Restricted Maximum Likelihood Approach Research Articles

Escalation of Human Resource Quality in National Health Resilience Through Estimating Multilevel Model Parameters with the Restricted Maximum Likelihood Approach to The 2019 UTBK Average

Multilevel modeling or Hierarchical Linear Modeling (HLM) is a statistical approach specifically used to analyze data with a two-level structure. This approach allows an understanding of the contribution of factors at both the individual and group levels to the response variable. One method commonly used in HLM is Restricted Maximum Likelihood (REML). REML is a parameter estimation method that is often applied in statistical models, especially linear models that incorporate random components. This allows more efficient parameter assessment compared to conventional estimation methods. In this research, multilevel model parameter estimation analysis was carried out using the limited maximum likelihood approach. The aim is to determine the multilevel linear regression model on the average UTBK score for health cluster study programs in 2019. This involves selecting the optimal node point based on the minimum Generalized Cross Validation (GCV) and identifying the factors that influence it. Predictor variables considered include interest and capacity of study programs at university level (Level-1), as well as the average UNBK and HDI scores at provincial level (Level-2). The findings of this research indicate that the most appropriate multilevel regression model is formed with three nodes with a minimum GCV value at Level-1 of 864.6593 and at Level-2 of 3.1816. At Level-1, the influencing factor is the interest variable and at Level-2 is the average provincial UNBK score in 2019 and the Human Development Index (HDI).

Likelihood function derivatives for a linear mixed model with compound symmetry assumption

The paper explores the properties of linear mixed models with simple random effects of the form: yi = Xiβ + ZiYi + εi, i = 1, . . . ,M, Yi ∼ N(0, Ψ), εi ∼ Т(0, σ2I), where M is the number of distinct groups, each consisting of ni observations. Random effects Yi and within-group errors εi are independent across different groups and within the same group. β is a p-dimensional vector of fixed effects, Yi is a q-dimensional vector of random effects, and Xi and Zi are known design matrices of dimensions nixp and nixq, of fixed and random effects respectively. Vectors εi represent within-group errors with a spherically Gaussian distribution.Assuming a compound symmetry in the correlation structure of the matrix Ψ governing the dependence among within-group errors, analytical formulas for the first two partial derivatives of the profile restricted maximum likelihood function with respect to the correlation parameters of the model are derived. The analytical representation of derivatives facilitates the effective utilization of numerical algorithms like Newton-Raphson or Levenberg-Marquardt.The restricted maximum likelihood (REML) estimation is a statistical technique employed to estimate the parameters within a mixed-effects model, particularly in the realm of linear mixed models. It serves as an extension of the maximum likelihood estimation method, aiming to furnish unbiased and efficient parameter estimates, especially in scenarios involving correlated data. Within the framework of the REML approach, the likelihood function undergoes adjustments to remove the nuisance parameters linked to fixed effects. This modification contributes to enhancing the efficiency of parameter estimation, particularly in situations where the primary focus is on estimating variance components or when the model encompasses both fixed and random effects.

Restricted estimators of repeated measurements model for unbalanced data

The model of one-way repeated measurements for imbalanced data, as the number of explanations are not the same at some levels, is the subject of this research. We used two methods to estimate the model parameters: the restricted maximum likelihood method and maximum likelihood approach, and we considered some of their characteristics. The Experiment’s practical research goal is to determine how the experimental elements and their interactions affect date palm productivity. The Experiment included the factor of irrigation method with three treatments: drip irrigation, basin irrigation, and the regular tidal method, while the irrigation interval factor included three treatments: 3, 6 and 9 days.

