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Mango fruit undergoes various physiochemical changes after its harvest that affect consumer acceptability. The present research work was carried out to evaluate the preservation effect of sodium nitroprusside (SNP) treatments on the postharvest quality of mango cv. Langra fruits at 12 ± 1°C for 35 days. The results revealed that SNP 1.0 mM treated fruits exhibited reduced weight loss (6.94%), decay incidence (8.05%), and activities of cell wall degrading enzymes like polygalacturonase and pectin methyl esterase in comparison to uncoated fruits on the last day of storage. Furthermore, the treatment maintained higher fruit firmness (56.77 N) along with fruit quality parameters like soluble solid content (16.24%), titratable acidity (0.20%), starch (4.99%), and, total flavonoid content (12.92 mg/100 g). Overall, SNP 1.0 mM combined with low-temperature storage was proved to be the most effective in extending the shelf life and preserving the quality characteristics of mango fruits. [ABSTRACT FROM AUTHOR]

Background: The additive model of inheritance assumes that heterozygotes (Aa) are exactly intermediate in respect to homozygotes (AA and aa). While this model is commonly used in single-locus genetic association studies, significant deviations from additivity are well-documented and contribute to phenotypic variance across many traits and systems. This assumption can introduce type I and type II errors by overestimating or underestimating the effects of variants that deviate from additivity. Alternative genotype encoding strategies have been explored to account for different inheritance patterns, but they often incur significant computational or methodological costs. To address these challenges, we introduce PAGER (Phenotype Adjusted Genotype Encoding and Ranking), an efficient pre-processing method that encodes each genetic variant based on normalized mean phenotypic differences between diallelic genotype classes (AA, Aa, and aa). This approach more accurately reflects each variant's true inheritance model, improving model precision while minimizing the costs associated with alternative encoding strategies. Results: Through extensive benchmarking on SNPs simulated with both binary and continuous phenotypes, we demonstrate that PAGER accurately represents various inheritance patterns (including additive, dominant, recessive, and heterosis), achieves levels of statistical power that meet or exceed other encoding strategies, and attains computation speeds up to 55 times faster than a similar method, EDGE. We also apply PAGER to publicly available real-world data and identify a novel, relevant putative QTL associated with body mass index in rats (Rattus norvegicus) that is not detected with the additive model. Conclusions: Overall, we show that PAGER is an efficient genotype encoding approach that can uncover sources of missing heritability and reveal novel insights in the study of complex traits while incurring minimal costs. [ABSTRACT FROM AUTHOR]

Gene-based burden tests are a popular and powerful approach for analysis of exome-wide association studies. These approaches combine sets of variants within a gene into a single burden score that is then tested for association. Typically, a range of burden scores are calculated and tested across a range of annotation classes and frequency bins. Correlation between these tests can complicate the multiple testing correction and hamper interpretation of the results. We introduce a method called the sparse burden association test (SBAT) that tests the joint set of burden scores under the assumption that causal burden scores act in the same effect direction. The method simultaneously assesses the significance of the model fit and selects the set of burden scores that best explain the association at the same time. Using simulated data, we show that the method is well calibrated and highlight scenarios where the test outperforms existing gene-based tests. We apply the method to 73 quantitative traits from the UK Biobank, showing that SBAT is a valuable additional gene-based test when combined with other existing approaches. This test is implemented in the REGENIE software. Gene-based burden tests are commonly used in exome-wide association studies. We introduce SBAT (sparse burden association test), which jointly models a set of burden scores under the assumption that the causal burden scores act in the same effect direction. We apply SBAT to 73 quantitative traits in the UK Biobank. [ABSTRACT FROM AUTHOR]

Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. We introduce a unique evaluation metric that is based on the distribution of the values, rather than the magnitude of the values, to identify analytes that are associated with a subset of the diseased cases, thereby revealing potential biomarkers for subtypes. Our approach, Bimodality Coefficient Difference (BCD), computes the difference between the degrees of bimodality for the cases and controls. We demonstrate the effectiveness of our approach with large-scale synthetic data trials containing nearly perfect subtypes. In order to reveal novel AD biomarkers for heterogeneous subtypes, we applied BCD to gene expression data for 8,650 genes for 176 AD cases and 187 controls. Our results confirm the utility of BCD for identifying subtypes of heterogeneous diseases. [ABSTRACT FROM AUTHOR]

We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of 'tallness' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer is commonly diagnosed symptomatically at an advanced stage. Better survival for early disease suggests improving diagnostic pathways may increase survival. This study examines literature assessing diagnostic intervals and their association with clinical and psychological outcomes. Methods: Medline, EMBASE, and EmCare databases were searched for studies including quantitative measures of at least one interval, published between January 1, 2000 and August 9, 2022. Interval measures and associations (interval, outcomes, analytic strategy) were synthesized. Risk of bias of association studies was assessed using the Aarhus Checklist and ROBINS-E tool. Results: In total, 65 papers (20 association studies) were included and 26 unique intervals were identified. Interval estimates varied widely and were impacted by summary statistic used (mean or median) and group focused on. Of Aarhus-defined intervals, patient (symptom to presentation, n = 23; range [median]: 7–168 days) and diagnostic (presentation to diagnosis, n = 22; range [median]: 7–270 days) were most common. Nineteen association studies examined survival or stage outcomes with most, including five low risk-of-bias studies, finding no association. Conclusions: Studies reporting intervals for ovarian cancer diagnosis are limited by inconsistent definitions and reporting. Greater utilization of the Aarhus statement to define intervals and appropriate analytic methods is needed to strengthen findings from future studies. [ABSTRACT FROM AUTHOR]

