Finding Genes That Underlie Complex Traits

Glazier, Anne M.; Nadeau, Joseph H.; Aitman, Timothy J.

doi:10.1126/science.1076641

Cited by 749 publications

(543 citation statements)

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“…With the completion of the human and mouse genome projects, genetic analysis has become a standard approach for identifying alleles for susceptibility to various diseases with complex phenotypes (20). The cumulative effect of such susceptibility genes resulting from genome crossing may be responsible for the diversity of pathologic manifestations of diseases.…”

Section: Discussionmentioning

confidence: 99%

Accelerating effect of an MRL gene locus on the severity and onset of arthropathy in DBA/1 mice

Oishi¹,

Miyazaki²,

Mizuki³

et al. 2005

Arthritis & Rheumatism

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Objective. To analyze the influence of the genetic background of an arthritis-prone strain of mice, MRL, on the spontaneous development of arthropathy in DBA/1 mice, which histopathologically resembles enthesopathy in humans, and to clarify the strain-specific gene loci and their interactions that confer susceptibility to arthropathy.Methods. MRL, DBA/1, (MRL ؋ DBA/1)F 1 , and (MRL ؋ DBA/1)F 2 intercross mice were prepared, and the severity and onset of arthropathy of the ankle joints in individual mice were quantified (0-3 and 0-5 scale, respectively). A genome-wide scan of 271 male F 2 intercross mice with polymorphic microsatellite markers was performed.Results. Only male DBA/1, (MRL ؋ DBA/1)F 1 , and (MRL ؋ DBA/1)F 2 mice developed arthropathy. The macroscopic and histopathologic findings of arthropathy in the F 2 mice were similar to those in the parental DBA/1 mice, but the onset was significantly earlier. In the quantitative trait locus analysis of male F 2 mice, 1 susceptibility locus for both the severity and early onset of the disease in the region of an MRL allele, Amd1, was located at marker D10Mit259 (map position 40.0 cM), which was common to 1 of the sialadenitis susceptibility loci in MRL mice, Asm1. Another susceptibility locus for the severity and early onset of arthropathy in the region of a DBA allele, Amd2, was located at D3Mit46 (29.5 cM). These loci manifested an additive effect on the development of arthropathy.Conclusion. Arthropathy in DBA/1 mice is under the control of an allelic combination of gene loci, one of which is common to the locus for sialadenitis in MRL/ MpJ-lpr/lpr mice.

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Section: Discussionmentioning

confidence: 99%

Accelerating effect of an MRL gene locus on the severity and onset of arthropathy in DBA/1 mice

Oishi¹,

Miyazaki²,

Mizuki³

et al. 2005

Arthritis & Rheumatism

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“…1,2 Although the rate at which Mendelian disorders have been genetically characterized has increased, 3 similar rates have not been obtained for the elucidation of polygenic diseases. 4,5 A significant fraction of bioinformatics research in the post-genomic era has focused on decoding genomic information through the use of different annotation methods. [6][7][8][9] Such annotation provides numerous opportunities to infer gene function.…”

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confidence: 99%

The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies

et al. 2004

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The size complexity of the human genome has been traditionally viewed as an obstacle that frustrates efforts aimed at identifying the genetic correlates of complex human phenotypes. As such complex phenotypes are attributed to the combined action of numerous genomic loci, attempts to identify the underlying multi-locus interactions may produce a combinatorial sum of false positives that drown out the real signal. Faced with such grim prospects for successfully identifying the genetic basis of complex phenotypes, many geneticists simply disregard epistatic interactions altogether. However, the emerging picture from systems biology is that the cellular programs encoded by the genome utilize nested signaling hierarchies to integrate a number of loosely coupled, semiautonomous, and functionally distinct genetic networks. The current view of these modules is that connections encoding inter-module signaling are relatively sparse, while the gene-to-gene (protein-to-protein) interactions within a particular module are typically denser. We believe that each of these modules is encoded by a finite set of discontinuous, sequence-specific, genomic intervals that are functionally linked to association rules, which correlate directly to features in the environment. Furthermore, because these environmental association rules have evolved incrementally over time, we explore theoretical models of cellular evolution to better understand the role of evolution in genomic complexity. Specifically, we present a conceptual framework for (1) reducing genomic complexity by partitioning the genome into subsets composed of functionally distinct genetic modules and (2) improving the selection of coding region SNPs, which results in an increased probability of identifying functionally relevant SNPs. Additionally, we introduce the notion of 'genomic closure,' which provides a quantitative measure of how functionally insulated a specific genetic module might be from the influence of the rest of the genome. We suggest that the development and use of theoretical models can provide insight into the nature of biological systems and may lead to significant improvements in computational algorithms designed to reduce the complexity of the human genome.

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“…[1][2][3] The strategy widely used until now in humans is based on association studies between the disease and polymorphisms in candidate genes. [4][5][6] The choice of the genes of interest is based on the biological plausibility of their involvement in the pathophysiology of the disease.…”

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confidence: 99%

“…[1][2][3] In contrast to the candidate gene approach, the genome-wide scan allows the identification of genetic factors responsible for a phenotype, without prior hypotheses on their role in the pathophysiology of the disease. Since the strategy is based on linkage analyses, which test the cosegregation of the phenotype with multiple genetic polymorphisms located throughout the genome, the strategy is theoretically able to detect all the genetic factors involved regarding the population studied.…”

mentioning

confidence: 99%

“…The approach is particularly complex in human populations where the modest phenotypic effect of a modifier gene is combined with a large genetic variability and the absence of standardised environmental factors. 1,2 To overcome these difficulties, Le Corvoisier et al 11 recently reported interesting results with a specific strategy in a murine model. The authors identified multiple quantitative trait loci (QTL) that modify the heart failure phenotype in an experimental mouse model of dilated cardiomyopathy induced by cardiac specific overexpression of calsequestrin.…”

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confidence: 99%

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