Systematic comparison of published host gene expression signatures for bacterial/viral discrimination.

BACKGROUND: Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. METHODS: This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. RESULTS: Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. CONCLUSIONS: In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

SNP characteristics and validation success in genome wide association studies.

Genome wide association studies (GWASs) have identified tens of thousands of single nucleotide polymorphisms (SNPs) associated with human diseases and characteristics. A significant fraction of GWAS findings can be false positives. The gold standard for true positives is an independent validation. The goal of this study was to identify SNP features associated with validation success. Summary statistics from the Catalog of Published GWASs were used in the analysis. Since our goal was an analysis of reproducibility, we focused on the diseases/phenotypes targeted by at least 10 GWASs. GWASs were arranged in discovery-validation pairs based on the time of publication, with the discovery GWAS published before validation. We used four definitions of the validation success that differ by stringency. Associations of SNP features with validation success were consistent across the definitions. The strongest predictor of SNP validation was the level of statistical significance in the discovery GWAS. The magnitude of the effect size was associated with validation success in a non-linear manner. SNPs with risk allele frequencies in the range 30-70% showed a higher validation success rate compared to rarer or more common SNPs. Missense, 5'UTR, stop gained, and SNPs located in transcription factor binding sites had a higher validation success rate compared to intergenic, intronic and synonymous SNPs. There was a positive association between validation success and the level of evolutionary conservation of the sites. In addition, validation success was higher when discovery and validation GWASs targeted the same ethnicity. All predictors of validation success remained significant in a multivariate logistic regression model indicating their independent contribution. To conclude, we identified SNP features predicting validation success of GWAS hits. These features can be used to select SNPs for validation and downstream functional studies.

Genotypic spectrum of 21-hydroxylase deficiency in an endogamous population.

BACKGROUND: Congenital adrenal hyperplasia (CAH) due to autosomal recessive 21-hydroxylase deficiency (21-OHD) is caused by defects in the CYP21 (CYP21A2) gene. Several mutations have been identified in the CYP21 (CYP21A2) gene of patients with 21-OHD. We aimed at determining the frequency of these mutations among a group of Egyptian patients and studying the genotype-phenotype correlation. METHODS: Forty-seven patients with CAH due to 21-OHD from 42 different families diagnosed by clinical and hormonal evaluation and classified accordingly into salt wasting (SW) and simple virilizing (SV) phenotypes were enrolled. Their ages ranged between 1.78 and 18.99 years. Molecular analysis of the CYP21 (CYP21A2) gene was performed for the detection of eleven common mutations: P30L, I2 splice (I2 G), Del 8 bp E3 (G110del8nt), I172N, cluster E6 (I236N, V237E, M239K), V281L, L307 frameshift (F306 + T), Q318X, R356W, P453S, R483P by polymerase chain reaction (PCR) and reverse hybridization. RESULTS: Disease-causing mutations were identified in 47 patients, 55.31% of them were compound heterozygous. The most frequent mutations were I2 splice (25.43%), followed by cluster E6 (16.66%) and P30L (15.78%). Two point mutations (P453S, R483P) were not identified in any patient. In the SW patients, genotypes were more compatible with their phenotypes. CONCLUSION: Molecular characterization should be considered along with clinical and biochemical diagnosis of CAH since it could confirm the diagnosis, outline the treatment strategy and morbidity, and ensure proper genetic counseling.

Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer's disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10-13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

New and sex-specific migraine susceptibility loci identified from a multiethnic genome-wide meta-analysis.

Migraine is a common disabling primary headache disorder that is ranked as the most common neurological cause of disability worldwide. Women present with migraine much more frequently than men, but the reasons for this difference are unknown. Migraine heritability is estimated to up to 57%, yet much of the genetic risk remains unaccounted for, especially in non-European ancestry populations. To elucidate the etiology of this common disorder, we conduct a multiethnic genome-wide association meta-analysis of migraine, combining results from the GERA and UK Biobank cohorts, followed by a European-ancestry meta-analysis using public summary statistics. We report 79 loci associated with migraine, of which 45 were novel. Sex-stratified analyses identify three additional novel loci (CPS1, PBRM1, and SLC25A21) specific to women. This large multiethnic migraine study provides important information that may substantially improve our understanding of the etiology of migraine susceptibility.

Determinants of hepatic insulin clearance - Results from a Mendelian Randomization study.