Abstract Background The additive model of inheritance assumes that heterozygotes (Aa) are exactly intermediate in respect to homozygotes (AA and aa). While this model is commonly used in single-locus genetic association studies, significant deviations from additivity are well-documented and contribute to phenotypic variance across many traits and systems. This assumption can introduce type I and type II errors by overestimating or underestimating the effects of variants that deviate from additivity. Alternative genotype encoding strategies have been explored to account for different inheritance patterns, but they often incur significant computational or methodological costs. To address these challenges, we introduce PAGER (Phenotype Adjusted Genotype Encoding and Ranking), an efficient pre-processing method that encodes each genetic variant based on normalized mean phenotypic differences between diallelic genotype classes (AA, Aa, and aa). This approach more accurately reflects each variant’s true inheritance model, improving model precision while minimizing the costs associated with alternative encoding strategies. Results Through extensive benchmarking on SNPs simulated with both binary and continuous phenotypes, we demonstrate that PAGER accurately represents various inheritance patterns (including additive, dominant, recessive, and heterosis), achieves levels of statistical power that meet or exceed other encoding strategies, and attains computation speeds up to 55 times faster than a similar method, EDGE. We also apply PAGER to publicly available real-world data and identify a novel, relevant putative QTL associated with body mass index in rats (Rattus norvegicus) that is not detected with the additive model. Conclusions Overall, we show that PAGER is an efficient genotype encoding approach that can uncover sources of missing heritability and reveal novel insights in the study of complex traits while incurring minimal costs.

Purpose of Review: Spontaneous coronary artery dissection (SCAD) has been increasingly recognized as a significant cause of acute myocardial infarction (AMI) in young and middle-aged women and arises through mechanisms independent of atherosclerosis. SCAD has a multifactorial etiology that includes environmental, individual, and genetic factors distinct from those typically associated with coronary artery disease. Here, we summarize the current understanding of the genetic factors contributing to the development of SCAD and highlight those factors which differentiate SCAD from atherosclerotic coronary artery disease. Recent Findings: Recent studies have revealed several associated variants with varying effect sizes for SCAD, giving rise to a complex genetic architecture. Associated genes highlight an important role for arterial cells and their extracellular matrix in the pathogenesis of SCAD, as well as notable genetic overlap between SCAD and other systemic arteriopathies such as fibromuscular dysplasia and vascular connective tissue diseases. Further investigation of individual variants (including in the associated gene PHACTR1) along with polygenic score analysis have demonstrated an inverse genetic relationship between SCAD and atherosclerosis as distinct causes of AMI. Summary: SCAD represents an increasingly recognized cause of AMI with opposing clinical and genetic risk factors from that of AMI due to atherosclerosis, and it is often associated with complex underlying genetic conditions. Genetic study of SCAD on a larger scale and with more diverse cohorts will not only further our evolving understanding of a newly defined genetic spectrum for AMI, but it will also inform the clinical utility of integrating genetic testing in AMI prevention and management moving forward. [ABSTRACT FROM AUTHOR]

Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High-resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, elucidating mechanisms that may underlie genetic susceptibility to heart failure in human populations.

Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. We introduce a unique evaluation metric that is based on the distribution of the values, rather than the magnitude of the values, to identify analytes that are associated with a subset of the diseased cases, thereby revealing potential biomarkers for subtypes. Our approach, Bimodality Coefficient Difference (BCD), computes the difference between the degrees of bimodality for the cases and controls. We demonstrate the effectiveness of our approach with large-scale synthetic data trials containing nearly perfect subtypes. In order to reveal novel AD biomarkers for heterogeneous subtypes, we applied BCD to gene expression data for 8,650 genes for 176 AD cases and 187 controls. Our results confirm the utility of BCD for identifying subtypes of heterogeneous diseases.

Jaina Razbek,1 Mayisha Daken,2 Yanggui Chen,3 Li Ma,3 Yan Zhang,1 Wanting Xu,1 Baofeng Wen,1 Junan Wang,1 Xiaomin Wang,1 Mingqin Cao1 1Department of Epidemiology and Health Statistics, College of Public Health, Xinjiang Medical University, Urumqi, 830011, People’s Republic of China; 2Department of Epidemic Prevention, Karamay Centre for Disease Control and Prevention, Karamay, 834000, People’s Republic of China; 3Department of Prevention and Control of Tuberculosis, Urumqi Centre for Disease Control and Prevention, Urumqi, 830011, People’s Republic of ChinaCorrespondence: Mingqin Cao, Tel +86-13319912419, Email cmq66@126.comPurpose: Early recognition and treatment of latent tuberculosis infection(LTBI) is key to tuberculosis(TB) prevention. However, the emergence of LTBI is influenced by a combination of factors, of which the role of individual immune cytokines remains controversial. The aim of this study is to explore the influencing factors of LTBI and their effects with cytokines on LTBI.Patients and Methods: Close contacts of tuberculosis in Urumqi City from 2021 to 2022 were selected for the study to conduct a field survey. It used logistic regression model to analyse the influencing factors of LTBI, principal component analysis to extract a composite indicators of cytokines, and structural equation modelling to explore the direct and indirect effects of cytokines and influencing factors on LTBI.Results: LTBI infection rate of 33.3% among 288 TB close contacts. A multifactorial Logistic model showed that factors influencing LTBI included education, daily contact hours, eating animal liver, and drinking coffee (P< 0.05); After controlling for confounding factors and extracting composite indicators of cytokines using principal component analysis, CXCL5 and IFN-γ is a protective factor for LTBI(OR=0.572, P=0.047), IL-10 and TNF-α is a risk factor for LTBI(OR=2.119, P=0.010); Structural equation modelling shows drinking coffee, eating animal liver, daily contact hours, and IL-10 and TNF-α had direct effects on LTBI and educations had indirect effects on LTBI(P< 0.05).Conclusion: IL-10 and TNF-α are involved in the immune response and are directly related to LTBI. By monitoring the cytokine levels of TB close contacts and paying attention to their dietary habits and exposure, we can detect and intervene in LTBI at an early stage and control their progression to TB.Keywords: close contacts, latent tuberculosis infection, cytokines, structural equation modelling, association studies