Prenatal exposure to environmental heavy metals and newborn telomere length: A systematic review and meta-analysis

Exposure to environmental heavy metals is associated with telomere length (TL) alteration. Available information regarding the effect of prenatal exposure to environmental pollutants on newborn TL is controversial. The aim of this study is to systematically review and conduct a meta-analysis of the existing epidemiological studies on the associations between prenatal metal exposure and newborn TL. A comprehensive literature search was performed using the online databases of PubMed, Web of Science, and ScienceDirect from their inception to December 1, 2023. Thirteen eligible studies were included from the overall initial identification of 3559 records. The effect size was expressed as standardized beta coefficients with 95% confidence intervals (CIs) by the restricted maximum-likelihood approach with a weighted random-effects model. Prenatal exposure to environmental heavy metals was associated with a shorter newborn TL (standardized beta = −0.04; 95% CI: −0.08, 0.00; p = 0.05). Subgroup analysis showed that prenatal exposure to cadmium was significantly, negatively associated with TL in newborns (standardized beta = −0.05; 95% CI: −0.10, −0.01; p = 0.021). Heavy metal exposure during the third trimester was significantly associated with a shorter TL in newborns (standardized beta = −0.05; 95% CI: −0.11, −0.01; p = 0.045). No significant association was found between the newborn's sex and exposure sample type. This study provides evidence for the negative effect of prenatal exposure to heavy metals on newborn TL. In particular, cadmium exposure and exposure during the third trimester of pregnancy are critical factors associated with heavy metal-induced TL shortening.

Cytomegalovirus Infection in Pregnancy Prevention and Treatment Options: A Systematic Review and Meta-Analysis.

Cytomegalovirus (CMV) infection is a significant health concern affecting numerous expectant mothers across the globe. CMV is the leading cause of health problems and developmental delays among infected infants. Notably, this study examines CMV infection in pregnancy, its management, prevention mechanisms, and treatment options. Specifically, information from the Cochrane Library, PUBMED, Wiley Online, Science Direct, and Taylor Francis databases were reviewed along with additional records identified through the register, the Google Scholar search engine. Based on the search, 21 articles were identified for systematic review. A total of six randomized controlled trials (RCTs) were utilized for a meta-analytic review. As heterogeneity was substantial, the random effects model was used for meta-analysis. Utilizing the random-effects model, the restricted maximum likelihood (REML) approach, the estimate of effect size (d = -0.479, 95% CI = -0.977 to 0.019, p = 0.060) suggests the results are not statistically significant, so it cannot be inferred that the prevention methods used were effective, despite an inverse relationship between treatment and number of infected cases. The findings indicated that several techniques are used to prevent, diagnose, and manage CMV infection during pregnancy, including proper hygiene, ultrasound examination (US), magnetic resonance imaging (MRI), amniocentesis, viremia, hyperimmunoglobulin (HIG), and valacyclovir (VACV). The current review has significant implications for addressing CMV infection in pregnancy. Specifically, it provides valuable findings on contemporary management interventions to prevent and treat CMV infection among expectant mothers. Therefore, it allows relevant stakeholders to address these critical health concerns and understand the effectiveness of the proposed prevention and treatment options.

Quantitative Associations between Season, Month, and Temperature-Humidity Index with Milk Yield, Composition, Somatic Cell Counts, and Microbial Load: A Comprehensive Study across Ten Dairy Farms over an Annual Cycle.