AIMS/HYPOTHESIS: Besides insulin resistance, type 2 diabetes associates with decreased hepatic insulin clearance (HIC). We now tested for causal relationship of HIC to liver fat accumulation or features of the metabolic syndrome. METHODS: HIC was derived from oral glucose tolerance tests with the "Oral C-peptide and Insulin Minimal Models" (n = 3311). Liver fat was quantified by magnetic resonance spectroscopy (n = 1211). Mendelian Randomization was performed using established single nucleotide polymorphisms (SNPs; 115 for liver fat, 155 alanine-aminotransferase, 37 insulin sensitivity, 37 insulin secretion, 72 fasting insulin, 5285 BMI, 163 visceral fat, 270 waist circumference, 442 triglycerides, 620 HDL-Cholesterol, 193 C-reactive protein, 53 lipodystrophy-like phenotypes). RESULTS: HIC associated inversely with liver fat (p < 0.003) and insulin sensitivity (p < 0.0001). Both liver fat and HIC were independently associated with insulin sensitivity (p < 0.0001). Neither liver fat nor alanine-aminotransferase were causally linked to HIC, as indicated by Mendelian Randomization (Nliver fat = 1054, NHIC = 2254; Nalanineaminotranferase = 1985, NHIC = 2251). BMI-related SNPs were causally associated with HIC (NBMI = 2772, NHIC = 2259, p < 0.001) but not waist circumference-SNPs (NSNPs-waist circumference = 2751, NHIC = 2280). Genetically determined insulin sensitivity was not causally related to HIC (Ninsulin sensitivity = 2752, NHIC = 2286). C-reactive protein and HDL were causally associated with HIC, with higher C-reactive protein and lower HDL leading to higher HIC (NC-reactive protein = 2660, NHIC = 2240; NHDL = 2694, NHIC = 2275). CONCLUSIONS: This Mendelian Randomization analysis does not support a causal link between hepatic steatosis and HIC. Other components of the metabolic syndrome seem to compensate peripheral hyperinsulinemia by increasing hepatic insulin extraction.

Challenges of CYP21A2 genotyping in children with 21-hydroxylase deficiency: determination of genotype-phenotype correlation using next generation sequencing in Southeastern Anatolia.

BACKGROUND/PURPOSE: Although it is known that there is generally a good correlation between genotypes and phenotypes, the number of studies reporting discrepancies has recently increased, exclusively between milder genotypes and their phenotypes due to the complex nature of the CYP21A2 gene and methodological pitfalls. This study aimed to assess CYP21A2 genotyping in children with 21-hydroxylase deficiency (21-OHD) and establish their predictive genotype-phenotype correlation features using a large cohort in Southeastern Anatolia's ethnically diverse population. METHODS: The patients were classified into three groups: salt-wasting (SW), simple virilizing (SV) and non-classical (NC). The genotypes were categorized into six groups due to residual enzyme activity: null-A-B-C-D-E. CYP21A2 genotyping was performed by sequence-specific primer and sequenced with next generation sequencing (NGS), and the expected phenotypes were compared to the observed phenotypes. RESULTS: A total of 118 unrelated children with 21-OHD were included in this study (61% SW, 24.5% SV and 14.5% NC). The pathogenic variants were found in 79.5% of 171 mutated alleles (60.2%, 22.2%, and 17.6% in SW, SV and NC, respectively). Patient distribution based on genotype groups was as follows: null-16.1%, A-41.4%, B-6.0%, C-14.4%, E-22%). In2G was the most common pathogenic variant (33.9% of all alleles) and the most common variant in the three phenotype groups (SW-38.8%, SV-22.2% and NC-23.3%). The total genotype-phenotype correlation was 81.5%. The correlations of the null and A groups were 100% and 76.1%, respectively, while it was lower in group B and poor in group C (71.4% and 23.5%, respectively). CONCLUSION: This study revealed that the concordance rates of the severe genotypes with their phenotypes were good, while those of the milder genotypes were poor. The discrepancies could have resulted from the complex characteristics of 21-OHD genotyping and the limitations of using NGS alone without integrating with other comprehensive methods.

Associations between Vascular Endothelial Growth Factor Gene Polymorphisms and Different Types of Diabetic Retinopathy Susceptibility: A Systematic Review and Meta-Analysis.