This study examined genetic diversity and growth performance of thirty Terminalia chebula genotypes from various regions of India. Seedlings were evaluated for growth attributes over 180 days after transplanting (DAT). Significant variations were observed across accessions for traits like shoot length, root length, collar diameter and volume. MHTC 04 showed superior performance, with maximum shoot length (36.23 cm) and root length (38.47 cm) at 180 DAT. KETC 05 exhibited the highest collar diameter (1.08 cm) and volume index (39.35) at 180 DAT. The genetic analysis revealed low to moderate PCV and GCV for most traits. Volume index showed the highest PCV (47.31 %) and GCV (47.23 %). High heritability (>95 %) was observed for several traits viz., root length, collar diameter and volume index. Path coefficient analysis identified collar diameter and shoot fresh weight as having the strongest positive direct effects on quality index. Cluster analysis grouped 30 genotypes into 10 clusters with cluster II being the largest (8 progenies). Quality index contributed the maximum (59.30 %) towards divergence followed by shoot dry weight (13.94 %) and volume index (8.28 %). This study identified promising genotypes and important traits for T. chebula improvement programmes, highlighting the potential for selective breeding to enhance the seedling quality and growth performance.

Sickle cell anemia (SCA) is the most common cause of stroke in children. As it is a rare disease, studies investigating the association with complications like stroke in SCD have small sample sizes. Here, we performed a systematic review and meta-analysis of the studies exploring an association of genetic variants with stroke to get a better indication of their association with stroke. PubMed and Google Scholar were searched to identify studies that had performed an association analysis of genetic variants for the risk of stroke in SCA patients. After screening of eligible studies, summary statistics of association analysis with stroke and other general information were extracted. Meta-analysis was performed using the fixed effect method on the tool METAL and forest plots were plotted using the R program. The random effect model was performed as a sensitivity analysis for loci where significant heterogeneity was observed. 407 studies were identified using the search term and after screening 37 studies that cumulatively analyzed 11,373 SCA patients were included. These 37 studies included a total of 2,222 SCA patients with stroke, predominantly included individuals of African ancestry (N = 16). Three of these studies performed whole exome sequencing while 35 performed single nucleotide-based genotyping. Though the studies reported association with 132 loci, meta-analyses could be performed only for 12 loci that had data from two or more studies. After meta-analysis we observed that four loci were significantly associated with risk for stroke: -α3.7 kb Alpha-thalassemia deletion (P = 0.00000027), rs489347-TEK (P = 0.00081), rs2238432-ADCY9 (P = 0.00085), rs11853426-ANXA2 (P = 0.0034), and rs1800629-TNF (P = 0.0003396). Ethnic representation of regions with a high prevalence of SCD like the Mediterranean basin and India needs to be improved for genetic studies on associated complications like stroke. Larger genome-wide collaborative studies on SCD and associated complications including stroke need to be performed. [ABSTRACT FROM AUTHOR]

Population genetic studies have long been an essential part of dissecting important traits of crops. The advent of genomic resources has significantly enhanced the efficiency of genetic mapping studies. Concurrently, the integration of multi-omics approaches affords a comprehensive perspective of plant systems, enabling researchers to investigate the correlations between genetic variations and horticultural traits across multiple dimensions. This review summarized studies combining forward genetics with multi-omics resources to analyze the genetic basis of important traits in horticultural crops. The strategies for the effective application of these integrated approaches in the genetic dissection of ornamental plant populations was discussed. The challenges in processing large dataset were acknowledged and promising prospects of artificial intelligence (AI) when addressing such issues were outlined. The review aims to provide guidance for population genetic research of horticultural crops and ornamental plants in the multi-omics era.

Abstract The cold stress susceptibility of tomato (Solanum lycopersicum) curtails its cultivation, with significant impact in temperate regions and on cropping seasons. To unravel genomic regions responsible for cold stress resilience, a diverse set of fifty genotypes encompassing cultivated, wild species, and landraces were genotyped using genotyping-by-sequencing. Over two years and six trials employing both early and late sowing, these lines were evaluated. Illumina-based next-generation sequencing produced up to 3 million reads per sample from individually sequenced library pools. The Tassel pipeline yielded 10,802 variants, subsequently filtered to 3,854 SNPs for genome-wide association analysis (GWAS). Employing clustering methods (population structure) via TASSEL, SNPhylo, and Kinship matrix, the fifty genotypes clustered into four distinct gene pools. The GWAS for cold tolerance in tomato integrated key traits including yield. Using six independent phenotypic datasets representing various environments, the study identified 4,517 significant marker-trait associations for cold tolerance traits. Notably, pivotal variations (> 10%) in cold stress tolerance, particularly proline content, were linked to marker-trait associations. Additionally, 5,727 significant marker-trait associations for yield and yield-related traits were unveiled, shedding light on fruit yield and directly associated attributes. The investigation pinpointed 685 candidate genes across all examined traits, including 60 genes associated with biological processes within these genomic regions. Remarkably, 7 out of the 60 genes were directly linked to abiotic stress tolerance, functioning as stress-responsive genes either directly or indirectly. The identified genes, particularly those associated with stress response, could hold the key to enhancing cold tolerance and overall crop productivity in tomato cultivation.

Abstract Objectives Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena. Data description Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants’ (whose personal data are protected for ethical reasons) and volunteers’ observation transcripts, volunteers’ audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University’s Agricultural Engineering and Agronomy Research Farms.