This current study addresses the knowledge gap regarding the influence of seasons, months, and THI on milk yield, composition, somatic cell counts (SCC), and total bacterial counts (TBC) of dairy farms in northeastern regions of Iran. For this purpose, ten dairy herds were randomly chosen, and daily milk production records were obtained. Milk samples were systematically collected from individual herds upon delivery to the dairy processing facility for subsequent analysis, including fat, protein, solids-not-fat (SNF), pH, SCC, and TBC. The effects of seasons, months, and THI on milk yield, composition, SCC, and TBC were assessed using an analysis of variance. To account for these effects, a mixed-effects model was utilized with a restricted maximum likelihood approach, treating month and THI as fixed factors. Our investigation revealed noteworthy correlations between key milk parameters and seasonal, monthly, and THI variations. Winter showed the highest milk yield, fat, protein, SNF, and pH (p < 0.01), whereas both SCC and TBC reached their lowest values in winter (p < 0.01). The highest values for milk yield, fat, and pH were recorded in January (p < 0.01), while the highest protein and SNF levels were observed in March (p < 0.01). December marked the lowest SCC and TBC values (p < 0.01). Across the THI spectrum, spanning from -3.6 to 37.7, distinct trends were evident. Quadratic regression models accounted for 34.59%, 21.33%, 4.78%, 20.22%, 1.34%, 15.42%, and 13.16% of the variance in milk yield, fat, protein, SNF, pH, SCC, and TBC, respectively. In conclusion, our findings underscore the significant impact of THI on milk production, composition, SCC, and TBC, offering valuable insights for dairy management strategies. In the face of persistent challenges posed by climate change, these results provide crucial guidance for enhancing production efficiency and upholding milk quality standards.

Genome-wide Association Study of Birth Weight in Bali Cattle (Bos javanicus)

The genome-wide association study is popularly used to identify markers associated with high economic traits in cattle breeds worldwide. However, this method has never been applied to Bali cattle ( Bos javanicus ), which are indigenous to Indonesia. Therefore, this study aimed to determine whether the GWAS can also be employed to figure out genomic regions potentially affecting Bali cattle's birth weight trait. In this study, phenotypic and genotypic data of 48 Bali cattle were used to figure out genomic regions which are potentially affecting the birth weight trait of Bali cattle. Those samples were genotyped using an Illumina bead chip array with 53.218 single nucleotide polymorphisms (SNPs). The estimation of the variance proportion explained by each SNP was conducted by a restricted maximum likelihood (REML) approach. The result showed that the birth weight trait in Bali cattle was affected by multiple regions dispersed over the genome, especially chromosomes 20 and 21. This study demonstrated that GWAS can be used effectively and discovered two intriguing regions related to quantitative trait loci and genes, namely STXBP6 and TERT , which previously had been linked to cattle birth weight and growth. These findings are important for developing and refining genomic-based selection programs for Bali cattle in Indonesia.

Genetic Parameters for Functional Longevity, Type Traits, and Production in the Serbian Holstein.

In this study, the authors focused on the evaluation of the genetic parameters of longevity, milk yield traits, and type traits in dairy cattle populations in the Republic of Serbia. The total dataset used consisted of production records and pedigree data for 32,512 Holstein cows that calved from 1981 to 2015. The animal model was applied to determine the variance and covariance components and genetic parameters of the analyzed traits by applying the restricted maximum likelihood (REML) approach and using the programs VCE6 and PEST. The heritability of longevity traits was estimated using the Survival Kit V6.0 software package. Variance and covariance were estimated for five production traits: milk yield (MY), fat yield (FY), protein yield (PY), milk fat content (MC), and protein content (PC), and three longevity traits: length of productive life (LPL), lifetime milk yield (LMY), and the number of lactations achieved (NL) as well as for 18 standard type traits. Heritabilities for the milk, fat, and protein yield traits were 0.20 (MY), 0.15 (FY), and 0.19 (PY), respectively. The estimated coefficients of heritability for the longevity traits were higher when using the Weibull proportional hazards model compared to the traditional linear methods and ranged from 0.08 for NL to 0.10 for LPL. Heritability values for the type traits varied from a low of 0.10 (RLSsv-rear legs set-side view) to medium values of 0.32 (ST-stature). Genetic correlations were found between MY and the following longevity traits: LPL, LMY, and NL with values of -0.18, -0.11, and -0.09, respectively. Genetic correlations were found between MY and a number of linear type traits and varied from 0.02 (between MY and RUH-rear udder height) to 0.28 (between MY and FUA-fore udder attachment). Genetic correlations between the 18 investigated type traits ranged from -0.33 between TL (top line) and RTP (rear teats position) to 0.71 between AN (angularity) and RUH (rear udder height). Genetic correlations between most linear type traits and longevity traits (LPL, LMY, and NL) were generally negative and very low. The highest positive genetic correlation was found between UD and LPL (rxy = 0.38).

