scholarly journals Regional heritability advanced complex trait analysis for GPU and traditional parallel architectures

2014 ◽  
Vol 30 (8) ◽  
pp. 1177-1179 ◽  
Author(s):  
L. Cebamanos ◽  
A. Gray ◽  
I. Stewart ◽  
A. Tenesa
2014 ◽  
Vol 44 (5) ◽  
pp. 445-455 ◽  
Author(s):  
Lindon J. Eaves ◽  
Beate St. Pourcain ◽  
George Davey Smith ◽  
Timothy P. York ◽  
David M. Evans

2010 ◽  
Vol 11 (4) ◽  
pp. 113 ◽  
Author(s):  
Thomas Mitchell-Olds
Keyword(s):  

2013 ◽  
Vol 22 (14) ◽  
pp. 2973-2973
Author(s):  
M. F. Keller ◽  
M. Saad ◽  
J. Bras ◽  
F. Bettella ◽  
N. Nicolaou ◽  
...  

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 919-929
Author(s):  
Daniel A. Skelly ◽  
Narayanan Raghupathy ◽  
Raymond F. Robledo ◽  
Joel H. Graber ◽  
Elissa J. Chesler

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.


Cell ◽  
2015 ◽  
Vol 162 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Evan G. Williams ◽  
Johan Auwerx
Keyword(s):  

2006 ◽  
Vol 361 (1467) ◽  
pp. 393-401 ◽  
Author(s):  
Richard Mott

I survey the state of the art in complex trait analysis, including the use of new experimental and computational technologies and resources becoming available, and the challenges facing us. I also discuss how the prospects of rodent model systems compare with association mapping in humans.


2004 ◽  
Vol 15 (8) ◽  
pp. 637-647 ◽  
Author(s):  
Robert W. Williams ◽  
Beth Bennett ◽  
Lu Lu ◽  
Jing Gu ◽  
John C. DeFries ◽  
...  

2011 ◽  
Vol 124 (3) ◽  
pp. 467-484 ◽  
Author(s):  
Peter Glen Walley ◽  
John Carder ◽  
Emma Skipper ◽  
Evy Mathas ◽  
James Lynn ◽  
...  

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