The cold stress susceptibility of tomato (Solanum lycopersicum) curtails its cultivation, with significant impact in temperate regions and on cropping seasons. To unravel genomic regions responsible for cold stress resilience, a diverse set of fifty genotypes encompassing cultivated, wild species, and landraces were genotyped using genotyping-by-sequencing. Over two years and six trials employing both early and late sowing, these lines were evaluated. Illumina-based next-generation sequencing produced up to 3 million reads per sample from individually sequenced library pools. The Tassel pipeline yielded 10,802 variants, subsequently filtered to 3,854 SNPs for genome-wide association analysis (GWAS). Employing clustering methods (population structure) via TASSEL, SNPhylo, and Kinship matrix, the fifty genotypes clustered into four distinct gene pools. The GWAS for cold tolerance in tomato integrated key traits including yield. Using six independent phenotypic datasets representing various environments, the study identified 4,517 significant marker-trait associations for cold tolerance traits. Notably, pivotal variations (> 10%) in cold stress tolerance, particularly proline content, were linked to marker-trait associations. Additionally, 5,727 significant marker-trait associations for yield and yield-related traits were unveiled, shedding light on fruit yield and directly associated attributes. The investigation pinpointed 685 candidate genes across all examined traits, including 60 genes associated with biological processes within these genomic regions. Remarkably, 7 out of the 60 genes were directly linked to abiotic stress tolerance, functioning as stress-responsive genes either directly or indirectly. The identified genes, particularly those associated with stress response, could hold the key to enhancing cold tolerance and overall crop productivity in tomato cultivation. [ABSTRACT FROM AUTHOR]

Objectives: Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena. Data description: Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants' (whose personal data are protected for ethical reasons) and volunteers' observation transcripts, volunteers' audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University's Agricultural Engineering and Agronomy Research Farms. [ABSTRACT FROM AUTHOR]

Purpose of Review: Spontaneous coronary artery dissection (SCAD) is a significant cause of acute myocardial infarction that is increasingly recognized in young and middle-aged women. The etiology of SCAD is likely multifactorial and may include the interaction of environmental and individual factors. Here, we summarize the current understanding of the genetic factors contributing to the development of SCAD. Recent Findings: The molecular findings underlying SCAD have been demonstrated to include a combination of rare DNA sequence variants with large effects, common variants contributing to a complex genetic architecture, and variants with intermediate impact. The genes associated with SCAD highlight the role of arterial cells and their extracellular matrix in the pathogenesis of the disease and shed light on the relationship between SCAD and other disorders, including fibromuscular dysplasia and connective tissue diseases. Summary: While up to 10% of affected individuals may harbor a rare variant with large effect, SCAD most often presents as a complex genetic condition. Analyses of larger and more diverse cohorts will continue to improve our understanding of risk susceptibility loci and will also enable consideration of the clinical utility of genetic testing strategies in the management of SCAD. [ABSTRACT FROM AUTHOR]

Tea is one of the most popular non-alcoholic beverages in the world, and free amino acids, especially theanine, make a major contribution to the umami taste of tea. However, the genetic basis of the variation in amino acid content in tea plants remains largely unknown. Here, we measured the free amino acid content in fresh leaves of 174 tea accessions over two years using a targeted metabolomics approach and obtained genotype data via RNA sequencing. Genome-wide association studies were conducted to investigate loci affecting the content of free amino acids. A total of 69 quantitative trait loci (–log10(P-value)>5) were identified. Functional annotation revealed that branched-chain amino acid aminotransferase, glutamine synthetase, nitrate transporter, and glutamate decarboxylase might be important for amino acid metabolism. Two significant loci, glutamine synthetase (Glu1, P=3.71×10−4; Arg1, P=4.61×10−5) and branched-chain amino acid aminotransferase (Val1, P=4.67×10−5; I_Leu1, P=3.56×10−6), were identified, respectively. Based on the genotyping result, two alleles of CsGS (CsGS-L and CsGS-H) and CsBCAT (CsBCAT-L and CsBCAT-H) were selected to perform function verification. Overexpression of CsGS-L and CsGS-H enhanced the contents of glutamate and arginine in transgenic plants, and overexpression of CsBCAT-L and CsBCAT-H promoted the accumulation of valine, isoleucine and leucine. Enzyme activity assay uncovered that SNP1054 is important for CsGS catalyzing glutamate into glutamine. Furthermore, CsGS-L and CsGS-H differentially regulated the accumulation of glutamine, and CsBCAT-L and CsBCAT-H differentially regulated the accumulation of branched-chain amino acids. In summary, the findings in our study would provide new insights into the genetic basis of amino acids contents variation in tea plants and facilitate the identification of elite genes to enhance amino acids content.

Abstract Background Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

Abstract Background Cervical cancer is the fourth most common cancer among women worldwide. Genome-wide association studies have revealed multiple susceptible genes and their polymorphisms for cervical cancer risk. Therefore, we aimed to investigate the correlation between single nucleotide polymorphisms (SNPs) of the CD40 gene and susceptibility to cervical squamous cell carcinoma (CSCC) in a population from the northeastern Han Chinese population. Methods The three SNPs (rs1800686, rs3765459, and rs4810485) of the CD40 gene were analyzed by multiplex polymerase chain reaction (PCR) combined with next-generation sequencing methods in 421 patients with CSCC, 594 patients with high-grade squamous intraepithelial lesions (HSIL), and 504 healthy females. Multivariate logistic regression analysis was used to analyze the potential relationship between CD40 gene polymorphisms and CSCC, or HSIL. Results Our research results showed the AA genotype of rs1800686 had a protective effect on CSCC in comparison to the GG genotype and AG+GG genotypes (AA vs. GG: p = 0.0389 and AA vs. AG+GG: p = 0.0280, respectively). After FDR correction, the results were still statistically significant (p = 0.0389 and p = 0.0389, respectively). Similarly, rs3765459 showed a reduced risk association for CSCC in the codominant and recessive models (AA vs. GG: p = 0.0286 and AA vs. AG+GG: p = 0.0222, respectively). Significant differences remained after FDR correction (p = 0.0286 and p = 0.0286, respectively). However, these differences were no longer significant after the Bonferroni correction. In addition, the genotypes for the rs4810485 polymorphisms were associated with parity of the patients with CSCC. The genotypes for the rs3765459 polymorphisms were significantly correlated with the D-dimer of the patients with CSCC. The 3 SNPs genotypes of the CD40 gene were closely related to the squamous cell carcinoma antigen (SCC) of the patients with HSIL. Conclusions The CD40 gene may play a role in the occurrence and development of CSCC.

