Polygenic scores, diet quality, and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts.

BACKGROUND: Both genetic and lifestyle factors contribute to the risk of type 2 diabetes, but the extent to which there is a synergistic effect of the 2 factors is unclear. The aim of this study was to examine the joint associations of genetic risk and diet quality with incident type 2 diabetes. METHODS AND FINDINGS: We analyzed data from 35,759 men and women in the United States participating in the Nurses' Health Study (NHS) I (1986 to 2016) and II (1991 to 2017) and the Health Professionals Follow-up Study (HPFS; 1986 to 2016) with available genetic data and who did not have diabetes, cardiovascular disease, or cancer at baseline. Genetic risk was characterized using both a global polygenic score capturing overall genetic risk and pathway-specific polygenic scores denoting distinct pathophysiological mechanisms. Diet quality was assessed using the Alternate Healthy Eating Index (AHEI). Cox models were used to calculate hazard ratios (HRs) for type 2 diabetes after adjusting for potential confounders. With over 902,386 person-years of follow-up, 4,433 participants were diagnosed with type 2 diabetes. The relative risk of type 2 diabetes was 1.29 (95% confidence interval [CI] 1.25, 1.32; P < 0.001) per standard deviation (SD) increase in global polygenic score and 1.13 (1.09, 1.17; P < 0.001) per 10-unit decrease in AHEI. Irrespective of genetic risk, low diet quality, as compared to high diet quality, was associated with approximately 30% increased risk of type 2 diabetes (Pinteraction = 0.69). The joint association of low diet quality and increased genetic risk was similar to the sum of the risk associated with each factor alone (Pinteraction = 0.30). Limitations of this study include the self-report of diet information and possible bias resulting from inclusion of highly educated participants with available genetic data. CONCLUSIONS: These data provide evidence for the independent associations of genetic risk and diet quality with incident type 2 diabetes and suggest that a healthy diet is associated with lower diabetes risk across all levels of genetic risk.

First demonstration of robust tri-gateß-Ga2O3nano-membrane field-effect transistors.

Nano-membrane tri-gateß-gallium oxide (ß-Ga2O3) field-effect transistors (FETs) on SiO2/Si substrate fabricated via exfoliation have been demonstrated for the first time. By employing electron beam lithography, the minimum-sized features can be defined with the footprint channel width of 50 nm. For high-quality interface betweenß-Ga2O3and gate dielectric, atomic layer-deposited 15 nm thick aluminum oxide (Al2O3) was utilized with tri-methyl-aluminum (TMA) self-cleaning surface treatment. The fabricated devices demonstrate extremely low subthreshold slope (SS) of 61 mV dec-1, high drain current (IDS) ON/OFF ratio of 1.5 × 109, and negligible transfer characteristic hysteresis. We also experimentally demonstrated robustness of these devices with current-voltage (I-V) characteristics measured at temperatures up to 400 °C.

The Mediterranean diet, plasma metabolome, and cardiovascular disease risk.

AIMS: To investigate whether metabolic signature composed of multiple plasma metabolites can be used to characterize adherence and metabolic response to the Mediterranean diet and whether such a metabolic signature is associated with cardiovascular disease (CVD) risk. METHODS AND RESULTS: Our primary study cohort included 1859 participants from the Spanish PREDIMED trial, and validation cohorts included 6868 participants from the US Nurses' Health Studies I and II, and Health Professionals Follow-up Study (NHS/HPFS). Adherence to the Mediterranean diet was assessed using a validated Mediterranean Diet Adherence Screener (MEDAS), and plasma metabolome was profiled by liquid chromatography-tandem mass spectrometry. We observed substantial metabolomic variation with respect to Mediterranean diet adherence, with nearly one-third of the assayed metabolites significantly associated with MEDAS (false discovery rate < 0.05). Using elastic net regularized regressions, we identified a metabolic signature, comprised of 67 metabolites, robustly correlated with Mediterranean diet adherence in both PREDIMED and NHS/HPFS (r = 0.28-0.37 between the signature and MEDAS; P = 3 × 10-35 to 4 × 10-118). In multivariable Cox regressions, the metabolic signature showed a significant inverse association with CVD incidence after adjusting for known risk factors (PREDIMED: hazard ratio [HR] per standard deviation increment in the signature = 0.71, P < 0.001; NHS/HPFS: HR = 0.85, P = 0.001), and the association persisted after further adjustment for MEDAS scores (PREDIMED: HR = 0.73, P = 0.004; NHS/HPFS: HR = 0.85, P = 0.004). Further genome-wide association analysis revealed that the metabolic signature was significantly associated with genetic loci involved in fatty acids and amino acids metabolism. Mendelian randomization analyses showed that the genetically inferred metabolic signature was significantly associated with risk of coronary heart disease (CHD) and stroke (odds ratios per SD increment in the genetically inferred metabolic signature = 0.92 for CHD and 0.91 for stroke; P < 0.001). CONCLUSIONS: We identified a metabolic signature that robustly reflects adherence and metabolic response to a Mediterranean diet, and predicts future CVD risk independent of traditional risk factors, in Spanish and US cohorts.

Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank.

BACKGROUND: Clinical and epidemiologic studies have shown that obesity is associated with asthma and that these associations differ by asthma subtype. Little is known about the shared genetic components between obesity and asthma. OBJECTIVE: We sought to identify shared genetic associations between obesity-related traits and asthma subtypes in adults. METHODS: A cross-trait genome-wide association study (GWAS) was performed using 457,822 subjects of European ancestry from the UK Biobank. Experimental evidence to support the role of genes significantly associated with both obesity-related traits and asthma through a GWAS was sought by using results from obese versus lean mouse RNA sequencing and RT-PCR experiments. RESULTS: We found a substantial positive genetic correlation between body mass index and later-onset asthma defined by asthma age of onset at 16 years or greater (Rg = 0.25, P = 9.56 × 10-22). Mendelian randomization analysis provided strong evidence in support of body mass index causally increasing asthma risk. Cross-trait meta-analysis identified 34 shared loci among 3 obesity-related traits and 2 asthma subtypes. GWAS functional analyses identified potential causal relationships between the shared loci and Genotype-Tissue Expression (GTEx) quantitative trait loci and shared immune- and cell differentiation-related pathways between obesity and asthma. Finally, RNA sequencing data from lungs of obese versus control mice found that 2 genes (acyl-coenzyme A oxidase-like [ACOXL] and myosin light chain 6 [MYL6]) from the cross-trait meta-analysis were differentially expressed, and these findings were validated by using RT-PCR in an independent set of mice. CONCLUSIONS: Our work identified shared genetic components between obesity-related traits and specific asthma subtypes, reinforcing the hypothesis that obesity causally increases the risk of asthma and identifying molecular pathways that might underlie both obesity and asthma.

Steep-Slope WSe2 Negative Capacitance Field-Effect Transistor.

P-type two-dimensional steep-slope negative capacitance field-effect transistors are demonstrated for the first time with WSe2 as channel material and ferroelectric hafnium zirconium oxide in gate dielectric stack. F4-TCNQ is used as p-type dopant to suppress electron leakage current and to reduce Schottky barrier width for holes. WSe2 negative capacitance field-effect transistors with and without internal metal gate structures and the internal field-effect transistors are compared and studied. Significant SS reduction is observed in WSe2 negative capacitance field-effect transistors by inserting the ferroelectric hafnium zirconium oxide layer, suggesting the existence of internal amplification (∼10) due to the negative capacitance effect. Subthreshold slope less than 60 mV/dec (as low as 14.4 mV/dec) at room temperature is obtained for both forward and reverse gate voltage sweeps. Negative differential resistance is observed at room temperature on WSe2 negative capacitance field-effect-transistors as the result of negative capacitance induced negative drain-induced-barrier-lowering effect.

Fast eQTL Analysis for Twin Studies.