Seleção de linhagens superiores de feijão-caupi derivadas de cultivares locais para a região semiárida brasileira

Abstract The objective of this work was to select superior cowpea lines derived from local cultivars for the semiarid region of southeastern Piauí, Brazil. A total of 38 genotypes (36 lines and two commercial cultivars) were evaluated in a randomized complete block design, with two replicates, in the municipality of Ipiranga do Piauí, in the state of Piauí. The following traits were evaluated: number of days to the onset of flowering, pod length, number of grains per pod, weight of 100 grains, commercial grain quality, and grain yield. Deviance analyzes were performed using the restricted maximum likelihood and best unbiased predictor approach, the parameters and genetic values were estimated, and simultaneous selection was carried out via the sum of ranks index. The likelihood ratio test showed a significant difference between the evaluated genotypes. Lines 36, 5, 24, 8, 2, 23, 29, 28, 34, 6, 19, 11, 7, and 20 are superior for the set of evaluated traits and, therefore, are the most promising to compose the advanced trial of the cowpea breeding program and to generate cultivars for family farmers in the semiarid region of southeastern Piauí.

Comparison of nonlinear functions in male and female chickens at different seasons using restricted Maximum Likelihood Approach

Comparison of nonlinear functions in male and female chickens at different seasons using restricted Maximum Likelihood Approach

Estimation of genetic parameters of Wood’s lactation curve parameters using Bayesian and REML methods for milk production trait of Holstein dairy cattle

ABSTRACT Bayesian and average information restricted maximum likelihood (AI-REML) approaches were used to estimate variance components and genetic parameters of Wood’s lactation curve parameters of milk production traits in three lactations of Iranian Holstein dairy cows. Wood’s function parameters (a, b, and c) of each lactation were initially estimated for individual cows using the NLIN procedure of the SAS programme, separately. The variance components of the parameters of each lactation were estimated by the AI-REML method using a single-trait animal model inairemlf90 programme. To do this with the Bayesian method by using the Gibbs sampling technique, the same animal model was used in the gibbs3f90 programme. The estimated heritability values by the AI-REML were 0.018, 0.00, and 0.019 for a parameter; 0.015, 0.007, and 0.02 for b parameter; and 0.23, 0.625, and 0.049 for c parameter in the 1st, 2nd, and 3rd lactations, respectively. The corresponding results from the Bayesian procedure (a posterior means) were 0.019, 0.01, and 0.035 for a parameter; 0.018, 0.019, and 0.043 for b parameter; and 0.024, 0.1, and 0.058 for c parameter, respectively. These obtained results reveal that the differences between the Bayesian and AI-REML methods in terms of estimation of heritability and variance components were small.

Location-scale models for meta-analysis.

Heterogeneity is commonplace in meta-analysis. When heterogeneity is found, researchers often aim to identify predictors that account for at least part of such heterogeneity by using mixed-effects meta-regression models. Another potentially relevant goal is to focus on the amount of heterogeneity as a function of one or more predictors, but this cannot be examined with standard random- and mixed-effects models, which assume a constant (i.e., homoscedastic) value for the heterogeneity variance component across studies. In this paper, we describe a location-scale model for meta-analysis as an extension of the standard random- and mixed-effects models that not only allows an examination of whether predictors are related to the size of the outcomes (i.e., their location), but also the amount of heterogeneity (i.e., their scale). We present estimation methods for such a location-scale model through maximum and restricted maximum likelihood approaches, as well as methods for inference and suggestions for visualization. We also provide an implementation via the metafor package for R that makes this model readily available to researchers. Location-scale models can provide a useful tool to researchers interested in heterogeneity in meta-analysis, with the potential to enhance the scope of research questions in the field of evidence synthesis.

