Mixed linear model approach adapted for genome-wide association studies

A few weeks ago I covered an R package for efficient mixed model regression that is capable of simultaneously accounting for both population stratification and relatedness to compute unbiased estimates of standard errors and p-values for genetic association studies. Fitting linear mixed effects models on GWAS scale can be very time consiuming, however, and another group recently reported a method that fits a mixed linear model very efficiently by clustering individuals into groups and eliminating the need to recompute variance components. They showed that using their modifications, they were able to reduce computation time by more than 800-fold over SAS proc mixed / SAS proc cluster. Check out the paper for more details.

Nature Genetics: Mixed linear model approach adapted for genome-wide association studies

Abstract: Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.

Introduction to single molecule real time (SMRT) sequencing from Pac Bio's Curtis Fideler

Definitely a seminar not to miss: Curtis Fideler, director of Sales at Pacific Biosciences, will be giving a special seminar here at Vanderbilt Thursday, May 20, 11:00a-noon in 202 Light Hall entitled "An Introduction to SMRT Sequencing: A description of Pacific Biosciences single molecule real time sequencing technology."

Science: Real-Time DNA Sequencing from Single  Polymerase Molecules

Wikipedia: Single Molecule Real Time Sequencing

Pacific Biosciences: SMRT Overview

More on the McClellan / King GWAS essay

First, if you haven't taken a look at the comments on my previous post on this paper, go take a look. Thanks to everyone for sharing your thoughts and pointing out some of my own oversight regarding this paper.

There was one issue in particular that deserves more attention than just another comment thread. McClellan and King draw special attention to a study by Kai Wang et al (2009) Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459:528. A big thanks to Kai Wang for pointing out this particularly egregious misrepresentation by McClellan and the emphasis I added in my own all-too-cursory review. McClellan discuss rs4307059, reported by Wang et al. to be associated with autism, as a “particularly dramatic example of the perils of cryptic population stratification”, reasoning that the substructure is a result of large frequency differences across Europe and its fixation in Africa, when in fact the frequency of this SNP is fairly consistent across large cohorts of European ancestry: European Americans (MAF=39%), WTCCC (MAF=38%), POPRES British (MAF=39%), POPRES Spanish (MAF=37%). The extreme estimates (.21-.77) come from extremely small sample sizes (n=7 in Tuscany, MAF=75%, and n=15 in the Orcadian sample, MAF=25%). These sample sizes are way to small to estimate allele frequencies with any stability. In fact, you can see the allele frequency distribution across 51 populations here, which shows that it's quite similar across most of Europe:

Further, using the full Fst data set (which can be downloaded directly at this link), if you sort all Illumina SNPs by their variation of allele frequencies (more precisely, Fst), the SNP rs4307059 lies right in middle, so it is fairly normal for any SNP with similar MAF to display variation of allele frequencies in subpopulations in Europe or in HapMap.

There are a few other issues pointed out in the comment thread that deserve attention. McClellan asserted, and I emphasized, that most GWAS hits do not replicate. While it's definitely true that nonreplication was a huge issue in genetic association studies in the past and in the early days of GWAS, most GWAS hits that are genome-wide significant (e.g. p<1e-8) DO replicate, and studies done with family designs, which can't be explained away by population stratification, add further evidence that many of these associations are genuine. And simply because a SNP lies outside a region with known biological function doesn't mean we should wave it off so easily. There's a nice discussion of this over at Gene Expression.

Discovering New Disease Genes Using Orthologous Phenotypes in Model Organisms

Check out this paper in PNAS and the corresponding synopsis in the New York Times. The authors take a unique approach to finding genes likely to be associated with human traits using orthologous phenotypes in model organisms, or phenologs. The idea is simple. The authors have a database of ~2000 disease associated genes in humans. To this database they added another ~200,000 gene-trait associations in model organisms including mice, yeast, worm, and plants. Then they look for overlapping sets of orthologous genes from these organisms to identify phenotypes in the model organisms. The related genes causing orthologous phenotypes, or phenologs, are predictive of genes causing disease in humans. For example, the authors found genes responsible for angiogenesis using yeast, breast cancer associated genes in C. elegans, and even genes responsible for deafness using plants.

