Dataset Information

From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs.

ABSTRACT:

Motivation

Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic.

Results

Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel 'task-correlated longitudinal sparse regression' model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used ℓ(2,1)-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs.

Availability

Software is publicly available at: http://ranger.uta.edu/%7eheng/Longitudinal/

Contact

heng@uta.edu or shenli@inpui.edu.

SUBMITTER: Wang H

PROVIDER: S-EPMC3436838 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs.

Wang Hua H Nie Feiping F Huang Heng H Yan Jingwen J Kim Sungeun S Nho Kwangsik K Risacher Shannon L SL Saykin Andrew J AJ Shen Li L

Bioinformatics (Oxford, England) 20120901 18

<h4>Motivation</h4>Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, a ...[more]

PMID: 22962490

Similar Datasets

Project description:BACKGROUND:Psychosis is a common phenomenon in Alzheimer's disease (AD). The APOE ?4 allele is the strongest genetic risk factor for the development of AD, but its association with psychosis remains unclear. OBJECTIVE:We investigated the associations between psychosis, subdivided into delusions and hallucinations, as well as APOE ?4 allele on cognitive and functional outcomes. Secondarily, we investigated the associations between APOE ?4, Lewy bodies, and psychosis. METHODS:Data from the National Alzheimer's Coordinating Center (NACC) were used. Nine hundred patients with a confirmed diagnosis of AD based on the NIA-AA Reagan were included in the analysis. Global cognition was assessed using the Mini-Mental State Exam (MMSE) and functional status was assessed using the Functional Activities Questionnaire (FAQ). Psychosis status was determined using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Factorial design was used to assess the effects of psychosis and APOE ?4, as well as their interaction. RESULTS:Psychosis and the presence of APOE ?4 were both associated with lower MMSE scores, while only psychosis was associated with higher FAQ scores. Furthermore, patients with hallucinations had lower MMSE and higher FAQ scores than patients with only delusions. There was a significant interaction effect between psychosis and APOE ?4 on MMSE scores, with APOE ?4 negatively affecting patients with hallucinations-only psychosis. APOE ?4 was positively associated with the presence of Lewy body pathology, and both were found to be more prevalent in psychotic patients, with a stronger association with hallucinations. CONCLUSION:Psychosis in AD was associated with greater cognitive and functional impairments. Patients with hallucinations-with or without delusions-conferred even greater deficits compared to patients with only delusions. The APOE ?4 allele was associated with worse cognition, especially for patients with hallucination-only psychosis. APOE ?4 may mediate cognitive impairment in the hallucinations phenotype through the development of Lewy bodies. Our findings support that subtypes of psychosis should be evaluated separately.

Project description:Genome-wide association studies (GWAS) with binary or single phenotype data have successfully identified disease-associated genotypes and determinants of antimicrobial resistance. We describe a novel phenotype-to-genotype approach for a major bacterial pathogen that involves simultaneously testing for associations among multiple disease-related phenotypes and linkages between phenotypic variation and genetic determinants. High-throughput assays quantified variation among 163 Neisseria meningitidis serogroup W ST-11 clonal complex isolates for 11 phenotypic traits. A comparison of carriage and two disease subgroups detected significant differences between groups for eight phenotypic traits. Candidate genotypic testing indicated that indels in csw, a capsular biosynthesis gene, were associated with reduced survival in antibody-depleted heat-inactivated serum. GWAS testing detected 341 significant genetic variants (3 single-nucleotide polymorphisms and 338 unitigs) across all traits except serum bactericidal antibody-depleted assays. Growth traits were associated with variants of capsular biosynthesis genes, carbonic anhydrase, and an iron-uptake system while adhesion-linked variation was in pilC2, marR, and mutS. Multiple phase variation states or combinatorial phasotypes were associated with significant differences in multiple phenotypes. Controlling for group effects through regression and recursive random forest approaches detected group-independent effects for nalP with biofilm formation and fetA with a growth trait. Through random forest testing, nine phenotypes were weakly predictive of MenW:cc11 sub-lineage, original or 2013, for disease isolates while three characteristics separated carriage and disease isolates with >80% accuracy. This study demonstrates the power of combining high-throughput phenotypic testing of pathogenically relevant isolate collections with genomics for identifying genetic determinants of specific disease-relevant phenotypes and the pathobiology of microbial pathogens.IMPORTANCENext-generation sequencing technologies have led to the creation of extensive microbial genome sequence databases for several bacterial pathogens. Mining of these databases is now imperative for unlocking the maximum benefits of these resources. We describe a high-throughput methodology for detecting associations between phenotypic variation in multiple disease-relevant traits and a range of genetic determinants for Neisseria meningitidis, a major causative agent of meningitis and septicemia. Phenotypic variation in 11 disease-related traits was determined for 163 isolates of the hypervirulent ST-11 lineage and linked to specific single-nucleotide polymorphisms, short sequence variants, and phase variation states. Application of machine learning algorithms to our data outputs identified combinatorial phenotypic traits and genetic variants predictive of a disease association. This approach overcomes the limitations of generic meta-data, such as disease versus carriage, and provides an avenue to explore the multi-faceted nature of bacterial disease, carriage, and transmissibility traits.