Twin data are commonly used for studying complex psychiatric disorders, and mixed effects models are one of the most popular tools for modeling dependence structures between twin pairs. However, for eQTL (expression quantitative trait loci) data where associations between thousands of transcripts and millions of single nucleotide polymorphisms need to be tested, mixed effects models are computationally inefficient and often impractical. In this paper, we propose a fast eQTL analysis approach for twin eQTL data where we randomly split twin pairs into two groups, so that within each group the samples are unrelated, and we then apply a multiple linear regression analysis separately to each group. A score statistic that automatically adjusts the (hidden) correlation between the two groups is constructed for combining the results from the two groups. The proposed method has well-controlled type I error. Compared to mixed effects models, the proposed method has similar power but drastically improved computational efficiency. We demonstrate the computational advantage of the proposed method via extensive simulations. The proposed method is also applied to a large twin eQTL data from the Netherlands Twin Register.

Amyloid-beta-activated human microglial cells through ER-resident proteins.

Microglial activation in the central nervous system is a key event in the neuroinflammation that accompanies neurodegenerative diseases such as Alzheimer's disease (AD). Among cytokines involved in microglial activation, amyloid ß (Aß) peptide is known to be a key molecule in the induction of diverse inflammatory products, which may lead to chronic inflammation in AD. However, proteomic studies of microglia in AD are limited due to lack of proper cell or animal model systems. In this study, we performed a proteomic analysis of Aß-stimulated human microglial cells using SILAC (stable isotope labeling with amino acids in cell culture) combined with LC-MS/MS. Results showed that 60 proteins increased or decreased their abundance by 1.5 fold or greater. Among these, ER-resident proteins such as SERPINH1, PDIA6, PDIA3, and PPIB were revealed to be key molecular biomarkers of human microglial activation by validation of the proteomic results by immunostaining, PCR, ELISA, and Western blot. Taken together, our data suggest that ER proteins play an essential role in human microglial activation by Aß and may be important molecular therapeutic targets for treatment of AD.

Type 2 diabetes and its genetic susceptibility are associated with increased severity and mortality of COVID-19 in UK Biobank.

Type 2 diabetes (T2D) is known as one of the important risk factors for the severity and mortality of COVID-19. Here, we evaluate the impact of T2D and its genetic susceptibility on the severity and mortality of COVID-19, using 459,119 individuals in UK Biobank. Utilizing the polygenic risk scores (PRS) for T2D, we identified a significant association between T2D or T2D PRS, and COVID-19 severity. We further discovered the efficacy of vaccination and the pivotal role of T2D-related genetics in the pathogenesis of severe COVID-19. Moreover, we found that individuals with T2D or those in the high T2D PRS group had a significantly increased mortality rate. We also observed that the mortality rate for SARS-CoV-2-infected patients was approximately 2 to 7 times higher than for those not infected, depending on the time of infection. These findings emphasize the potential of T2D PRS in estimating the severity and mortality of COVID-19.

Gene-diet interaction analysis using novel weighted food scores discovers the adipocytokine signaling pathway associated with the development of type 2 diabetes.

Introduction: The influence of dietary patterns measured using Recommended Food Score (RFS) with foods with high amounts of antioxidant nutrients for Type 2 diabetes (T2D) was analyzed. Our analysis aims to find associations between dietary patterns and T2D and conduct a gene-diet interaction analysis related to T2D. Methods: Data analyzed in the current study were obtained from the Korean Genome and Epidemiology Study Cohort. The dietary patterns of 46 food items were assessed using a validated food frequency questionnaire. To maximize the predictive power of the RFS, we propose two weighted food scores, namely HisCoM-RFS calculated using the novel Hierarchical Structural Component model (HisCoM) and PLSDA-RFS calculated using Partial Least Squares-Discriminant Analysis (PLS-DA) method. Results: Both RFS (OR: 1.11; 95% CI: 1.03- 1.20; P = 0.009) and PLSDA-RFS (OR: 1.10; 95% CI: 1.02-1.19, P = 0.011) were positively associated with T2D. Mapping of SNPs (P < 0.05) from the interaction analysis between SNPs and the food scores to genes and pathways yielded some 12 genes (CACNA2D3, RELN, DOCK2, SLIT3, CTNNA2, etc.) and pathways associated with T2D. The strongest association was observed with the adipocytokine signalling pathway, highlighting 32 genes (STAT3, MAPK10, MAPK8, IRS1, AKT1-3, ADIPOR2, etc.) most likely associated with T2D. Finally, the group of the subjects in low, intermediate and high using both the food scores and a polygenic risk score found an association between diet quality groups with issues at high genetic risk of T2D. Conclusion: A dietary pattern of poor amounts of antioxidant nutrients is associated with the risk of T2D, and diet affects pathway mechanisms involved in developing T2D.