BACKGROUND: Vascular endothelial growth factor (VEGF) gene polymorphisms have been shown to be associated with the risk of diabetic retinopathy (DR), but the results were inconsistent. The aim of this study was to systematically assess the associations between VEGF gene polymorphisms and different types of DR (nonproliferative DR and proliferative DR). METHODS: Electronic databases PubMed, Embase, Web of Science, CNKI, and WANFANG DATA were searched for articles on the associations between VEGF gene polymorphisms and different types of DR up to November 6, 2019. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and subgroup analyses were conducted by ethnicity. Sensitivity analysis was conducted to assess the stability of the results. Publication bias was assessed by using the Egger regression asymmetry test and visualization of funnel plots. A systematic review was conducted for polymorphisms with a high degree of heterogeneity (I 2 > 75%) or studied in only one study. RESULTS: A total of 13 and 18 studies analyzed the associations between VEGF SNPs and nonproliferative DR (NPDR) as well as proliferative DR (PDR), respectively. There were significant associations between rs2010963 and NPDR in Asian (dominant model: OR = 1.29, 95%CI = 1.04 - 1.60); and rs2010963 is associated with PDR in total population (dominant model: OR = 1.20, 95%CI = 1.03 - 1.41), either Asian (recessive model: OR = 1.57, 95%CI = 1.04 - 2.35) or Caucasian (recessive model: OR = 1.83, 95%CI = 1.28 - 2.63). Rs833061 is associated with PDR in Asian (recessive model: OR = 1.58, 95%CI = 1.11 - 2.26). Rs699947 is associated with NPDR in the total population (dominant model: OR = 2.04, 95%CI = 1.30 - 3.21) and associated with PDR in Asian (dominant model: OR = 1.72, 95%CI = 1.05 - 2.84). CONCLUSIONS: Rs2010963, rs833061, and rs699947 are associated with NPDR or PDR, which may be involved in the occurrence and development of DR.

Catechol-O-Methyltransferase Genotype, Frailty, and Gait Speed in a Biracial Cohort of Older Adults.

OBJECTIVE: To examine whether the association between dopamine-related genotype and gait speed differs according to frailty status or race. DESIGN: Cross-sectional population-based study (Cardiovascular Health Study). SETTING: Multicenter study, four U.S. sites. PARTICIPANTS: Volunteer community-dwelling adults aged 65 years and older, without evidence of Parkinson's disease (N = 3,744; 71 years; 82% White; 39% male). MEASUREMENTS: Gait speed (usual pace; m/s), physical frailty (Fried definition), and genetic polymorphism of catechol-O-methyltransferase (COMT; rs4680), an enzyme regulating tonic brain dopamine levels, were assessed. Interaction of COMT by frailty and by race predicting gait speed were tested, and, if significant, analyses were stratified. Multivariable regression models of COMT predicting gait speed were adjusted for demographics and locomotor risk factors. Sensitivity analyses were repeated, stratified by clinical cutoffs of gait speed (0.6 and 1.0 m/s) instead of frailty status. RESULTS: The interaction of COMT by frailty and COMT by race were P = .02 and P = .01, respectively. Compared with Met/Met (higher dopaminergic signaling), the Val/Val group (lower dopaminergic signaling) walked marginally more slowly in the full cohort (0.87 vs 0.89 m/s; P = .2). Gait speed differences were significant for frail (n = 220; 0.55 vs 0.63 m/s; P = .03), but not for prefrail (n = 1,691; 0.81 vs 0.81 m/s; P = .9) or nonfrail (n = 1,833; 0.98 vs 0.97 m/s; P = .7); results were similar in fully adjusted models. Among frail, associations were similar for Whites and Blacks, with statistical significance for Whites only. Associations stratified by clinical cutoffs of gait speed were not significant. CONCLUSION: The association of dopamine-related genotype with gait speed is stronger among adults with frailty compared with those without frailty. The potential effects of dopaminergic signaling on preserving physical function in biracial cohorts of frail adults should be further examined.

Genetic association research in football: A systematic review.

