Month: March 2015

Simultaneous Genetic Analysis of more than One Trait

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Most methods that try to understand the relationship between an individual’s genetics and traits analyze one trait at a time. Our lab recently published a paper focusing on analyzing multiple traits together. This subject is significant because analyzing multiple traits can discover more genetic variants that affect traits, but the analysis methods are challenging and often very computationally inefficient. This is especially the case for mixed-model methods which take into account the relatedness among individuals in the study. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation that is a measure of the portion of the total correlation between traits that is due to additive genetic effects.

In our recent paper, we aim to solve this problem by introducing a technique that can be used to assess genome-wide association quickly, reducing analysis time from hours to seconds. Our method is called a Matrix Variate Linear Mixed Model (mvLMM) and is similar to the method recently developed by Mathew Stephen’s group ((22706312)). Our method is available as a software which works together with the pylmm software that we are developing on mixed models which is available at http://genetics.cs.ucla.edu/pylmm/. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM/.

We demonstrate the efficacy of our method by analyzing correlated traits in the Northern Finland Birth Cohort ((19060910)). Comparing to a standard approach ((22843982); (22902788)), we show that our method results in more than a 10-fold time reduction for a pair of correlated traits, taking the analysis time from about 35 minutes to about 2.5 minutes for the cubic operations plus another 12 seconds for the iterative part of the algorithm. In addition, the cubic operation can be saved so that it does not have to be re-calculated when analyzing other traits in the same cohort. Finally, we demonstrate how this method can be used to analyze gene expression data. Using a well-studied yeast dataset ((18416601)), we show how estimation of the genetic and environmental components of correlation between pairs of genes allows us for to understand the relative contribution of genetics and environment to coexpression.

One of the key ideas of our approach is to represent the multiple phenotypes as a matrix where the rows are individuals and the columns are traits. We then assume the data follows a “matrix variate normal” distribution where we define a covariance structure on the trait among the rows (individuals) and columns (traits). The use of the matrix variate normal is the key to making our algorithm efficient.

The full paper about mvLMM is below:

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**Update** Since publishing, it has been brought to our attention there is related work published by Karin Meyer in 1985 (which cited earlier work by Robin Thompson from 1976) we did not cite. If our method interests you, please also take a moment to review the following paper:

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US-Israel Binational Science Foundation and Gilbert Foundation Renew Support

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We are very happy to announce the US-Israel Binational Science Foundation (BSF) in partnership with the Gilbert Foundation are renewing support of our collaboration with Eran Halperin’s group in Tel Aviv University. This is our labs oldest active collaboration which began in 2001 when Professor Eskin met Eran Halperin at the RECOMB conference.

Our first joint project in genetics was a collaboration with Eran Halperin in 2003 (who was in Berkeley, CA at the time) on a problem called haplotype phasing and led to the software HAP ((14988101)). That led us to become involved in the first whole-genome map of human variation, which was published on the cover of Science in 2005 ((15718463)). We have continued to work closely and publish together because we have very complementary backgrounds. We came from machine learning and Eran come from theory. We have many joint projects, regular conference calls and visits, and collaborations between our students. One of my Ph.D. students was a post doc in Professor Halperin’s group and one of his post docs was recruited to UCLA as a faculty member.

Many of our most important research contributions have been jointly authored papers. This includes our work on characterizing genetic diversity using spatial ancestry analysis (SPA-(22610118)) and genotyping common and rare variants in very large population studies using overlapping pool sequencing, which can be used for the detection of cancer fusion genes from RNA sequences ((21989232)).

Thanks to the additional funding from BSF, we are expanding our current goals to address the problem of analysis of genetic data in conjunction with other data types such as epigenetic data (changes to the DNA along one’s lifetime) and RNA expression. There is strong evidence that these additional signals can provide more insights to the mechanisms of the disease, for example, epigenetic changes have been shown to be strongly related to certain diseases and environmental effects.

Further, the project enables an exchange of ideas and collaborations between not only myself and Eran but also between our students. Everyone involved benefits from this collaboration of Israeli and American scientists. This is our first BSF project and we are very grateful for the support of our collaboration.

To read the full article on our collaboration and the BSF, please click here.

Bibliography