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer is the fourth most common cancer among women worldwide. Genome-wide association studies have revealed multiple susceptible genes and their polymorphisms for cervical cancer risk. Therefore, we aimed to investigate the correlation between single nucleotide polymorphisms (SNPs) of the CD40 gene and susceptibility to cervical squamous cell carcinoma (CSCC) in a population from the northeastern Han Chinese population. Methods: The three SNPs (rs1800686, rs3765459, and rs4810485) of the CD40 gene were analyzed by multiplex polymerase chain reaction (PCR) combined with next-generation sequencing methods in 421 patients with CSCC, 594 patients with high-grade squamous intraepithelial lesions (HSIL), and 504 healthy females. Multivariate logistic regression analysis was used to analyze the potential relationship between CD40 gene polymorphisms and CSCC, or HSIL. Results: Our research results showed the AA genotype of rs1800686 had a protective effect on CSCC in comparison to the GG genotype and AG+GG genotypes (AA vs. GG: p = 0.0389 and AA vs. AG+GG: p = 0.0280, respectively). After FDR correction, the results were still statistically significant (p = 0.0389 and p = 0.0389, respectively). Similarly, rs3765459 showed a reduced risk association for CSCC in the codominant and recessive models (AA vs. GG: p = 0.0286 and AA vs. AG+GG: p = 0.0222, respectively). Significant differences remained after FDR correction (p = 0.0286 and p = 0.0286, respectively). However, these differences were no longer significant after the Bonferroni correction. In addition, the genotypes for the rs4810485 polymorphisms were associated with parity of the patients with CSCC. The genotypes for the rs3765459 polymorphisms were significantly correlated with the D-dimer of the patients with CSCC. The 3 SNPs genotypes of the CD40 gene were closely related to the squamous cell carcinoma antigen (SCC) of the patients with HSIL. Conclusions: The CD40 gene may play a role in the occurrence and development of CSCC. [ABSTRACT FROM AUTHOR]

Background: Laryngeal squamous cell carcinoma (LSCC) is the major pathological subtype of laryngeal cancer. It has been shown that alterations of the expression of non-classical human leukocyte antigens (HLA) and the chain-related MIC molecules by malignant cells can lead to escape from the immune system control and certain allele variants may participate in immune editing and therefore be associated with modulation of cancer risk. The aim of the present study was to investigate the role of non-classical HLA class Ib and chain-related MIC polymorphisms, determined at the allelic level by next-generation sequencing (NGS), in patients from the Bulgarian population, diagnosed with LSCC. Materials and methods: In the present study DNA samples from 48 patients with LSCC were used. Data was compared to 63 healthy controls analysed in previous studies. HLA genotyping was performed by using the AlloSeq Tx17 early pooling protocol and the library preparation AlloSeq Tx17 kit (CareDx). Sequencing was performed on MiniSeq sequencing platform (Illumina) and HLA genotypes were assigned with the AlloSeq Assign analysis software v1.0.3 (CareDx) and the IPD-IMGT/HLA database 3.45.1.2. Results: The HLA disease association tests revealed a statistically significant predisposing association of HLA-F*01:01:02 (Pc = 0.0103, OR = 24.0194) with LSCC, while HLA-F*01:01:01 (Pc = 8.21e-04, OR = 0.0485) has a possible protective association. Additionally we observed several haplotypes with statistically significant protective and predisposing associations. The strongest association was observed for F*01:01:01-H*01:01:01 (P = 0.0054, haplotype score=-2.7801). Conclusion: Our preliminary study suggests the involvement of HLA class Ib in cancer development and the possible role of the shown alleles as biomarkers of LSCC. [ABSTRACT FROM AUTHOR]

Abstract Background Clinical trials have shown zoledronic acid as a potent bisphosphonate in preventing bone loss, but with varying potency between patients. Human osteoclasts ex vivo reportedly displayed a variable sensitivity to zoledronic acid > 200-fold, determined by the half-maximal inhibitory concentration (IC50), with cigarette smoking as one of the reported contributors to this variation. To reveal the molecular basis of the smoking-mediated variation on treatment sensitivity, we performed a DNA methylome profiling on whole blood cells from 34 healthy female blood donors. Multiple regression models were fitted to associate DNA methylation with ex vivo determined IC50 values, smoking, and their interaction adjusting for age and cell compositions. Results We identified 59 CpGs displaying genome-wide significance (p

The dopaminergic pathways control neural signals that modulate mood and behaviour along and have a vital role in the aetiology of major depression (MDD), schizophrenia (SHZ) and bipolar disorder (BD). Genome-wide association studies (GWAS) have reported several dopaminergic and cognitive pathway genes association with these disorders however, no such comprehensive data was available regarding the Pakistani population. Therefore, the present study was conducted to analyse the 11 genetic variants of dopaminergic and cognitive system genes in MDD, SHZ, and BD in the Pakistani population. A total of 1237 subjects [MDD n = 479; BD n = 222; SHZ n = 146; and controls n = 390], were screened for 11 genetic variants through polymerase chain reaction (PCR) techniques. Univariant followed by multivariant logistic regression analysis was applied to determine the genetic association. Significant risk associations were observed for rs4532 and rs1799732 with MDD; and rs1006737 and rs2238056 with BD. However, after applying multiple test corrections rs4532 and rs1799732 association did not remain significant for MDD. Moreover, a protective association was found for three variants; DRD4-120bp, rs10033951 and rs2388334 in the current cohort. In conclusion, the current study revealed the risk association of single nucleotide polymorphisms (SNPs) rs1006737 and rs2238056 with BD and the protective effect of the DRD4-120bp variant in MDD and BD, of rs2388334 in BD and of rs10033951 in MDD, BD, and SHZ in the current Pakistani cohort. Thus, the study is valuable in understanding the genetic basis of MDD, BD and SHZ in the Pakistani population, which may pave the way for future functional studies. [ABSTRACT FROM AUTHOR]