Epigenome-wide contributions to individual differences in childhood phenotypes: a GREML approach

BackgroundDNA methylation is an epigenetic mechanism involved in human development. Numerous epigenome-wide association studies (EWAS) have investigated the associations of DNA methylation at single CpG sites with childhood outcomes. However, the overall contribution of DNA methylation across the genome (R2Methylation) towards childhood phenotypes is unknown. An estimate of R2Methylation would provide context regarding the importance of DNA methylation explaining variance in health outcomes. We therefore estimated the variance explained by epigenome-wide cord blood methylation (R2Methylation) for five childhood phenotypes: gestational age, birth weight, and body mass index (BMI), IQ and ADHD symptoms at school age. We adapted a genome-based restricted maximum likelihood (GREML) approach with cross-validation (CV) to DNA methylation data and applied it in two population-based birth cohorts: ALSPAC (n = 775) and Generation R (n = 1382).ResultsUsing information from > 470,000 autosomal probes we estimated that DNA methylation at birth explains 32% (SDCV = 0.06) of gestational age variance and 5% (SDCV = 0.02) of birth weight variance. The R2Methylation estimates for BMI, IQ and ADHD symptoms at school age estimates were near 0% across almost all cross-validation iterations.ConclusionsThe results suggest that cord blood methylation explains a moderate degree of variance in gestational age and birth weight, in line with the success of previous EWAS in identifying numerous CpG sites associated with these phenotypes. In contrast, we could not obtain a reliable estimate for school-age BMI, IQ and ADHD symptoms. This may reflect a null bias due to insufficient sample size to detect variance explained in more weakly associated phenotypes, although the true R2Methylation for these phenotypes is likely below that of gestational age and birth weight when using DNA methylation at birth.

Genetic studies on the estimates of (Co) variance components for growth traits in Barbari goat

The objectives of the current study were to evaluate the genetic progress and variability for growth traits and also to suggest a suitable direction to the existing breeding program of the Barbari goat population in India. The analysis included 10,648 animals descended from 2697 dams and 420 sires at the ICAR-Central Institute for Research on Goats, Makhdoom between 1993 and 2020 (28 years). The effect of fixed factors such as period and season of kidding, sex of the kid, type of birth, parity of doe, and doe weight at kidding on live weights were significant (P < 0.05). Animal models with average information restricted maximum likelihood (AIREML) approach were used to estimate genetic parameters for growth traits. The mean of inbreeding coefficient for the whole pedigree was 2.16%. For the inbred animals it was 3.32%, indicating within normal estimate for average inbreeding in a nucleus flock. Results suggested that the genetic model for additive direct effects of animal and dam and their covariance, as well as dam's permanent environment, was best for birth weight (BW), weaning weight (WW), weight at six months (6 W), and weight at nine months (9 W), whereas the model with direct effects of animal and dam and their covariance was best for weight at 12 months (12 W). The estimates of direct heritability (h2) for Barbari goat were 0.31 ± 0.04, 0.23 ± 0.03, 0.18 ± 0.03, 0.13 ± 0.03 and 0.21 ± 0.04 for BW, WW, 6 W, 9 W, and 12 W, respectively. However, the estimates were influenced by negative correlation between direct additive effects of animal and dam. We also observed substantial variance for additive maternal effect (0.09–0.15) and maternal permanent environment effect (0.11–0.17) across traits analysed. Pre-weaning growth traits had a significant maternal effect that receded as age advanced. The prospect of selecting Barbari goats for growth traits is indicated by moderate h2 estimates. The positive and desired genetic trend revealed favourable response to selection in the nucleus of the Barbari goat for growth traits. The significant and positive genetic and phenotypic correlation between post-weaning weights (6–9 W= 0.89) suggests scope for early selection of Barbari goats by decreasing the age for selection from 9 to 6 months.

