Gene–environment interactions in complex diseases: genetic models and methods for QTL mapping in multiple half-sib populations

Kadarmideen, Haja N.; Li, Y.; Janss, Luc

doi:10.1017/s0016672306008391

Cited by 6 publications

(3 citation statements)

References 13 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is well known that complex traits and clinical diseases in most species are not determined by individual genes acting alone but rather in complex multi-gene interactions. 18,32 In many cases, it is the gene-gene interactions that cause phenotypic variation in complex diseases or traits rather than the marginal effects of individual genes. 18,32,45 Hence, new approaches are warranted to capture such interacting genes.…”

Section: Differential Gene Expression Analysismentioning

confidence: 99%

See 1 more Smart Citation

Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers

2011

Self Cite

View full text Add to dashboard Cite

This study reports on the molecular systems biology of gastrointestinal nematode (GIN) infection and potential biomarkers for GIN resistance in sheep. Microarray gene expression data were obtained for 3 different tissues at 4 time points from sheep artificially challenged with two types of nematodes, Haemonchus contortus (HC) and Trichostrongylus colubriformis (TC). We employed an integrated systems biology approach, integrating 3 main methods: standard differential gene expression analyses, weighted gene co-expression network analyses (WGCNA) and quantitative genetic analyses of gene expression traits of key biomarkers. Using standard differential gene expression analyses we identified differentially expressed genes (DE) which responded differently in sheep challenged with HC compared to those challenged with TC. These interaction genes (e.g. MRPL51, SMEK2, CAT, MAPK1IP1 and SLC25A20A) were enriched in Wnt receptor signalling pathway (p = 0.0132) and positive regulation of NFκβ transcription factor activity (p = 0.00208). We report FCER1A, a gene encoding a high-affinity receptor for the Fc region of immunoglobulin E, which is linked to innate immunity to GIN in sheep. Using weighted gene co-expression network analysis (WGCNA) methods, we identified gene modules that were correlated with the length of infection (disease modules). Hub genes (with high intramodular connectivity) were filtered further to identify biomarkers that are related to the length of infection (e.g. CAT, FBX033, COL15A1, IGFBP7, FBLN1 and IgCgamma). The biomarkers we found in HC networks were significantly associated with functions such as T-cell and B-cell regulations, TNF-alpha, interleukin and cytokine production. In TC networks, biomarkers were significantly associated with functions such as protein catabolic process, heat shock protein binding, protein targeting and localization, cytokine receptor binding, TNF receptor binding, apoptosis and IGF binding. These results provide specific gene targets for therapeutic interventions and provide insights into GIN infections in sheep which may be used to infer the same in related host species. This is also the first study to apply the concept of estimating breeding values of animals to expression traits and reveals 11 heritable candidate biomarkers (0.05 to 0.92) that could be used in selection of animals for GIN resistance.

show abstract

Section: Differential Gene Expression Analysismentioning

confidence: 99%

“…18,32 In many cases, it is the gene-gene interactions that cause phenotypic variation in complex diseases or traits rather than the marginal effects of individual genes. 18,32,45 Hence, new approaches are warranted to capture such interacting genes.…”

Section: Differential Gene Expression Analysismentioning

confidence: 99%

Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers

2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have specifically avoided discussing G × E × M interactions that exist within breed between animals at the level of genes which respond to different environments and/or different management interventions. These are extensively discussed in Kadarmideen et al. (2006a,b) and are outside the scope of this study.…”

Section: Genotype By Environment By Management Interaction In Cattlementioning

confidence: 99%

Evolutionary process of Bos taurus cattle in favourable versus unfavourable environments and its implications for genetic selection

O’Neill

Swain

Kadarmideen

2010

Evolutionary Applications

Self Cite

View full text Add to dashboard Cite

The evolutionary processes that have enabled Bos taurus cattle to establish around the globe are at the core to the future success of livestock production. Our study focuses on the history of cattle domestication including the last 60 years of B. taurus breeding programmes in both favourable and unfavourable environments and its consequences on evolution and fitness of cattle. We discuss the emergence of ‘production diseases’ in temperate production systems and consider the evolutionary genetics of tropical adaptation in cattle and conclude that the Senepol, N'Dama, Adaptaur and Criollo breeds, among others with similar evolutionary trajectories, would possess genes capable of improving the productivity of cattle in challenging environments. Using our own experimental evidence from northern Australia, we review the evolution of the Adaptaur cattle breed which has become resistant to cattle tick. We emphasize that the knowledge of interactions between genotype, environment and management in the livestock systems will be required to generate genotypes for efficient livestock production that are both economically and environmentally sustainable. Livestock producers in the 21st century will have less reliance on infrastructure and veterinary products to alleviate environmental stress and more on the animal's ability to achieve fitness in a given production environment.

show abstract

From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding

2006

Self Cite

View full text Add to dashboard Cite

This article reviews methods of integration of transcriptomics (and equally proteomics and metabolomics), genetics, and genomics in the form of systems genetics into existing genome analyses and their potential use in animal breeding and quantitative genomic modeling of complex traits. Genetical genomics or the expression quantitative trait loci (eQTL) mapping method and key findings in this research are reviewed. Various procedures and potential uses of eQTL mapping, global linkage clustering, and systems genetics are illustrated using actual analysis on recombinant inbred lines of mice with data on gene expression (for diabetes- and obesity-related genes), pathway, and single nucleotide polymorphism (SNP) linkage maps. Experimental and bioinformatics difficulties and possible solutions are discussed. The main uses of this systems genetics approach in quantitative genomics were shown to be in refinement of the identified QTL, candidate gene and SNP discovery, understanding gene-environment and gene-gene interactions, detection of candidate regulator genes/eQTL, discriminating multiple QTL/eQTL, and detection of pleiotropic QTL/eQTL, in addition to its use in reconstructing regulatory networks. The potential uses in animal breeding are direct selection on heritable gene expression measures, termed “expression assisted selection,” and genetical genomic selection of both QTL and eQTL based on breeding values of the respective genes, termed “expression-assisted evaluation.”

show abstract

Gene–environment interactions in complex diseases: genetic models and methods for QTL mapping in multiple half-sib populations

Cited by 6 publications

References 13 publications

Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers

Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers

Evolutionary process of Bos taurus cattle in favourable versus unfavourable environments and its implications for genetic selection

From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding

Contact Info

Product

Resources

About