Millets are known for their resilience and nutritional benefits and hence believed to have a promising role in ensuring food and nutritional security under changing climatic conditions. Research on millets has intensified in recent years, especially in dissecting the genetic components of yield, stress tolerance and nutritional quality traits. Recent advances in next‐generation sequencing, bioinformatics and associated statistical procedures for genome‐wide association studies (GWAS) have provided wide opportunities to resolve the genetic complexity of polygenic traits by measuring historical and evolutionary recombination events in the natural population(s). During the past decade, GWAS has been successfully employed to identify key genes controlling growth, development, stress tolerance, nutrient use efficiency and nutritional quality traits in sorghum, pearl millet, foxtail millet and finger millet. However, progress in other minor millets is still in its infancy. Genetic dissection of these complex traits in millets may pave the way for genetic alteration of climate resilience, photosynthesis and nutrient accumulation in rice and wheat. In this review, progress in GWAS analysis in detecting QTLs underlying complex traits in sorghum and other millets is highlighted. [ABSTRACT FROM AUTHOR]

Introduction: Substantial links between autoimmune diseases have been shown by an increasing number of studies, and one hypothesis for this comorbidity is that there is a common genetic cause. Methods: In this paper, a large-scale cross-trait Genome-wide Association Studies (GWAS) was conducted to investigate the genetic overlap among rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease and type 1 diabetes. Results and discussion: Through the local genetic correlation analysis, 2 regions with locally significant genetic associations between rheumatoid arthritis and multiple sclerosis, and 4 regions with locally significant genetic associations between rheumatoid arthritis and type 1 diabetes were discovered. By cross-trait meta-analysis, 58 independent loci associated with rheumatoid arthritis and multiple sclerosis, 86 independent loci associated with rheumatoid arthritis and inflammatory bowel disease, and 107 independent loci associatedwith rheumatoid arthritis and type 1 diabeteswere identified with genome-wide significance. In addition, 82 common risk genes were found through genetic identification. Based on gene set enrichment analysis, it was found that shared genes are enriched in exposed dermal system, calf, musculoskeletal, subcutaneous fat, thyroid and other tissues, and are also significantly enriched in 35 biological pathways. To verify the association between diseases, Mendelian randomized analysis was performed, which shows possible causal associations between rheumatoid arthritis and multiple sclerosis, and between rheumatoid arthritis and type 1 diabetes. The common genetic structure of rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease and type 1 diabetes was explored by these studies, and it is believed that this important discovery will lead to new ideas for clinical treatment. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains the leading cause of death due to a single bacterial agent, with approximately 10.6 million people developing active disease and 1.6 million deaths reported globally in 2021. After exposure, some, but not all individuals, will become infected with the bacillus. However, only a small fraction (approximately 5 to 15%) of these individuals will progress to clinical disease, while in the remainder, infection is seemingly contained, and no signs of clinical disease are shown. Numerous observations have advocated for the role of host genetics in the display of these inter-individual variabilities in infection and disease phenotypes. In this review, we will provide an overview of the approaches, findings and limitations of the very first studies investigating TB genetic susceptibility to more recent studies. Lastly, we highlight several approaches, namely, linkage analyses and association studies, proposed to discover genetic markers associated with TB susceptibility. This review also explored the concept of polygenic risk scores (PRS) for prediction of tuberculosis susceptibility. The identification of host genetic factors influencing TB susceptibility/resistance is paramount to not only better understand the physiopathology of the disease but also explore more effective approaches for the development of both optimal preventive measures (i.e. better vaccines) and treatments of TB disease. [ABSTRACT FROM AUTHOR]

Summary: Obstructive sleep apnea is a common disorder that leads to sleep fragmentation and is potentially bidirectionally related to a variety of comorbidities, including an increased risk of heart failure and stroke. It is often considered a consequence of anatomical abnormalities, especially in the head and neck, but its pathophysiology is likely to be multifactorial in origin. With geometric morphometrics, and a large sample of adults from the Study for Health in Pomerania, we explore the association of craniofacial morphology to the apnea–hypopnea index used as an estimate of obstructive sleep apnea severity. We show that craniofacial size and asymmetry, an aspect of morphological variation seldom analysed in obstructive sleep apnea research, are both uncorrelated to apnea–hypopnea index. In contrast, as in previous analyses, we find evidence that brachycephaly and larger nasal proportions might be associated to obstructive sleep apnea severity. However, this correlational signal is weak and completely disappears when age‐related shape variation is statistically controlled for. Our findings suggest that previous work might need to be re‐evaluated, and urge researchers to take into account the role of confounders to avoid potentially spurious findings in association studies. [ABSTRACT FROM AUTHOR]

The present investigation on correlation and path analysis studies helps to study the relationship among the yield and yield contributing traits along with the quality parameters. The observations were taken in the F1 population of 35 hybrids along with lines (7 lines) and testers (5 testers) for the traits viz. plant height (cm), number of sympodial branches per plant, the number of bolls per plant, boll weight (g), lint index (g), single cotton yield per plant (g), ginning outturn (%), upper half mean length (cm), elongation percent (%) and micronaire value (µg per inch). The results revealed that seed cotton yield per plant had strong and positive association with the traits namely number of sympodial branches per plant (0.646), number of bolls (0.633) and boll weight (0.652). Path analysis study revealed that number of sympodial branches per plant (1.5396), boll weight (0.6285), lint index (1.3526) and upper half mean length (0.3392) had high direct positive effects on single plant yield and indirect very high positive effects through the traits viz., number of sympodial branches per plant via., number of bolls per plant (1.5125) and boll weight (1.0121). Hence selection based on these traits that are positively associated and direct positive effect with the yield would produce the best outcome in the genotypes in further breeding programmes. [ABSTRACT FROM AUTHOR]

While the human microbiome's structure and function have been extensively studied, its within-species genetic diversity is less well understood. However, genetic mutations in the microbiome can confer biomedically relevant traits, such as the ability to extract nutrients from food, metabolize drugs, evade antibiotics, and communicate with the host immune system. The population genetic processes by which these traits evolve are complex, in part due to interacting ecological and evolutionary forces in the microbiome. Advances in metagenomic sequencing, coupled with bioinformatics tools and population genetic models, facilitate quantification of microbiome genetic variation and inferences about how this diversity arises, evolves, and correlates with traits of both microbes and hosts. In this review, we explore the population genetic forces (mutation, recombination, drift, and selection) that shape microbiome genetic diversity within and between hosts, as well as efforts towards predictive models that leverage microbiome genetics.