I remember seeing a talk about this at this year's Pacific Symposium in Biocomputing. You can learn more about the methodology at phenologs.org, and download all the original data used in the paper and build your own phenolog database, which could be very useful for disease gene prediction or prioritization of GWAS hits for followup. 

PNAS: Systematic discovery of nonobvious human disease models through orthologous phenotypes

New York Times: The Search for Genes Leads to Unexpected Places

phenologs.org: Systematic discovery of non-obvious disease models and candidate genes

How today's scientific culture affects young scientists (This is good.)

Here's a very good 3-page essay on how modern scientific policy and culture (e.g. short-term funding, unstable job security, publish-or-perish mindset) is adversely affecting young scientists, causing lots of bright minds to abandon academia in search of other careers (via @WileyScience).

BioEssays: How today's scientific culture affects young scientists

Anyone care to comment?

Abstract: Surviving in academia has become a headache for many young scientists. But not only for them - older researchers too are increasingly preoccupied with the state of science policy and the procedures of scientific evaluation. We here analyze the pressures that prospective scientists like us feel today and compare them to what researchers from the past witnessed. What emerges is that science has undergone a profound cultural change that would have prevented some scholars from the 19th century from making their breakthroughs. While the inner motivation of most scientists at that time was to satisfy their appetite for knowledge, the modern raison d'être of scientists mostly addresses how to provide a living for themselves. Nevertheless, the general awareness of this situation amongst scientists suggests that there is space for an open debate and reform.

Top 10 Algorithms in Data Mining

The authors here invited ACM KDD Innovation Award and IEEE ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining, including the algorithm name, justification for nomination, and a representative publication reference. The list was voted on by other IEEE and ACM award winners to narrow this down to a top 10 list. These algorithms are used for association analysis, classification, clustering, statistical learning, and much more.You can read the paper here.

Here are the winners:

1. C4.5
2. The k-Means algorithm
3. Support Vector Machines
4. The Apriori algorithm
5. Expectation-Maximization
6. PageRank
7. AdaBoost
8. k-Nearest Neighbor Classification
9. Naive Bayes
10. CART (Classification and Regression Trees)

The 2007 paper gives a brief overview of what the method is commonly used for and how it works, along with lots of references. It also has a much more detailed description of how these winners were selected than what I've said here.

The exciting thing is I've seen nearly all of these algorithms used for mining genetic data for complex patterns of genetic and environmental exposures that influence complex disease. See some recent papers at EvoBio and PSB. Further, lots of these methods are implemented in several R packages.

Top 10 Algorithms in Data Mining (PDF)

Havasupai Indians and the Ethical Use of Data

The recent settlement between Arizona State University and the Havasupai Indian tribe is calling attention to (and perhaps challenging) the ideas of informed consent. While I'm sure there are arguments to be made supporting both sides of this case, regardless of your position this is an excellent reminder that there are people's lives behind the alleles in our spreadsheets and PED files.

http://www.nytimes.com/2010/04/22/us/22dna.html

Checklist: Statistical Problems to Document and Avoid

Update 2010-04-21: I forgot to post the link last time. That would have been helpful. Here you go:

Vanderbilt Biostatistics: Statistical Problems to Document and to Avoid

.....

At the Regression Modeling Strategies course I attended a few weeks ago, Frank Harrell pointed out the checklist on the biostatistics department's website of statistical problems to document and avoid. It was recommended that authors of any paper employing statistical analysis should go through this checklist before writing and submitting a manuscript.  Some of the topics include:

• Design and sample size issues
• Inefficient use of continuous variables (don't categorize!)
• Assumptions of parametric tests
• Inappropriate analysis of repeated measures data
• P-value interpretation
• Filtering results
• Missing data
• Multiple testing concerns
• Model building and specification
• Use of stepwise variable selection (don't do it)
• Overfitting

Be sure to check this out before writing up results, and ideally before you even plan any experiments, especially if you are relatively new to quantitative analysis.