Estimation of co-variance components and genetic parameters of fertility and production traits in crossbred cattle of Kerala

Estimation of co-variance components and genetic parameters of fertility and production traits will help to find out the relative importance of genetic and environmental components of each trait and to develop a genetic evaluation system for overall improvement in performances of crossbred cattle of Kerala. In the present study, major fertility trait considered was daughters pregnancy rate (DPR), measures the percentage of non-pregnant animals that become pregnant during each oestrous cycle. Data pertaining to 1180 crossbred cattle sired by 208 Frieswal bulls, spread over a period of 16 years from 2003 to 2019, maintained at different farms of Kerala Veterinary and Animal Sciences University and field centres of ICAR- Filed Progeny Testing Scheme were analysed in the study. Estimates of covariance components and genetic parameters were obtained using restricted maximum likelihood (REML) approach using average information (AI) algorithm. It was observed that DPR had low heritability (0.092 ± 0.03), compared to 305 days milk yield (MY) (0.170 ± 0.094) and fat percent (FP) (0.173 ± 0.072). Phenotypic (rp), genetic (rg) and residual (re) correlation indicated unfavourable association of fertility with production traits. The estimates of variance and (co)variance components were computed by multivariate animal model. The results indicated that DPR was having lower direct additive (σ2a) 0.046 and environmental variance (σ2c) 0.063 compared to other traits. Highest additive genetic variance (σ2a) 27035.8 was obtained for MY. The study estimates the magnitude of correlations and covariances of fertility and production traits. Since DPR had lower additive genetic variance and negative association with milk production traits in cattle, it would be included as an indirect measure in the evaluation and breeding programs of crossbred cattle of Kerala.

Genetic relationship between superovulatory response traits and carcass traits in Japanese Black cattle.

We estimated the genetic correlations between superovulatory response traits and carcass traits in Japanese Black cattle. As regards the superovulatory response traits in cows, we analyzed the phenotypic records of the total number of embryos and oocytes (TNE) and the number of good embryos (NGE) collected from 1532 donors between 2008 and 2018. As regards the carcass traits in fattened animals, we analyzed the phenotypic records for cold carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, estimated yield percent, and marbling score for 1448 progenies derived from 596 donors and slaughtered between 2004 and 2020. Variance components were estimated using single-trait and two-trait animal models and the restricted maximum likelihood approach. The estimated genetic correlations with the carcass traits ranged from -0.05 to 0.04 for TNE and from -0.14 to 0.04 for NGE, and their standard errors ranged from 0.10 to 0.14. These results imply that the genetic relationship between the superovulatory response traits in Japanese Black donor cows and the carcass traits in their fattened progenies was weak to negligible. Therefore, we concluded that selecting donors with superior genetic ability for superovulatory responses would not have antagonistic effects on carcass performance in their fattened progenies.

Estimation of An Optimum Spatial Autocorrelation in Linear Mixed Models Containing Spatial Effects

Fay-Herriot model assumes that the random effects between regions (areas) are independent of each other. This allows the regions to be mutually independent so that the estimators obtained are unbiased estimators. In cases where the regions are not mutually independent, it can develop a model in which the assumptions are violated (not fulfilled) or allow the regions to be dependent. The development of this model is known as small area estimation (SAE) with spatial effects. In small area estimation with spatial effects, one of the important parameters is the spatial autocorrelation parameter. In various small area estimation with spatial effects, it is still very rare to generate estimators of the spatial autocorrelation parameter. Most of the parameter values used are known, that is by trying to enter several spatial autocorrelation parameter values to show that the addition of regional aspects can increase the accuracy of the small area estimation. In addition, they have also tried a restricted maximum likelihood approach in estimating the spatial autocorrelation and component variance but they still assume that the sampling variance is known (assigned). Therefore, this research proposes a concentrated log-likelihood function by means of numerical procedure to find an optimum estimate value for spatial autocorrelation coefficient where both a sampling variance and a component variance are unknown. Parameters estimators obtained in the models, fixed and random effects parameters, are proved to be consistent.