Both genome-wide association study and next-generation sequencing data analyses are widely employed to identify disease susceptible common and/or rare genetic variants. Rare variants generally have large effects though they are hard to detect due to their low frequencies. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some adhoc assumptions (e.g., ignoring dependence between rare variants). In this study, we analytically derived optimal weights for both common and rare variants and proposed a general and novel approach to test association between an optimally weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. Additionally, we applied G-TOW and other competitive methods to test disease-associated genes in real data of schizophrenia. The G-TOW has successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic and the sequence kernel association test. Simulation study and real data analysis indicate that G-TOW is a powerful test.

Abstract Background Tooth agenesis (TA) is the developmental absence of one or more teeth and is the most common craniofacial disorder in humans. Maxillary lateral incisor agenesis (MLIA) is a specific subtype of TA and can have esthetic, functional, and psychosocial implications for patients. The aim of this study was to evaluate the prevalence of MLIA amongst patients with non-syndromic tooth agenesis, as well as its association with other dental anomalies. Materials and methods The dental records of 240 patients with non-syndromic congenitally missing teeth treated at the University of Alabama at Birmingham Department of Orthodontics were reviewed. Dolphin Imaging software was used to identify missing teeth, microdonts, peg laterals, impactions, and transpositions. Data were analyzed using chi-square or Fisher’s exact test. All the tests were two-sided at the significance level of 0.05 (SAS 9.4). Results In the patient cohort, MLIA prevalence was 37.5% (second most common) and no gender or ethnic differences were identified. We also observed the bilaterally missing lateral incisors more frequently than the unilateral presentation (p = 0.0006). Additionally, 62.5% of patients with unilateral MLIA displayed a contralateral tooth that was a peg (p = 0.0001); however, no association was found with other microdonts. Furthermore, of the 90 patients missing at least one maxillary lateral incisor, 42.2% were missing another tooth type and 10% of MLIA patients also had an impacted tooth (mainly maxillary canines). However, these were not statistically significant. Finally, no transposed teeth were found in our patients. Conclusions This study found that maxillary lateral incisors were the second most frequently missing teeth. When clinicians diagnose congenital absence of a maxillary lateral incisor, the patient should be evaluated for other missing teeth, peg lateral incisors, or potential impactions, especially maxillary canines.

Twenty five genotypes of brown top millet were considered for genetic divergence, association and path analysis during Kharif, 2019. Among the eight yield components studied panicle length (23.67 %) contributed the maximum for divergence followed by fodder yield (14.33 %) and harvest index (13.67%). Divergence studies grouped twenty five genotypes into seven clusters. Among seven clusters, cluster I was the largest having 11 genotypes after which came cluster II with 5 genotypes, cluster III with 3 genotypes, clusters IV and V with 2 genotypes and clusters VI and VII with one genotype each. The maximum intra cluster distance was noticed in cluster IV (8.0788) indicating the highest variability. The maximum inter cluster distance was observed between cluster VI and cluster VII (54.086). Understanding the results obtained from trait association and path analysis revealed positive association and high positive direct effects by fodder yield and harvest index on grain yield.

Background: Clinical trials have shown zoledronic acid as a potent bisphosphonate in preventing bone loss, but with varying potency between patients. Human osteoclasts ex vivo reportedly displayed a variable sensitivity to zoledronic acid > 200-fold, determined by the half-maximal inhibitory concentration (IC50), with cigarette smoking as one of the reported contributors to this variation. To reveal the molecular basis of the smoking-mediated variation on treatment sensitivity, we performed a DNA methylome profiling on whole blood cells from 34 healthy female blood donors. Multiple regression models were fitted to associate DNA methylation with ex vivo determined IC50 values, smoking, and their interaction adjusting for age and cell compositions. Results: We identified 59 CpGs displaying genome-wide significance (p < 1e−08) with a false discovery rate (FDR) < 0.05 for the smoking-dependent association with IC50. Among them, 3 CpGs have p < 1e−08 and FDR < 2e−03. By comparing with genome-wide association studies, 15 significant CpGs were locally enriched (within < 50,000 bp) by SNPs associated with bone and body size measures. Furthermore, through a replication analysis using data from a published multi-omics association study on bone mineral density (BMD), we could validate that 29 out of the 59 CpGs were in close vicinity of genomic sites significantly associated with BMD. Gene Ontology (GO) analysis on genes linked to the 59 CpGs displaying smoking-dependent association with IC50, detected 18 significant GO terms including cation:cation antiporter activity, extracellular matrix conferring tensile strength, ligand–gated ion channel activity, etc. Conclusions: Our results suggest that smoking mediates individual sensitivity to zoledronic acid treatment through epigenetic regulation. Our novel findings could have important clinical implications since DNA methylation analysis may enable personalized zoledronic acid treatment. [ABSTRACT FROM AUTHOR]

The world is currently faced with a pandemic of coronavirus disease 2019 (COVID-19). Several genome-wide and exome-wide studies (GWAS and EWAS) have been performed to identify the variability in the host genetic constitution that likely underlines the inter-individual variabilities in COVID-19 severity and clinical manifestation. Due to the magnitude of the articles available, creating a list of host-specific genetic variants and genes associated with COVID-19 can be both time-consuming and extremely challenging for COVID-19 researchers. To this end, the COVID-19 Host Genome database was built. This is currently the only dedicated, free-to-use database that deals solely with COVID-19 host-specific genetic variants and genes. HyperText Markup language (HTML), Cascading Style Sheets (CSS), Hypertext Preprocessor (PHP), and My Structured Query Language (MySQL) server (version 5.7.38) were used to develop the website, storage, and extraction of the data. So far, 787 genetic variants from 63 previously published articles were collected. The tabular data are hyperlinked to the original articles and the users can download all data from the database. COVID-19 Host GenomeDB is being revised constantly every month, and can benefit the research community studying the genetic variants to improve COVID-19 treatment and prevention strategies.