Project description:GenADA is a multi-site collaborative study, involving GlaxoSmithKline Inc and nine medical centres in Canada, to develop a dataset containing 1000 Alzheimer's disease patients and 1000 ethnically-matched controls in order to associate DNA sequence (allelic) variations in candidate genes with Alzheimer's disease phenotypes. The study consists of both retrospective and prospective components, that is, patients with an existing diagnosis of Alzheimer's disease as well as newly diagnosed patients were enrolled in the study. Thus, clinical data was retrospectively or prospectively obtained on Day 1 of entry into GenADA. Where possible, biological relatives with Alzheimer's (up to third degree relationship such as cousins) and unaffected siblings of AD cases were also recruited. Note that recruitment numbers for biological relatives were lower than expected and genotypic data has not been submitted to dbGap for these subjects. The purpose of this study is: <ul> <li>To identify DNA sequence variations (genotype) in candidate genes that are associated with the clinical symptoms and behavioural features of Alzheimer's disease (phenotype), which differ between study participants with and without the disease.</li> <li>To identify other genotype-phenotype associations in cognitively impaired study participants such as age of onset, family history, rate of cognitive decline, patterns of behavioural/psychiatric non-cognitive symptoms factors, response to treatment co-morbid conditions, and risks/exposure.</li> </ul> The final subject recruitment for this study included 875 Alzheimer's disease patients, 850 ethnically-matched controls, and 37 family members. GenADA LONG is a longitudinal assessment to the original GenADA study. Eligible subjects were recruited from five of the nine memory clinics that participated in the GenADA study. Mild to moderate AD participants, a matched subset of controls, and biological related siblings (both affected and unaffected) or other blood relatives affected with AD, were initially examined a minimum of 12 months from recruitment into the original GenADA study, then at two further intervals of 12 and 18 months after time of entry into GenADA LONG. This enables an evaluation of the disease progression in AD patients and a determination of whether controls show evidence of cognitive decline. The overall goal of this extension study is to identify genetic differences and environmental influences that modulate the age of onset of the disease, the course of the disease, and/or biomarkers for neurodegenerative processes. Three of the five memory clinics that participated in the GenADA LONG study recruited eligible patients into GenADA Imaging. A concurrent neuroimaging sub-study was conducted at three of the five memory clinics participating in GenADA LONG. Eligible AD cases with mild to moderate AD, who were recruited into the original GenADA study and participated in the GenADA LONG extension study, were enrolled. Additionally, controls that showed signs of cognitive decline, as part of the assessment in GenADA LONG, were imaged at baseline, 12 and 18 month scanning intervals. The objective of this study is to find genes that: affect changes in AD brain volume measure by magnetic resonance in order to investigate how well change in brain volume predicts other key clinical measures in AD, such as neurodegenerative scales; that correlate changes in brain volume for other genotype-phenotype associations in cognitively impaired study participants; and that correlate with other clinically applicable magnetic resonance measures of pathology that can be conducted at the same time as structural volume measures, and are complementary to the volume measures. The ultimate aim of this research is to obtain a better understanding and definition of Alzheimer's Disease in order to develop new improved medicines.

Dataset Information

From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs.

Motivation

Results

Availability

Contact

Publications

From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets