scholarly journals The Convergence of Systems and Reductionist Approaches in Complex Trait Analysis

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

scholarly journals Resolving the Effects of Maternal and Offspring Genotype on Dyadic Outcomes in Genome Wide Complex Trait Analysis (“M-GCTA”)

2014 ◽  
Vol 44 (5) ◽  
pp. 445-455 ◽  
Author(s):  
Lindon J. Eaves ◽  
Beate St. Pourcain ◽  
George Davey Smith ◽  
Timothy P. York ◽  
David M. Evans
Keyword(s):  
Complex Trait ◽  
Trait Analysis ◽  
Genome Wide ◽  
Offspring Genotype

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
Keyword(s):  
Complex Trait ◽  
Parallel Architectures ◽  
Trait Analysis

scholarly journals Complex-trait analysis in plants

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

scholarly journals Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease

2013 ◽  
Vol 22 (14) ◽  
pp. 2973-2973
Author(s):  
M. F. Keller ◽  
M. Saad ◽  
J. Bras ◽  
F. Bettella ◽  
N. Nicolaou ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Complex Trait ◽  
Missing Heritability ◽  
Trait Analysis ◽  
Genome Wide

scholarly journals Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 919-929
Author(s):  
Daniel A. Skelly ◽  
Narayanan Raghupathy ◽  
Raymond F. Robledo ◽  
Joel H. Graber ◽  
Elissa J. Chesler
Keyword(s):  
Gene Expression ◽  
Canonical Correlation ◽  
Complex Traits ◽  
Behavioral Genetics ◽  
Association Studies ◽  
Complex Trait ◽  
Integrated Analysis ◽  
Data Set ◽  
Trait Analysis ◽  
Molecular Features

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.

scholarly journals Finding the molecular basis of complex genetic variation in humans and mice

2006 ◽  
Vol 361 (1467) ◽  
pp. 393-401 ◽  
Author(s):  
Richard Mott
Keyword(s):  
Genetic Variation ◽  
Association Mapping ◽  
Molecular Basis ◽  
State Of The Art ◽  
Complex Trait ◽  
The State ◽  
Rodent Model ◽  
Model Systems ◽  
Trait Analysis

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.

Genetic structure of the LXS panel of recombinant inbred mouse strains: a powerful resource for complex trait analysis

2004 ◽  
Vol 15 (8) ◽  
pp. 637-647 ◽  
Author(s):  
Robert W. Williams ◽  
Beth Bennett ◽  
Lu Lu ◽  
Jing Gu ◽  
John C. DeFries ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Recombinant Inbred ◽  
Inbred Mouse ◽  
Complex Trait ◽  
Mouse Strains ◽  
Inbred Mouse Strains ◽  
Recombinant Inbred Mouse ◽  
Trait Analysis

scholarly journals A new broccoli × broccoli immortal mapping population and framework genetic map: tools for breeders and complex trait analysis

2011 ◽  
Vol 124 (3) ◽  
pp. 467-484 ◽  
Author(s):  
Peter Glen Walley ◽  
John Carder ◽  
Emma Skipper ◽  
Evy Mathas ◽  
James Lynn ◽  
...  
Keyword(s):  
Genetic Map ◽  
Mapping Population ◽  
Complex Trait ◽  
Trait Analysis
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