Genome-Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases.

Background Patients with rheumatoid arthritis (RA) have a 2- to 10-fold increased risk of cardiovascular disease (CVD), but the biological mechanisms and existence of causality underlying such associations remain to be investigated. We aimed to investigate the genetic associations and underlying mechanisms between RA and CVD by leveraging large-scale genomic data and genetic cross-trait analytic approaches. Methods and Results Within UK Biobank data, we examined the genetic correlation, shared genetics, and potential causality between RA (Ncases=6754, Ncontrols=452 384) and cardiovascular diseases (CVD, Ncases=44 238, Ncontrols=414 900) using linkage disequilibrium score regression, cross-trait meta-analysis, and Mendelian randomization. We observed significant genetic correlations of RA with myocardial infarction (rg:0.40 [95% CI, 0.24-0.56), angina (rg:0.42 [95% CI, 0.28-0.56]), coronary heart diseases (rg:0.41 [95% CI, 0.27-0.55]), and CVD (rg:0.43 [95% CI, 0.29-0.57]) after correcting for multiple testing (P<0.05/5). When stratified by frequent use of analgesics, we found increased genetic correlation between RA and CVD among participants without aspirin usage (rg:0.54 [95% CI, 0.30-0.78] for angina; Pvalue=6.69×10-6) and among participants with paracetamol usage (rg:0.75 [95% CI, 0.20-1.29] for myocardial infarction; Pvalue=8.90×10-3), whereas others remained similar. Cross-trait meta-analysis identified 9 independent shared loci between RA and CVD, including PTPN22 at chr1p13.2, BCL2L11 at chr2q13, and CCR3 at chr3p21.31 (Psingle trait<1×10-3 and Pmeta<5×10-8), highlighting potential shared pathogenesis including accelerating atherosclerosis, upregulating oxidative stress, and vascular permeability. Finally, Mendelian randomization estimates showed limited evidence of causality between RA and CVD. Conclusions Our results supported shared genetic pathogenesis rather than causality in explaining the observed association between RA and CVD. The identified shared genetic factors provided insights into potential novel therapeutic target for RA-CVD comorbidities.

Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies.

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort.

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

TwinEQTL: ultrafast and powerful association analysis for eQTL and GWAS in twin studies.

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model for twin genome-wide association study data. Instead of analyzing all twin samples together with linear mixed-effects model, TwinEQTL first splits twin samples into 2 independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the 2 nonindependent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between 2 dependent test statistics at each single-nucleotide polymorphism is independent of its minor allele frequency. Thus, the correlation is constant across all single-nucleotide polymorphisms. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared with the gold-standard linear mixed-effects models. To accommodate expression quantitative loci analysis with twin subjects, we further implement TwinEQTL into an R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for genome-wide association study and expression quantitative loci analysis with twin samples.

Statistical models and computational tools for predicting complex traits and diseases.

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

A transdisciplinary approach to understand the epigenetic basis of race/ethnicity health disparities.

Health disparities correspond to differences in disease burden and mortality among socially defined population groups. Such disparities may emerge according to race/ethnicity, socioeconomic status and a variety of other social contexts, and are documented for a wide range of diseases. Here, we provide a transdisciplinary perspective on the contribution of epigenetics to the understanding of health disparities, with a special emphasis on disparities across socially defined racial/ethnic groups. Scientists in the fields of biological anthropology, bioinformatics and molecular epidemiology provide a summary of theoretical, statistical and practical considerations for conducting epigenetic health disparities research, and provide examples of successful applications from cancer research using this approach.