Genetic variation is responsible for a large amount of the inter-individual performance disparities seen in sport. As such, in the last ten years genetic association studies have become more common; with one of the most frequently researched sports being football. However, the progress and methodological rigour of genetic association research in football is yet to be evaluated. Therefore, the aim of this paper was to identify and evaluate all genetic association studies involving football players and outline where and how future research should be directed. Firstly, a systematic search was conducted in the Pubmed and SPORTDiscus databases, which identified 80 eligible studies. Progression analysis revealed that 103 distinct genes have been investigated across multiple disciplines; however, research has predominately focused on the association of the ACTN3 or ACE gene. Furthermore, 55% of the total studies have been published within the last four years; showcasing that genetic association research in football is increasing at a substantial rate. However, there are several methodological inconsistencies which hinder research implications, such as; inadequate description or omission of ethnicity and on-field positions. Furthermore, there is a limited amount of research on several key areas crucial to footballing performance, in particular; psychological related traits. Moving forward, improved research designs, larger sample sizes, and the utilisation of genome-wide and polygenic profiling approaches are recommended. Finally, we introduce the Football Gene Project, which aims to address several of these limitations and ultimately facilitate greater individualised athlete development within football.

Quantile-Dependent Expressivity and Gene-Lifestyle Interactions Involving High-Density Lipoprotein Cholesterol.

BACKGROUND: The phenotypic expression of a high-density lipoprotein (HDL) genetic risk score has been shown to depend upon whether the phenotype (HDL-cholesterol) is high or low relative to its distribution in the population (quantile-dependent expressivity). This may be due to the effects of genetic mutations on HDL-metabolism being concentration dependent. METHOD: The purpose of this article is to assess whether some previously reported HDL gene-lifestyle interactions could potentially be attributable to quantile-dependent expressivity. SUMMARY: Seventy-three published examples of HDL gene-lifestyle interactions were interpreted from the perspective of quantile-dependent expressivity. These included interactive effects of diet, alcohol, physical activity, adiposity, and smoking with genetic variants associated with the ABCA1, ADH3, ANGPTL4, APOA1, APOA4, APOA5, APOC3, APOE, CETP, CLASP1, CYP7A1, GALNT2, LDLR, LHX1, LIPC, LIPG, LPL, MVK-MMAB, PLTP, PON1, PPARα, SIRT1, SNTA1,and UCP1genes. The selected examples showed larger genetic effect sizes for lifestyle conditions associated with higher vis-à-vis lower average HDL-cholesterol concentrations. This suggests these reported interactions could be the result of selecting subjects for conditions that differentiate high from low HDL-cholesterol (e.g., lean vs. overweight, active vs. sedentary, high-fat vs. high-carbohydrate diets, alcohol drinkers vs. abstainers, nonsmokers vs. smokers) producing larger versus smaller genetic effect sizes. Key Message: Quantile-dependent expressivity provides a potential explanation for some reported gene-lifestyle interactions for HDL-cholesterol. Although overall genetic heritability appears to be quantile specific, this may vary by genetic variant and environmental exposure.

Contribution of Rs780094 and Rs1260326 Polymorphisms in GCKR Gene to Non-alcoholic Fatty Liver Disease: A Meta-Analysis Involving 26,552 Participants.

BACKGROUND: Many published studies attempted to elucidate the implication of glucokinase regulator gene (GCKR) polymorphisms in the susceptibility to non-alcoholic fatty liver disease (NAFLD), but the results among them were still controversial. OBJECTIVE: This meta-analysis aims to precisely assess the relationship between the GCKR polymorphisms and the risk of NAFLD. METHODS: Systematic computerized searches in six databases were performed and updated on April 6, 2020. Meta-analyses were conducted by calling the R programs based on accumulated epidemiological data. Odds ratio (OR) and 95% confidential interval (CI) were calculated to summarize the effect estimates. RESULTS: In total, 25 studies including 6,598 cases and 19,954 controls were included. The pooled estimates indicated that the T allele carrier of the GCKR rs780094 polymorphism has predisposition to NAFLD (allele model: OR: 1.20, 95% CI: 1.11~1.29; homozygote model: OR: 1.38, 95% CI: 1.15~1.67; heterozygote model: OR: 1.25, 95% CI: 1.12~1.39; dominant model: OR: 1.29, 95% CI: 1.13~1.47; recessive model: OR: 1.18, 95% CI: 1.06~1.31), and the same as the rs1260326 polymorphism (allele model: OR: 1.32, 95% CI: 1.22~1.42; homozygote model: OR: 1.65, 95% CI: 1.40~1.94; heterozygote model: OR: 1.24, 95% CI: 1.07~1.43; dominant model: OR: 1.39, 95% CI: 1.21~1.59; recessive model: OR: 1.44, 95% CI: 1.28~1.62). Further stratified analyses according to age and ethnicity confirmed the statistical existence in most subgroups. CONCLUSION: This meta-analysis suggested that both of the GCKR rs780094 and rs1260326 polymorphisms are significantly associated with the increased risk of NAFLD.

