Dataset Information

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.

ABSTRACT: With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer's Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and 2 types of biomarkers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression.

SUBMITTER: Wang X

PROVIDER: S-EPMC5912922 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.

Wang Xiaoqian X Yan Jingwen J Yao Xiaohui X Kim Sungeun S Nho Kwangsik K Risacher Shannon L SL Saykin Andrew J AJ Shen Li L Huang Heng H

Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) 20170412

With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer's Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations o ...[more]

PMID: 29696245

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Project description:AimTo investigate genotype-phenotype correlations in patients with perianal Crohn's disease (PCD) in order to determine which factors predispose to development of perianal disease in Crohn's patients.MethodsSeven-hundred and ninety-five Caucasian individuals (317 CD patients and 478 controls without inflammatory bowel disease, IBD) were prospectively enrolled into a clinical/genetic database. Demographic and clinical data, as well as peripheral blood leukocyte DNA were obtained from all patients. The following were evaluated: three NOD2/CARD15 polymorphisms: R702W, G908R, and 1007insC; five IL-23r risk alleles: rs1004819, rs10489629, rs2201841, rs11465804, and rs11209026; a well-characterized single-nucleotide polymorphism (SNP) on the IBD5 risk haplotype (OCTN1) and two peripheral tag SNPs (IGR2060 and IGR3096).ResultsPCD occurred in 147 (46%) of CD patients. There was no significant difference in the age at disease diagnosis between non-PCD and PCD patients (33 vs. 29 years, respectively). PCD patients were more likely to have disease located in the colon and ileocolic regions (79 PCD vs. 57% non-PCD; n = 116 vs. n = 96; p < 0.001), whereas patients with non-PCD were more likely to have Crohn's within the terminal ileum and upper gastrointestinal tract (43% non-PCD vs. 21% PCD; n = 73 vs. n = 31; p < 0.05). Thirty-four percent of patients with PCD required a permanent ileostomy (n = 50) compared to only 4% of non-PCD patients (n = 6; p < 0.05). Mutations in CARD15/NOD2 and IL-23r were risk factors for CD overall; however, in contrast to prior reports, in this patient population, OCTN1 and IGR variations within the IBD5 haplotype were not significant predictors of PCD.ConclusionColon/ileocolic CD location appears to be a significant predictor of perianal manifestations of CD. Patients with PCD are more likely to require permanent fecal diversion. We did not identify any genetic variations or combination of clinical findings and genetic variations within the CARD15/NOD2, IL-23r, and OCTN1 genes or IGR that were predictive of PCD.

Project description:With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms.

Dataset Information

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.

Publications

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.

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