Genomic-Metabolomic Associations Support the Role of LIPC and Glycerophospholipids in Age-Related Macular Degeneration.

PURPOSE: Large-scale genome-wide association studies (GWAS) have reported important single nucleotide polymorphisms (SNPs) with significant associations with age-related macular degeneration (AMD). However, their role in disease development remains elusive. This study aimed to assess SNP-metabolite associations (i.e., metabolite quantitative trait loci [met-QTL]) and to provide insights into the biological mechanisms of AMD risk SNPs. DESIGN: Cross-sectional multicenter study (Boston, Massachusetts, and Coimbra, Portugal). PARTICIPANTS: Patients with AMD (n = 388) and control participants (n = 98) without any vitreoretinal disease (> 50 years). METHODS: Age-related macular degeneration grading was performed using color fundus photographs according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and evaluated with mass spectrometry for metabolomic profiling and Illumina OmniExpress for SNPs profiling. Analyses of met-QTL of endogenous metabolites were conducted using linear regression models adjusted for age, gender, smoking, 10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by generating genetic risk scores and assessing their associations with metabolites using linear regression models, accounting for the same covariates. Modeling was performed first for each cohort, and then combined by meta-analysis. Multiple comparisons were accounted for using the false discovery rate (FDR). MAIN OUTCOME MEASURES: Plasma metabolite levels associated with AMD risk SNPs. RESULTS: After quality control, data for 544 plasma metabolites were included. Meta-analysis of data from all individuals (AMD patients and control participants) identified 28 significant met-QTL (ß = 0.016-0.083; FDR q-value < 1.14 × 10-2), which corresponded to 5 metabolites and 2 genes: ASPM and LIPC. Polymorphisms in the LIPC gene were associated with phosphatidylethanolamine metabolites, which are glycerophospholipids, and polymorphisms in the ASPM gene with branched-chain amino acids. Similar results were observed when considering only patients with AMD. Genetic risk score-metabolite associations further supported a global impact of AMD risk SNPs on the plasma metabolome. CONCLUSIONS: This study demonstrated that genomic-metabolomic associations can provide insights into the biological relevance of AMD risk SNPs. In particular, our results support that the LIPC gene and the glycerophospholipid metabolic pathway may play an important role in AMD, thus offering new potential therapeutic targets for this disease.

Arabidopsis ING and Alfin1-like protein families localize to the nucleus and bind to H3K4me3/2 via plant homeodomain fingers.

In yeast and animals, tri- and dimethylation of histone H3 at lysine 4 (H3K4me3/2) are markers of transcriptionally active genes that have recently been shown to be primary ligands for the plant homeodomain (PHD) finger. However, PHD fingers able to bind to H3K4me3/2 have not been identified in plants. Here, we identify 83 canonical PHD fingers in the Arabidopsis proteome database that are supported by both SMART and Pfam prediction. Among these, we focus on PHD fingers in ING (inhibitor of growth) homologues (AtING) and Alfin1-like (AL) proteins, which are highly similar to those in human ING2 and bromodomain PHD finger transcription factor (BPTF), based on predicted tertiary structures. ING proteins are found in yeast, animals and plants, whereas AL proteins exist only in plants. In vitro binding experiments indicated that PHD fingers in AtING and AL proteins in Arabidopsis can bind to H3K4me3, and, to a lesser extent, to H3K4me2. In addition, mutational analysis confirmed that a predicted aromatic cage and a specific conserved acidic residue are both crucial for binding to H3K4me3/2. Finally, we demonstrate that AtING and AL proteins are nuclear proteins that are expressed in various tissues of the Arabidopsis plant. Thus, we propose that ING and AL proteins are nuclear proteins that are involved in chromatin regulation by binding to H3K4me3/2, the active histone markers, in plants.

Histone occupancy-dependent and -independent removal of H3K27 trimethylation at cold-responsive genes in Arabidopsis.

