Ai-Ru Hsieh

Serum adipocyte fatty acid-binding protein levels in patients with critical illness are associated with insulin resistance and predict mortality

IntroductionHyperglycemia and insulin resistance are commonplace in critical illness, especially in patients with sepsis. Recently, several hormones secreted by adipose tissue have been determined to be involved in overall insulin sensitivity in metabolic syndrome-related conditions, including adipocyte fatty-acid binding protein (A-FABP). However, little is known about their roles in critical illness. On the other hand, there is evidence that several adipose tissue gene expressions change in critically ill patients.MethodsA total of 120 patients (72 with sepsis, 48 without sepsis) were studied prospectively on admission to a medical ICU and compared with 45 healthy volunteers as controls. Various laboratory parameters and metabolic and inflammatory profiles were assessed within 48 hours after admission. Clinical data were collected from medical records.ResultsCompared with healthy controls, serum A-FABP concentrations were higher in all critically ill patients, and there was a trend of higher A-FABP in patients with sepsis. In multivariate correlation analysis in all critically ill patients, the serum A-FABP concentrations were independently related to serum creatinine, fasting plasma glucose, total cholesterol, TNF-alpha, albumin, and the Acute Physiology and Chronic Health Evaluation II scores. In survival analysis, higher A-FABP levels (> 40 ng/ml) were associated with an unfavorable overall survival outcome, especially in sepsis patients.ConclusionsCritically ill patients have higher serum A-FABP concentrations. Moreover, A-FABP may potentially serve as a prognostic biomarker in critically ill patients with sepsis.

On the use of multifactor dimensionality reduction (MDR) and classification and regression tree (CART) to identify haplotype-haplotype interactions in genetic studies

Haplotype-based approaches may have greater power than single-locus analyses when the SNPs are in strong linkage disequilibrium with the risk locus. To overcome potential complexities owing to large numbers of haplotypes in genetic studies, we evaluated two data mining approaches, multifactor dimensionality reduction (MDR) and classification and regression tree (CART), with the concept of haplotypes considering their haplotype uncertainty to detect haplotype-haplotype (HH) interactions. In evaluation of performance for detecting HH interactions, MDR had higher power than CART, but MDR gave a slightly higher type I error. Additionally, we performed an HH interaction analysis with a publicly available dataset of Parkinsons disease and confirmed previous findings that the RET proto-oncogene is associated with the disease. In this study, we showed that using HH interaction analysis is possible to assist researchers in gaining more insight into identifying genetic risk factors for complex diseases.

Constructing endophenotypes of complex diseases using non-negative matrix factorization and adjusted rand index.

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 (), MS2 (), and MS3 (). 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.

Modeling expression quantitative trait loci in data combining ethnic populations

BackgroundCombining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive.ResultsIn this report, we explored the applicability of a constrained two-way model to identify eQTL for combined ethnic data that might contain genetic diversity among ethnic populations. In addition, gene expression differences resulted from ethnic allele frequency diversity between populations were directly estimated and analyzed by the constrained two-way model. Through simulation, we investigated effects of genetic diversity on eQTL identification by examining gene expression data pooled from normal quantile transformation of each population. Using the constrained two-way model to reanalyze data from Caucasians and Asian individuals available from HapMap, a large number of eQTL were identified with similar genetic effects on the gene expression levels in these two populations. Furthermore, 19 single nucleotide polymorphisms with inter-population differences with respect to both genotype frequency and gene expression levels directed by genotypes were identified and reflected a clear distinction between Caucasians and Asian individuals.ConclusionsThis study illustrates the influence of minor allele frequencies on common eQTL identification using either separate or combined population data. Our findings are important for future eQTL studies in which different datasets are combined to increase the power of eQTL identification.

Association of Plasma Thrombospondin-1 Level with Cardiovascular Disease and Mortality in Hemodialysis Patients

BACKGROUND Thrombospondin-1 (TSP-1) is known to be involved in the regulation of angiogenesis, inflammation, and vascular function. Clinical studies have demonstrated its correlation with peripheral artery disease, coronary artery disease, and pulmonary hypertension. In this study, we explored its potential roles in the background of end-stage renal disease (ESRD). METHODS A total of 140 ESRD outpatients (ages 61.0 ± 12.4 years) were prospectively followed for 34 ± 7 months. Their TSP-1 levels were analyzed from pre-hemodialysis blood sample. Cardiovascular survey included ankle- brachial index (ABI), echocardiography and Tl-201 dipyridamole single-photon emission computed tomography (SPECT). RESULTS Plasma TSP-1 levels were higher in those patients with preexisting clinical evidence of cardiovascular disease (CVD) than those without (p = 0.002). TSP-1 concentrations were also correlated with ABI, left ventricular ejection fraction, and scar burden in SPECT. Stepwise logistic regression analysis revealed that TSP-1 level was independently associated with the presence of CVD, with an odds ratio of 1.38 [95% confidence interval (CI), 1.09-1.75, p = 0.008]. In survival analyses, 31 patients (22%) died during the follow-up, 16 (52%) arising from cardiovascular causes. Cox hazards analysis revealed that the patients with TSP-1 levels in the highest tertile had a 5.32- and 6.75-fold higher risk for all-cause and cardiovascular mortality than those in the lowest tertile. This predictive value for all-cause mortality still persisted after multivariate adjustment (hazard ratio, 8.71; 95% CI, 1.36-55.68; p = 0.02). CONCLUSIONS This study hallmarks the association of elevated TSP-1 level with CVD and adverse outcome among hemodialysis patients. KEY WORDS Thrombospondin-1; End-stage renal disease; Cardiovascular disease; Mortality.

Identifying rare and common disease associated variants in genomic data using Parkinson's disease as a model.

BackgroundGenome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinsons disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects.ResultsWe propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinsons disease case–control dataset as a model to demonstrate the application of our method.Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinsons disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinsons disease.ConclusionsOur method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinsons disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.

Effects of sex and generation on hepatitis B viral load in families with hepatocellular carcinoma

AIM To explore factors associated with persistent hepatitis B virus (HBV) infection in a cohort of hepatocellular carcinoma (HCC)-affected families and then investigate factors that correlate with individual viral load among hepatitis B surface antigen (HBsAg)-positive relatives. METHODS We evaluated non-genetic factors associated with HBV replication in relatives of patients with HCC. Relatives of 355 HCC cases were interviewed using a structured questionnaire. Demographics, relationship to index case, HBsAg status of mothers and index cases were evaluated for association with the HBV persistent infection or viral load by generalized estimating equation analysis. RESULTS Among 729 relatives enrolled, parent generation (P = 0.0076), index generation (P = 0.0044), mothers positive for HBsAg (P = 0.0007), and HBsAg-positive index cases (P = 5.98 × 10-8) were associated with persistent HBV infection. Factors associated with HBV viral load were evaluated among 303 HBsAg-positive relatives. Parent generation (P = 0.0359) and sex (P = 0.0007) were independent factors associated with HBV viral load. The intra-family HBV viral load was evaluated in families clustered with HBsAg-positive siblings. An intra-family trend of similar HBV viral load was found for 27 of 46 (58.7%) families. Male offspring of HBsAg-positive mothers (P = 0.024) and older siblings were associated with high viral load. CONCLUSION Sex and generation play important roles on HBV viral load. Maternal birth age and nutritional changes could be the reasons of viral load difference between generations.

A Novel Method for Identification and Quantification of Consistently Differentially Methylated Regions

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

Using Maximal Segmental Score in Genome-Wide Association Studies

Genome‐wide association studies (GWAS) have become the method of choice for identifying disease susceptibility genes in common disease genetics research. Despite successes in these studies, much of the heritability remains unexplained due to lack of power and low resolution. High‐density genotyping arrays can now screen more than 5 million genetic markers. As a result, multiple comparison has become an important issue especially in the era of next‐generation sequencing. We propose to use a two‐stage maximal segmental score procedure (MSS) which uses region‐specific empirical P‐values to identify genomic segments most likely harboring the disease gene. We develop scoring systems based on Fishers P‐value combining method to convert locus‐specific significance levels into region‐specific scores. Through simulations, our result indicated that MSS increased the power to detect genetic association as compared with conventional methods provided type I error was at 5%. We demonstrated the application of MSS on a publicly available case‐control dataset of Parkinsons disease and replicated the findings in the literature. MSS provides an efficient exploratory tool for high‐density association data in the current era of next‐generation sequencing. R source codes to implement the MSS procedure are freely available at http://www.csjfann.ibms.sinica.edu.tw/EAG/program/programlist.htm.

A non-threshold region-specific method for detecting rare variants in complex diseases

A region-specific method, NTR (non-threshold rare) variant detection method, was developed—it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.