Multiple testing correction in linear mixed models. Journal Article
In: Genome Biol, 17 (1), pp. 62, 2016, ISSN: 1474-760X.
Mixed Models and Confounding Factors Talk @ Simons Institute
I recently gave a talk on mixed models and confounding factors which is a long time interest of our research group at a workshop which is part of the Evolutionary Biology and the Theory of Computing program which was held at the Simons Institute on the UC Berkeley Campus. The talk was held on February 21st. This talk spans many years of work in our group including work by Hyun Min Kang (now at Michigan), Noah Zaitlen (now at UCSF), and Jimmie Ye (now at Harvard) as well as a sneak peak at very recent work by Joanne Joo, Jae-Hoon Sul and Buhm Han.
The video of the talk is available here and is also on our YouTube Channel ZarlabUCLA.
The papers which are covered in the talk include the EMMA, EMMAX and ICE papers published in 2008 as well as a very new paper that should be coming out soon. The key papers from the talk are:
Variance component model to account for sample structure in genome-wide association studies. Journal Article
In: Nat Genet, 42 (4), pp. 348-54, 2010, ISSN: 1546-1718.
Efficient control of population structure in model organism association mapping. Journal Article
In: Genetics, 178 (3), pp. 1709-23, 2008, ISSN: 0016-6731.
Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots. Journal Article
In: Genetics, 180 (4), pp. 1909-25, 2008, ISSN: 0016-6731.
Emrah Kostem’s talk about his research
Emrah Kostem, who graduated this year and is now at Illumina, gave a talk about the research he completed in the lab this summer at our retreat. It is available here and gives a good overview of what the goals of our group are and some details of the projects that Emrah completed in the lab.
One of the topics he discusses is his recently published work on estimating heritability, which is quantifying the amount that genetics accounts for the variance of a trait. He discusses his work on how to partition heritability into the contributions of genomic regions(10.1016/j.ajhg.2013.03.010).
He also talks about his work which takes advantage of the insight that association statistics follow the multivariate normal distribution and applies this to two problems. The first is the problem of selecting follow up SNPs using the results of an association study(10.1534/genetics.111.128595). The second problem is the problem of speeding up eQTL studies using a two stage approach where only a fraction of the association tests are performed but virtually all of the significant associations are still discovered(10.1089/cmb.2013.0087).
Details of what he talked about are in his papers:
Improving the accuracy and efficiency of partitioning heritability into the contributions of genomic regions. Journal Article
In: Am J Hum Genet, 92 (4), pp. 558-64, 2013, ISSN: 1537-6605.
Efficiently Identifying Significant Associations in Genome-wide Association Studies. Journal Article
In: J Comput Biol, 20 (10), pp. 817-30, 2013, ISSN: 1557-8666.
Increasing Power of Genome-wide Association Studies by Collecting Additional SNPs. Journal Article
In: Genetics, 2011, ISSN: 1943-2631.