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.

Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype-phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene-disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases.

An Overview of the Main Genetic, Epigenetic and Environmental Factors Involved in Autism Spectrum Disorder Focusing on Synaptic Activity.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects social interaction and communication, with restricted interests, activity and behaviors. ASD is highly familial, indicating that genetic background strongly contributes to the development of this condition. However, only a fraction of the total number of genes thought to be associated with the condition have been discovered. Moreover, other factors may play an important role in ASD onset. In fact, it has been shown that parental conditions and in utero and perinatal factors may contribute to ASD etiology. More recently, epigenetic changes, including DNA methylation and micro RNA alterations, have been associated with ASD and proposed as potential biomarkers. This review aims to provide a summary of the literature regarding ASD candidate genes, mainly focusing on synapse formation and functionality and relevant epigenetic and environmental aspects acting in concert to determine ASD onset.

Dedicated transcriptomics combined with power analysis lead to functional understanding of genes with weak phenotypic changes in knockout lines.

Systematic knockout studies in mice have shown that a large fraction of the gene replacements show no lethal or other overt phenotypes. This has led to the development of more refined analysis schemes, including physiological, behavioral, developmental and cytological tests. However, transcriptomic analyses have not yet been systematically evaluated for non-lethal knockouts. We conducted a power analysis to determine the experimental conditions under which even small changes in transcript levels can be reliably traced. We have applied this to two gene disruption lines of genes for which no function was known so far. Dedicated phenotyping tests informed by the tissues and stages of highest expression of the two genes show small effects on the tested phenotypes. For the transcriptome analysis of these stages and tissues, we used a prior power analysis to determine the number of biological replicates and the sequencing depth. We find that under these conditions, the knockouts have a significant impact on the transcriptional networks, with thousands of genes showing small transcriptional changes. GO analysis suggests that A930004D18Rik is involved in developmental processes through contributing to protein complexes, and A830005F24Rik in extracellular matrix functions. Subsampling analysis of the data reveals that the increase in the number of biological replicates was more important that increasing the sequencing depth to arrive at these results. Hence, our proof-of-principle experiment suggests that transcriptomic analysis is indeed an option to study gene functions of genes with weak or no traceable phenotypic effects and it provides the boundary conditions under which this is possible.

Genotype-phenotype correlation of HbH disease in northern Iraq.

BACKGROUND: HbH disease results from dysfunction of three, less commonly two, α-globin genes through various combinations of deletion and non-deletion mutations. Characterization of the mutations and the underlying genotypes is fundamental for proper screening and prevention of thalassaemia in any region. The aim of this study was to explore the genetic arrangements of HbH disease and to correlate the genotypes with the clinical phenotypes. METHODS: A total of 44 HbH disease patients were enrolled in this study. They were clinically and haematologically assessed. The patients were tested for 21 common α-globin gene mutations using multiplex PCR and reverse hybridization. According to the genotype, the patients were categorized into two separate sub-groups, deletion and non-deletion types HbH disease. RESULTS: Within the studied HbH disease patients, eight different α-globin gene mutations were detected in nine different genetic arrangements. The --MED and -α3.7 deletions were the two most frequently encountered mutations (37.5 and 35.2% respectively). Patients with deletion genotypes constituted 70.4%. The most common detected genotype was --MED/-α3.7 (59.1%), followed by αpoly-A1α/αpoly-A1α (13.6%). For the first time, coinheritance of two relatively mild mutations (-α3.7/ααAdana) was unpredictably detected in a 1.5 year-old child with Hb of 7.1 g/dL. CONCLUSION: The HbH disease patients' clinical characteristics were variable with no ample difference between the deletion and non-deletion types. These results can be of benefit for the screening and management of thalassaemia in this region.

Association of the MTHFR 677C>T and 1298A>C polymorphisms and male infertility risk: a meta-analysis.

