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A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data.

ABSTRACT: MOTIVATION: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene-endophenotype-disease relationship in which endophenotypes were represented by hidden nodes. RESULTS: We successfully constructed a three-endophenotype model for Taiwanese hypertensive males with high identification accuracy. Of the three endophenotypes, one is strongly protective, another is weakly protective and the third is highly correlated with developing young-onset male hypertension. Sixteen of the involved 101 genes were highly and consistently influential to the endophenotypes. Identification of SLC4A5, SLC5A10 and LDOC1 indicated that sodium/bicarbonate transport, sodium/glucose transport and cell-proliferation regulation may play important upstream roles and identification of BNIP1, APOBEC3F and LDOC1 suggested that apoptosis, innate immune response and cell-proliferation regulation may play important downstream roles in hypertension. The involved genes not only provide insights into the mechanism of hypertension but should also be considered in future gene mapping endeavors.

SUBMITTER: Lynn KS

PROVIDER: S-EPMC2666815 | biostudies-other | 2009 Apr

REPOSITORIES: biostudies-other

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A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data.

Lynn Ke-Shiuan KS Li Li-Lan LL Lin Yen-Ju YJ Wang Chiuen-Huei CH Sheng Shu-Hui SH Lin Ju-Hwa JH Liao Wayne W Hsu Wen-Lian WL Pan Wen-Harn WH

Bioinformatics (Oxford, England) 20090223 8

<h4>Motivation</h4>Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted ...[more]

PMID: 19237446

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Project description:Complex diseases are typically caused by combinations of molecular disturbances that vary widely among different patients. Endophenotypes, a combination of genetic factors associated with a disease, offer a simplified approach to dissect complex trait by reducing genetic heterogeneity. Because molecular dissimilarities often exist between patients with indistinguishable disease symptoms, these unique molecular features may reflect pathogenic heterogeneity. To detect molecular dissimilarities among patients and reduce the complexity of high-dimension data, we have explored an endophenotype-identification analytical procedure that combines non-negative matrix factorization (NMF) and adjusted rand index (ARI), a measure of the similarity of two clusterings of a data set. To evaluate this procedure, we compared it with a commonly used method, principal component analysis with k-means clustering (PCA-K). A simulation study with gene expression dataset and genotype information was conducted to examine the performance of our procedure and PCA-K. The results showed that NMF mostly outperformed PCA-K. Additionally, we applied our endophenotype-identification analytical procedure to a publicly available dataset containing data derived from patients with late-onset Alzheimer's disease (LOAD). NMF distilled information associated with 1,116 transcripts into three metagenes and three molecular subtypes (MS) for patients in the LOAD dataset: MS1 (n1=80), MS2 (n2=73), and MS3 (n3=23). ARI was then used to determine the most representative transcripts for each metagene; 123, 89, and 71 metagene-specific transcripts were identified for MS1, MS2, and MS3, respectively. These metagene-specific transcripts were identified as the endophenotypes. Our results showed that 14, 38, 0, and 28 candidate susceptibility genes listed in AlzGene database were found by all patients, MS1, MS2, and MS3, respectively. Moreover, we found that MS2 might be a normal-like subtype. Our proposed procedure provides an alternative approach to investigate the pathogenic mechanism of disease and better understand the relationship between phenotype and genotype.

Project description:Lack of power and reproducibility are caveats of genetic association studies of common complex diseases. Indeed, the heterogeneity of disease etiology demands that causal models consider the simultaneous involvement of multiple genes. Rothman's sufficient-cause model, which is well known in epidemiology, provides a framework for such a concept. In the present work, we developed a three-stage algorithm to construct gene clusters resembling Rothman's causal model for a complex disease, starting from finding influential gene pairs followed by grouping homogeneous pairs.The algorithm was trained and tested on 2,772 hypertensives and 6,515 normotensives extracted from four large Caucasian and Taiwanese databases. The constructed clusters, each featured by a major gene interacting with many other genes and identified a distinct group of patients, reproduced in both ethnic populations and across three genotyping platforms. We present the 14 largest gene clusters which were capable of identifying 19.3% of hypertensives in all the datasets and 41.8% if one dataset was excluded for lack of phenotype information. Although a few normotensives were also identified by the gene clusters, they usually carried less risky combinatory genotypes (insufficient causes) than the hypertensive counterparts. After establishing a cut-off percentage for risky combinatory genotypes in each gene cluster, the 14 gene clusters achieved a classification accuracy of 82.8% for all datasets and 98.9% if the information-short dataset was excluded. Furthermore, not only 10 of the 14 major genes but also many other contributing genes in the clusters are associated with either hypertension or hypertension-related diseases or functions.We have shown with the constructed gene clusters that a multi-causal pie-multi-component approach can indeed improve the reproducibility of genetic markers for complex disease. In addition, our novel findings including a major gene in each cluster and sufficient risky genotypes in a cluster for disease onset (which coincides with Rothman's sufficient cause theory) may not only provide a new research direction for complex diseases but also help to reveal the disease etiology.

