Track to Rule Them All: Close but not quite from the 1000 Genomes Project

I recently curated the latest population frequency catalog from the 1000 Genomes Project onto our annotation servers, and I had very high hopes for this track. First of all, I applaud 1000 Genomes for the amount of effort they have put in to providing the community with the largest set of high-quality whole genome controls available. My high hopes are… Read more »

Have We Wasted 7 Years and $100 Million Dollars on GWAS Studies?

Type 2 Diabetes, Rheumatoid Arthritis, Obesity, Chrohn’s Diseases and Coronary Heart Disease are examples of common, chronic diseases that have a significant genetic component. It should be no surprise that these diseases have been the target of much genetic research. Yet over the past decade, the tools of our research efforts have failed to unravel the complete biological architecture of… Read more »

Learning From Our GWAS Mistakes: From experimental design to scientific method

This month Biostatistics published online an open access article I co-authored with Dr. Laura Black from Montana State University: “Learning From Our GWAS Mistakes: From Experimental Design To Scientific Method.” The paper version is expected to come out in the April 2012 issue. I’m hoping that you will take the time to read it. And I’m hoping you will violently… Read more »

Introducing SVS 7.6!

      Delaina Hawkins    February 15, 2012    4 Comments on Introducing SVS 7.6!

It’s that time again! We here at Golden Helix are excited to announce SVS 7.6 with more features for DNA-Seq analysis, the addition of RNA-Seq functionality, reorganization of SVS into “packages” including two new ones, and the release of new plot types enabled by Matplotlib. It’s certainly been busy as we pack all this into the sixth installment of the… Read more »

“Dammit Jim, I’m a doctor, not a bioinformatician!”

Academic Software, Productivity, and Reproducible Research httpv://www.youtube.com/watch?v=pGMLCxKPMSE&NR=1 Do you ever feel like Dr. McCoy on Star Trek, where your job and expertise is to do x, but to achieve your goals you also have to do y and z, which you either don’t want to do or don’t have the skills to do? Genetic researchers are faced with this every… Read more »

Missing Heritability and the Future of GWAS

“Where is the missing heritability?” is a question asked frequently in genetic research, usually in the context of diseases that have large heritability estimates, say 60-80%, and yet where only perhaps 5-10% of that heritability has been found. The difficulty seems to come down to the common disease/common variant hypothesis not holding up. Or perhaps more accurately, that the frequency… Read more »

Enhanced ROH Analysis Improves Effectiveness to Identify Rare, Penetrant Recessive Loci

In the paper Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia, Todd Lencz, Ph.D. introduced a new way of doing association testing using SNP microarray platforms. The method, which he termed “whole genome homozygosity association”, first identifies patterned clusters of SNPs demonstrating extended homozygosity (runs of homozygosity or “ROHs”) and then employs both genome-wide and regionally-specific statistical tests… Read more »