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The influence of disease categories on gene candidate predictions from model organism phenotypes.

ABSTRACT: The molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have recently developed Exomiser as a tool for identifying causative variants from exome analysis results by filtering and prioritising using a number of criteria including the phenotype similarity between the disease and mouse mutants involving the gene candidates. Initial investigations revealed a variation in performance for different medical categories of disease, due in part to a varying contribution of the phenotype scoring component.In this study, we further analyse the performance of our cross-species phenotype matching algorithm, and examine in more detail the reasons why disease gene filtering based on phenotype data works better for certain disease categories than others. We found that in addition to misleading phenotype alignments between species, some disease categories are still more amenable to automated predictions than others, and that this often ties in with community perceptions on how well the organism works as model.In conclusion, our automated disease gene candidate predictions are highly dependent on the organism used for the predictions and the disease category being studied. Future work on computational disease gene prediction using phenotype data would benefit from methods that take into account the disease category and the source of model organism data.

SUBMITTER: Oellrich A

PROVIDER: S-EPMC4108905 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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The influence of disease categories on gene candidate predictions from model organism phenotypes.

Oellrich Anika A Koehler Sebastian S Washington Nicole N Mungall Chris C Lewis Suzanna S Haendel Melissa M Robinson Peter N PN Smedley Damian D

Journal of biomedical semantics 20140603 Suppl 1 Proceedings of the Bio-Ontologies Spec Interest

<h4>Background</h4>The molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have rec ...[more]

PMID: 25093073

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Project description:IntroductionGenetic studies of malocclusion etiology have identified 4 deleterious mutations in genes DUSP6,ARHGAP21, FGF23, and ADAMTS1 in familial Class III cases. Although these variants may have large impacts on Class III phenotypic expression, their low frequency (<1%) makes them unlikely to explain most malocclusions. Thus, much of the genetic variation underlying the dentofacial phenotypic variation associated with malocclusion remains unknown. In this study, we evaluated associations between common genetic variations in craniofacial candidate genes and 3-dimensional dentoalveolar phenotypes in patients with malocclusion.MethodsPretreatment dental casts or cone-beam computed tomographic images from 300 healthy subjects were digitized with 48 landmarks. The 3-dimensional coordinate data were submitted to a geometric morphometric approach along with principal component analysis to generate continuous phenotypes including symmetric and asymmetric components of dentoalveolar shape variation, fluctuating asymmetry, and size. The subjects were genotyped for 222 single-nucleotide polymorphisms in 82 genes/loci, and phenotpye-genotype associations were tested via multivariate linear regression.ResultsPrincipal component analysis of symmetric variation identified 4 components that explained 68% of the total variance and depicted anteroposterior, vertical, and transverse dentoalveolar discrepancies. Suggestive associations (P < 0.05) were identified with PITX2, SNAI3, 11q22.2-q22.3, 4p16.1, ISL1, and FGF8. Principal component analysis for asymmetric variations identified 4 components that explained 51% of the total variations and captured left-to-right discrepancies resulting in midline deviations, unilateral crossbites, and ectopic eruptions. Suggestive associations were found with TBX1AJUBA, SNAI3SATB2, TP63, and 1p22.1. Fluctuating asymmetry was associated with BMP3 and LATS1. Associations for SATB2 and BMP3 with asymmetric variations remained significant after the Bonferroni correction (P <0.00022). Suggestive associations were found for centroid size, a proxy for dentoalveolar size variation with 4p16.1 and SNAI1.ConclusionsSpecific genetic pathways associated with 3-dimensional dentoalveolar phenotypic variation in malocclusions were identified.

Project description:BackgroundHuman African trypanosomiasis (HAT), a lethal disease induced by Trypanosoma brucei gambiense, has a range of clinical outcomes in its human host in West Africa: an acute form progressing rapidly to second stage, spontaneous self-cure and individuals able to regulate parasitaemia at very low levels, have all been reported from endemic foci. In order to test if this clinical diversity is influenced by host genetic determinants, the association between candidate gene polymorphisms and HAT outcome was investigated in populations from HAT active foci in Guinea.Methodology and resultsSamples were collected from 425 individuals; comprising of 232 HAT cases, 79 subjects with long lasting positive and specific serology but negative parasitology and 114 endemic controls. Genotypes of 28 SNPs in eight genes passed quality control and were used for an association analysis. IL6 rs1818879 allele A (p = 0.0001, OR = 0.39, CI95 = [0.24-0.63], BONF = 0.0034) was associated with a lower risk of progressing from latent infection to active disease. MIF rs36086171 allele G seemed to be associated with an increased risk (p = 0.0239, OR = 1.65, CI95 = [1.07-2.53], BONF = 0.6697) but did not remain significant after Bonferroni correction. Similarly MIF rs12483859 C allele seems be associated with latent infections (p = 0.0077, OR = 1.86, CI95 = [1.18-2.95], BONF = 0.2157). We confirmed earlier observations that APOL1 G2 allele (DEL) (p = 0.0011, OR = 2.70, CI95 = [1.49-4.91], BONF = 0.0301) is associated with a higher risk and APOL1 G1 polymorphism (p = 0.0005, OR = 0.45, CI95 = [0.29-0.70], BONF = 0.0129) with a lower risk of developing HAT. No associations were found with other candidate genes.ConclusionOur data show that host genes are involved in modulating Trypanosoma brucei gambiense infection outcome in infected individuals from Guinea with IL6 rs1818879 being associated with a lower risk of progressing to active HAT. These results enhance our understanding of host-parasite interactions and, ultimately, may lead to the development of new control tools.

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The influence of disease categories on gene candidate predictions from model organism phenotypes.

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The influence of disease categories on gene candidate predictions from model organism phenotypes.

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