Trimethylation of histone H3 at lysine 27 (H3K27me3) is a histone marker that is present in inactive gene loci in both plants and animals. Transcription of some of the genes with H3K27me3 should be induced by internal or external cues, yet the dynamic fate of H3K27me3 in these genes during transcriptional regulation is poorly understood in plants. Here we show that H3K27me3 in two cold-responsive genes, COR15A and ATGOLS3, decreases gradually in Arabidopsis during exposure to cold temperatures. We found that removal of H3K27me3 can occur by both histone occupancy-dependent and -independent mechanisms. Upon cold exposure, histone H3 levels decreased in the promoter regions of COR15A and ATGOLS3 but not in their transcribed regions. When we returned cold-exposed plants to normal growth conditions, transcription of COR15A and ATGOLS3 was completely repressed to the initial level before cold exposure in 1 day. In contrast, plants still maintained the cold-triggered decrease in H3K27me3 at COR15A and ATGOLS3, but this decrease did not enhance transcriptional induction of the two genes upon re-exposure to cold. Taken together, these results indicate that gene activation is not inhibited by H3K27me3 itself but rather leads to removal of H3K27me3, and that H3K27me3 can be inherited at a quantitative level, thereby serving as a memory marker for recent transcriptional activity in Arabidopsis.

The role of cyanopterin in UV/blue light signal transduction of cyanobacterium Synechocystis sp. PCC 6803 phototaxis.

We analyzed the effects of inactivating the pteridine glycosyltransferase gene (pgtA) on the photomovement of the cyanobacterium Synechocystis sp. PCC 6803 under different light conditions. The pgtA mutant displayed abnormal photomovement under UV-A/blue light. In particular, the pgtA mutant showed a negative phototactic response under UV-A (315-400 nm), whereas the wild-type did not show any photomovement. Inhibition of pterin biosynthesis by N-acetylserotonin (NAS), an inhibitor of sepiapterin reductase, also inhibited a positive phototactic response of the wild-type under white and blue light. In addition, negative phototaxis of the pgtA mutant was observed under UV-A/blue light in the presence of NAS. These results indicated that the product of the PgtA enzyme, cyanopterin, is involved in the inhibition of the negative phototaxis of the wild-type by sensing the UV-A. However, 2,4-diamino-6-hydroxypyrimidine-mediated inhibition of GTP cyclohydrolase I, the rate-limiting enzyme for pterin biosynthesis, significantly increased the positive phototaxis toward UV-A in the wild-type and the pgtA mutant. Furthermore, we measured the action spectrum of phototaxis in vivo for the wild-type and pgtA mutant. Maximal activity of the wild-type was at 300, 380 and 440 nm, indicating absorption by pterins and flavin. In particular, the UV-A/ blue peak at 380 and 440 nm obtained from the action spectrum of phototaxis was found to be closely correlated with the in vitro absorption spectrum previously reported for the cyanobacterial cryptochrome DASH. By investigating the photomovement of the wild-type and pgtA mutant to UV and blue light, we suggest that pterin can function as the chromophore of putative UV/blue photoreceptor(s) in cyanobacterial phototaxis.

New evaluation measures for multifactor dimensionality reduction classifiers in gene-gene interaction analysis.

MOTIVATION: Gene-gene interactions are important contributors to complex biological traits. Multifactor dimensionality reduction (MDR) is a method to analyze gene-gene interactions and has been applied to many genetics studies of complex diseases. In order to identify the best interaction model associated with disease susceptibility, MDR classifiers corresponding to interaction models has been constructed and evaluated as a predictor of disease status via a certain measure such as balanced accuracy (BA). It has been shown that the performance of MDR tends to depend on the choice of the evaluation measures. RESULTS: In this article, we introduce two types of new evaluation measures. First, we develop weighted BA (wBA) that utilizes the quantitative information on the effect size of each multi-locus genotype on a trait. Second, we employ ordinal association measures to assess the performance of MDR classifiers. Simulation studies were conducted to compare the proposed measures with BA, a current measure. Our results showed that the wBA and tau(b) improved the power of MDR in detecting gene-gene interactions. Noticeably, the power increment was higher when data contains the greater number of genetic markers. Finally, we applied the proposed evaluation measures to real data.