Project description:A universally acknowledged, core property of language is its complexity, at each level of structure - sounds, words, phrases, clauses, utterances, and higher levels of discourse. How does this complexity originate and develop in a language? We cannot fully answer this question from spoken languages, since they are all thousands of years old or descended from old languages. However, sign languages of deaf communities can arise at any time and provide empirical data for testing hypotheses related to the emergence of language complexity. An added advantage of the signed modality is a correspondence between visible physical articulations and linguistic structures, providing a more transparent view of linguistic complexity and its emergence (Sandler, 2012). These essential characteristics of sign languages allow us to address the issue of emerging complexity by documenting the use of the body for linguistic purposes. We look at three types of discourse relations of increasing complexity motivated by research on spoken languages - additive, symmetric, and asymmetric (Mann and Thompson, 1988; Sanders et al., 1992). Each relation type can connect units at two different levels: within propositions (simpler) and across propositions (more complex). We hypothesized that these relations provide a measure for charting the time course of emergence of complexity, from simplest to most complex, in a new sign language. We test this hypothesis on Israeli Sign Language (ISL), a young language, some of whose earliest users are still available for recording. Taking advantage of the unique relation in sign languages between bodily articulations and linguistic form, we study fifteen ISL signers from three generations, and demonstrate that the predictions indeed hold. We also find that younger signers tend to converge on more systematic marking of relations, that they use fewer articulators for a given linguistic function than older signers, and that the form of articulations becomes reduced, as the language matures. Mapping discourse relations to the bodily expression of linguistic components across age groups reveals how simpler, less constrained, and more gesture-like expressions, become language.

Project description:OBJECTIVE:Inactivating heterozygous mutations in the GCK gene are a common cause of MODY and result in mild fasting hyperglycaemia, which does not require treatment. We aimed to identify the frequency, clinical and molecular features of GCK mutations in a Turkish paediatric cohort. DESIGN AND PATIENTS:Fifty-four unrelated probands were selected based on the following criteria: age of diagnosis ?17 years, family history of diabetes in at least two generations, anti-GAD/ICA negative, BMI<95.p and follow-up with diet, oral antidiabetic drug or low-dose insulin treatment (?0·5U/kg/d). A MODY probability score (www.diabetesgenes.org) was calculated and 21 patients with a score ?75%, HbA1c levels ?7·5% (58·5 mmol/mol) and fasting blood glucose (FBG) levels 99-145 mg/dl (5·5-8·0 mmol/l) were selected for Sanger sequencing of the GCK gene. Targeted next-generation sequencing for all known monogenic diabetes genes was undertaken for any patient without a GCK gene mutation. RESULTS:GCK gene mutations (pathogenic or likely pathogenic variants) and a novel intronic variant of uncertain significance (c.208 + 3A>T) were identified in 13/54 probands (24%). Twelve of these patients had a MODY probability score ?75%. FBG level and 2-h glucose level in OGTT were 123 ± 14 mg/dl (6·8 ± 0·7 mmol/l) (107-157 mg/dl) and 181 ± 30 mg/dl (10·1 ± 1·6 mmol/l) (136-247 mg/dl), respectively. Average of glucose increment in OGTT was 58 ± 27 mg/dl (3·2 ± 1·5 mmol/l) (19-120 mg/dl), and mean HbA1c level was 6·5 ± 0·5% (47·5 ± 5·5 mmol/mol) (5·9-7·6%). Five novel missense mutations were identified (p.F123S, p.L58P, p.G246A, p.F419C, and p.S151C). Two patients treated with low-dose insulin before the molecular analysis were able to stop treatment. CONCLUSIONS:Approximately 1 in 4 MODY cases in this Turkish paediatric cohort have a GCK mutation. Selection of patients for GCK gene analysis using the MODY probability score was an effective way of identifying most (11/12) patients with a GCK mutation.

Dataset Information

A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data.

Publications

A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data.

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