Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Creator
  • Ehsani, Alireza
    • Other Affiliation: Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Tjele, DK-8830, Denmark
  • Sørensen, Peter
    • Other Affiliation: Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Tjele, DK-8830, Denmark
  • Pomp, Daniel
    • Affiliation: School of Medicine
  • Allan, Mark
    • Other Affiliation: Trans Ova Genetics, Sioux Center, Sioux, IA, 51250, USA
  • Janss, Luc
    • Other Affiliation: Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Tjele, DK-8830, Denmark
Abstract
  • Abstract Background To understand the genetic architecture of complex traits and bridge the genotype-phenotype gap, it is useful to study intermediate -omics data, e.g. the transcriptome. The present study introduces a method for simultaneous quantification of the contributions from single nucleotide polymorphisms (SNPs) and transcript abundances in explaining phenotypic variance, using Bayesian whole-omics models. Bayesian mixed models and variable selection models were used and, based on parameter samples from the model posterior distributions, explained variances were further partitioned at the level of chromosomes and genome segments. Results We analyzed three growth-related traits: Body Weight (BW), Feed Intake (FI), and Feed Efficiency (FE), in an F2 population of 440 mice. The genomic variation was covered by 1806 tag SNPs, and transcript abundances were available from 23,698 probes measured in the liver. Explained variances were computed for models using pedigree, SNPs, transcripts, and combinations of these. Comparison of these models showed that for BW, a large part of the variation explained by SNPs could be covered by the liver transcript abundances; this was less true for FI and FE. For BW, the main quantitative trait loci (QTLs) are found on chromosomes 1, 2, 9, 10, and 11, and the QTLs on 1, 9, and 10 appear to be expression Quantitative Trait Locus (eQTLs) affecting gene expression in the liver. Chromosome 9 is the case of an apparent eQTL, showing that genomic variance disappears, and that a tri-modal distribution of genomic values collapses, when gene expressions are added to the model. Conclusions With increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. Partitioning of explained variances at the chromosome and genome-segment level clearly separated regulatory and structural genomic variation as the areas where SNP effects disappeared/remained after adding transcripts to the model. The models that include transcripts explained more phenotypic variance and were better at predicting phenotypes than a model using SNPs alone. The predictions from these Bayesian models are generally unbiased, validating the estimates of explained variances.
Date of publication
DOI
Identifier
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Alireza Ehsani et al.; licensee BioMed Central Ltd.
License
Journal title
  • BMC Genomics
Journal volume
  • 13
Journal issue
  • 1
Page start
  • 456
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1471-2164
Bibliographic citation
  • BMC Genomics. 2012 Sep 05;13(1):456
Publisher
  • BioMed Central Ltd
Access right
  • Open Access
Date uploaded
  • January 15, 2013

Relations

Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
file details 1471-2164-13-456.pdf 2019-05-07 Public Download
file details Figure S4. 2019-05-07 Public Download
file details Figure S2. 2019-05-07 Public Download
file details Figure S3. Distribution of phenotypes of traits Body Weight including 440 animals, Feed Intake and Feed Efficiency including 337 animals each. 2019-05-07 Public Download
file details Figure S1. 2019-05-07 Public Download
file details 1471-2164-13-456.xml 2019-05-07 Public Download