A semiparametric mixed-effects model for censored longitudinal data.

In longitudinal studies involving laboratory-based outcomes, repeated measurements can be censored due to assay detection limits. Linear mixed-effects (LMEs) models are a powerful tool to model the relationship between a response variable and covariates in longitudinal studies. However, the linear parametric form of linear mixed-effect models is often too restrictive to characterize the complex relationship between a response variable and covariates. More general and robust modeling tools, such as nonparametric and semiparametric regression models, have become increasingly popular in the last decade. In this article, we use semiparametric mixed models to analyze censored longitudinal data with irregularly observed repeated measures. The proposed model extends the censored linear mixed-effect model and provides more flexible modeling schemes by allowing the time effect to vary nonparametrically over time. We develop an Expectation-Maximization (EM) algorithm for maximum penalized likelihood estimation of model parameters and the nonparametric component. Further, as a byproduct of the EM algorithm, the smoothing parameter is estimated using a modified linear mixed-effects model, which is faster than alternative methods such as the restricted maximum likelihood approach. Finally, the performance of the proposed approaches is evaluated through extensive simulation studies as well as applications to data sets from acquired immune deficiency syndrome studies.

Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas.

OBJECTIVESulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction.RESEARCH DESIGN AND METHODSAs an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions.RESULTSAfter establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction at a genome-wide scale (P < 5 × 10−8). The C allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), P = 2.39 × 10−8), lower reduction in HbA1c. Similarly, the C allele was associated with higher glucose trough levels (β = 1.61, P = 0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). In 3,029 human whole blood samples, the C allele is a cis eQTL for increased expression of GXYLT1 (β = 0.21, P = 2.04 × 10−58). The C allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA1c (P = 4.80 × 10−8). In 1,183 human liver samples, the C allele at rs10770791 is a cis eQTL for reduced SLCO1B1 expression (P = 1.61 × 10−7), which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed (P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor.CONCLUSIONSWe have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs.

Integrating a problem-solving intervention with routine care to improve psychosocial functioning among mothers of children with sickle cell disease: A randomized controlled trial.

ObjectiveTo assess the feasibility of a problem-solving skills training intervention in improving psychological outcomes in mothers of infants with sickle cell disease (SCD).Design and methodsThis parallel randomized controlled trial recruited 64 babies with SCD, 6 to 12 months of age, and their mothers. Baseline measurements assessed mothers’ coping and problem-solving skills, depression, and parental stress before random assignment to intervention or control groups (n = 32 each). Problem-solving skills intervention was delivered through 6 monthly sessions, when babies attended for routine penicillin prophylaxis. All measurements were repeated for both groups at the end of the intervention period. Intention to treat analysis used repeated measures mixed models with the restricted estimation maximum likelihood approach.ResultsThe problem-solving intervention had no significant effect on mothers’ problem-solving skills (adjusted treatment effect: -1.69 points (95% CI:-5.62 to 2.25)), coping behaviours (adjusted treatment effect: 0.65 points (95% CI:- -7.13 to 8.41)) or depressive symptoms (adjusted treatment effect: -0.41 (95% CI: -6.00 to 5.19)). It reduced mothers’ level of difficulty in managing stressful events by 9.5 points (95% CI (-16.86 to -2.16); effect size: 0.21 SD). In the subgroup of mothers at risk of depression (n = 31 at baseline), the intervention reduced depression scores with treatment effect of 10.4 points (95%CI: -18.83 to -1.88; effect size: 0.67 SD).ConclusionThis problem-solving skills intervention study suggests feasibility and possible efficacy in improving some maternal outcomes. Further refinement and culturally appropriate adaptations of the intervention could lead to stronger effects.