BACKGROUND AND OBJECTIVES: One of the possible male sterility risk factors are polymorphisms of Methylenetetrahydrofolate reductase (MTHFR). However, the epidemiologic investigations described inconsistent results regarding MTHFR polymorphism and the risk of male infertility. For that reason, we carried out a meta-analysis of published case-control studies to re-examine the controversy. METHODS: Electronic searches of Cochrane, EMBASE, Google Scholar, and PubMed were conducted to select eligible studies for this meta-analysis (updated to May 2019). According to our exclusion and inclusion criteria, only high-quality studies that remarked the association between MTHFR polymorphisms and male infertility risk were included. The Crude odds ratio (OR) with a confidence interval of 95% (CI) was used to assess the relationship between MTHFR polymorphism and male infertility risk. RESULTS: Thirty-four case-control studies with 9662 cases and 9154 controls concerning 677C/T polymorphism and 22 case-control studies with 5893 cases and 6303 controls concerning 1298A/C polymorphism were recruited. Both MTHFR polymorphisms had significant associations with male infertility risk (CT + TT vs. CC: OR = 1.37, 95% CI: 1.21-1.55, P = 0.00, I2 = 41.9%); (CC vs. CA + AA: OR = 0.82, 95% CI: 0.52-1.30, P = 0.04, I2 = 50.1%). Further, when stratified by ethnicity, the significant association results were observed in Asians and Caucasians for 677C/T and just Asians for 1298A/C. CONCLUSIONS: Some of MTHFR polymorphisms like MTHFR 677C > T are associated with an elevated male infertility risk. To confirm our conclusion and to provide more accurate and complete gene-environment communication with male infertility risk, more analytical studies are needed.

Genotypic-phenotypic heterogeneity of 뫧-thalassemia and hereditary persistence of fetal hemoglobin (HPFH) in India.

Large deletions in the ß-globin gene cluster lead to increased HbF levels by delaying the γ- to ß-globin switch process. However, these deletions when inherited as a homozygous condition or when co-inherited with ß-thalassemia result in variable clinical phenotypes. Individuals or families with a clinically presenting child, where the parents had HbF levels ≥ 10%, were further screened for the presence of large ß-globin cluster deletions. Six deletions in the ß-globin gene cluster were screened by GAP-PCR, and the uncharacterized deletions were further analyzed by gene dosage or by multiplex ligation-dependent probe amplification (MLPA). Among 192 individuals suspected for the inheritance of large deletions, 138 were heterozygous for large deletions, 45 were compound heterozygous of a large ß-globin cluster deletion and ß-thalassemia, and 9 were found to be homozygous for deletions. Among the heterozygotes, the Asian Indian inversion-deletion was found to be the most common deletion (39.9%), followed by the HPFH-3 deletion (30.0%). Other deletions 49.3 kb, δß-thalassemia (21.2%), and 32.6 kb deletion (4.4%) were also found to be prevalent in our population. Patients compound heterozygous or homozygous for HPFH-3 and 32.6 kb deletions showed a milder clinical presentation, as compared with the patients compound heterozygous or homozygous for the Asian Indian inversion-deletion and 49.3 kb δß-thalassemia. This comprehensive study highlights the mutation spectrum of large ß-globin cluster deletions and the clinical heterogeneity in the patients homozygous or compound heterozygous with ß-thalassemia, thus asserting the need for molecular characterization of these deletions.

Association analysis in a Latin American population revealed ethnic differences in rheumatoid arthritis-associated SNPs in Caucasian and Asian populations.

Large genome-wide association studies (GWAS) have increased our knowledge of the genetic risk factors of rheumatoid arthritis (RA). However, little is known about genetic susceptibility in populations with a large admixture of Amerindian ancestry. The aim of the present study was to test the generalizability of previously reported RA loci in a Latin American (LA) population with admixed ancestry. We selected 128 single nucleotide polymorphisms (SNPs) in linkage equilibrium, with high association to RA in multiple populations of non-Amerindian origin. Genotyping of 118 SNPs was performed in 313 RA patients/487 healthy control subjects by mid-density arrays of polymerase chain reaction (PCR). Some of the identified associations were validated in an additional cohort (250 cases/290 controls). One marker, the SNP rs2451258, located upstream of T Cell Activation RhoGTPase Activating Protein (TAGAP) gene, showed significant association with RA (p = 5 × 10-3), whereas 18 markers exhibited suggestive associations (p < 0.05). Haplotype testing showed association of some groups of adjacent SNPs around the signal transducer and activator of transcription 4 (STAT4) gene (p = 9.82 × 10-3 to 2.04 × 10-3) with RA. Our major finding was little replication of previously reported genetic associations with RA. These results suggest that performing GWAS and admixture mapping in LA populations has the potential to reveal novel loci associated with RA. This in turn might help to gain insight into the 'pathogenomics' of this disease and to explore trans-population differences for RA in general.

Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model.

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.