In general, the standard practice for correcting for population stratification in genetic studies is to use principal components analysis (PCA) to categorize samples along different ethnic axes .  Price et al. published on this in 2006, and since then PCA plots are a common component of many published GWAS studies.

I’m a bit exhausted from a week of excellent science at ICHG. First, let me say that Montreal is a truly remarkable city with fantastic food and a fascinating blend of architectural styles, all making the meeting a fun place to be…. Now on to the genomics – I’ll recap a few of the most exciting sessions I attended. You can find a live-stream of tweets from the meeting by searching the #ICHG2011 and #ICHG hashtags.

This week, I'm off to Montreal for the International Congress on Human Genetics and I hope to see you there! If you are attending and are already a part of the Twitterverse, bring a tablet or phone and tweet away about the meeting using the official hashtag, #ICHG2011.

Like most bioinformatics nerds (or anyone with a facebook account), I’m fascinated by networks. Most people immediately think of protein-protein interaction networks, or biological pathways when thinking about networks, but sometimes representing a problem as a network makes solving problems easier. Recently, some collaborators from the PAGE study had a list of a few hundred SNPs gathered from multiple loci across the genome.

I just accepted an offer for a faculty position at the University of Virginia in the Center for Public Health Genomics / Department of Public Health Sciences. Starting in October I will be developing and directing a new centralized bioinformatics core in the UVA School of Medicine. Over the next few weeks I'm taking a much-needed vacation next door in Kauai and then packing up for the move to Charlottesville.

I just read Gregory Cooper and Jay Shendure's review "Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data" in Nature Reviews Genetics. It's a good review about how to narrow down deleterious disease-causing variants from many, many variants throughout the genome when statistics and genetic information alone isn't enough.

Daniel Cook in Jeff Murray's lab at the University of Iowa put together this handy Excel template for keeping track of how samples from four 96-well plates are interleaved to configure a single 384-well plate using robotic liquid handling systems, like the Hydra II. Paste in lists of samples on your 96-well plates: And you'll get out a map of how the 384-well plate layout: And a summary list: You can download the Excel file here.

I was recently contacted by a couple of German biologists working on a project evaluating opinions on sharing raw data from DTC genetic testing companies like 23andme. A handful of people like the gang at Genomes Unzipped, the PGP-10, and others at SNPedia have released their own genotype or sequencing data into the public domain. As of now, data like this is scattered around the web and most of it is not attached to any phenotype data.

I mentioned BioStar in a previous post about getting all your questions answered. I can't emphasize enough how helpful the BioStar and other StackExchange communities are. Whenever I ask a statistics question on CrossValidated or a programming question on StackOverflow I often multiple answers within 10 minutes.

Peter Visscher and colleagues have recently published a flurry of papers employing a new software package called GCTA to estimate the heritability of traits using GWAS data (GCTA stands for Genome-wide Complex Trait Analysis -- clever acronymity!). The tool, supported (and presumably coded) by Jian Yang is remarkably easy to use, based in part on the familiar PLINK commandline interface.

Your Rprofile is a script that R executes every time you launch an R session.