HIV-associated lipodystrophy (HIVLD) is a metabolic condition with an irregularity in the production of lipoprotein particles, and its occurrence varies among HIV-infected patients. MTP and ABCG2 genes have a role in the transport of lipoproteins. The polymorphisms of MTP -493G/T and ABCG2 34G/A affect its expression and influence the secretion and transportation of lipoproteins. Hence, we investigated the MTP -493G/T and ABCG2 34G/A polymorphisms in 187 HIV-infected patients (64 with HIVLD and 123 without HIVLD) along with 139 healthy controls using polymerase chain reaction (PCR)-restriction fragment length polymorphism and expression analysis using real-time PCR. ABCG2 34A allele showed an insignificantly reduced risk of LDHIV severity [P = 0.07, odds ratio (OR) = 0.55]. MTP -493T allele exhibited a non-significantly reduced risk for the development of dyslipidemia (P = 0.08, OR = 0.71). In patients with HIVLD, the ABCG2 34GA genotype was linked with impaired low-density lipoprotein levels and showed a reduced risk for LDHIV severity (P = 0.04, OR = 0.17). In patients without HIVLD, the ABCG2 34GA genotype was associated with impaired triglyceride levels with marginal significance and showed an increased risk for the development of dyslipidemia (P = 0.07, OR = 2.76). The expression level of MTP gene was 1.22-fold decreased in patients without HIVLD compared with that in patients with HIVLD. ABCG2 gene was upregulated 2.16-fold in patients with HIVLD than in patients without HIVLD. In conclusion, MTP -493C/T polymorphism influences the expression level of MTP in patients without HIVLD. Individuals without HIVLD having ABCG2 34GA genotype with impaired triglyceride levels may facilitate dyslipidemia risk.

The present investigation on correlation and path analysis studies helps to study the relationship among the yield and yield contributing traits along with the quality parameters. The observations were taken in the F1 population of 35 hybrids along with lines (7 lines) and testers (5 testers) for the traits viz. plant height (cm), number of sympodial branches per plant, the number of bolls per plant, boll weight (g), lint index (g), single cotton yield per plant (g), ginning outturn (%), upper half mean length (cm), elongation percent (%) and micronaire value (µg per inch). The results revealed that seed cotton yield per plant had strong and positive association with the traits namely number of sympodial branches per plant (0.646), number of bolls (0.633) and boll weight (0.652). Path analysis study revealed that number of sympodial branches per plant (1.5396), boll weight (0.6285), lint index (1.3526) and upper half mean length (0.3392) had high direct positive effects on single plant yield and indirect very high positive effects through the traits viz., number of sympodial branches per plant via., number of bolls per plant (1.5125) and boll weight (1.0121). Hence selection based on these traits that are positively associated and direct positive effect with the yield would produce the best outcome in the genotypes in further breeding programmes.

Misdiagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) can occur when different case definitions are used by clinicians (relative misdiagnosis) or when failing the genuine diagnosis of another disease (misdiagnosis in a strict sense). This problem translates to a recurrent difficulty in reproducing research findings. To tackle this problem, we simulated data from case-control studies under misdiagnosis in a strict sense. We then estimated the power to detect a genuine association between a potential causal factor and ME/CFS. A minimum power of 80% was obtained for studies with more than 500 individuals per study group. When the simulation study was extended to the situation where the potential causal factor could not be determined perfectly (e.g., seropositive/seronegative in serological association studies), the minimum power of 80% could only be achieved in studies with more than 1000 individuals per group. In conclusion, current ME/CFS studies have suboptimal power under the assumption of misdiagnosis. This power can be improved by increasing the overall sample size using multi-centric studies, reporting the excluded illnesses and their exclusion criteria, or focusing on a homogeneous cohort of ME/CFS patients with a specific pathological mechanism where the chance of misdiagnosis is reduced. [ABSTRACT FROM AUTHOR]

Asthma is a heterogeneous and genetically complex respiratory disease, and more than 300 million people are affected worldwide. In this study, frequencies of four SNPs (rs3816470, rs7216389, rs8067378, rs12603332) in chromosome 17q21 region were analyzed and their relationship with the asthma susceptibility, in the Pashtun population of Khyber Pakhtunkhwa province (KPK) of Pakistan were investigated. DNA samples from 500 subjects (asthma cases/controls) were genotyped by Sanger sequencing. Chi-square tests, logistic regression analysis, linkage disequilibrium, and haplotype analysis techniques were applied to study the association of the SNPs with asthma. Genetic models, including recessive, dominant, co-dominant, over-dominant, and additive, were tested. The frequencies of alleles T/T at rs3816470 (OR = 1.91; 95%CI = 1.15–3.18; p =.011*) and rs7216389 (OR = 2.14; 95%CI = 1.21–3.79; p =.0076*), A/A at rs 8067378 (OR = 1.89; 95%CI = 1.17–3.06; p =.0081*), C/C at rs12603332 (OR = 1.97; 95%CI = 1.18–3.27; p =.008*), under recessive models, respectively, were significantly (p-values <.0125) associated with asthma susceptibility. The frequencies of T/T genotype in rs3816470 (OR = 6.01; 95%CI = 2.48–14.60; p =.000147*), and rs7216389 (OR = 5.05; 95%CI = 1.79–14.21; p =.003296*), and C/C at rs12603332 (OR = 2.64; 95%CI = 1.11–6.32; p =.019063*), were significantly (p-values <.0125) associated with asthma susceptibility in Pashtun women by stratified analysis based on age and gender. Similarly, three unique haplotypes were found associated with disease development and protective effect in female and male subjects. Linkage disequilibrium analysis presented a strong linkage (≥80%) between SNP variants and predicted their co-inheritance in the studied population. The 17q21 variants (rs3816470, rs7216389, rs12603332) were found significantly (p-values <.0125) associated with asthma predisposition in the Pashtun population of KPK exclusively in the female asthmatic cases. Supplemental data for this article can be accessed [ABSTRACT FROM AUTHOR]