Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Effects of COVID-19 on the cardiovascular system: A mendelian randomization study.

Infections with the coronavirus disease 2019 (COVID-19) and disorders of the heart and blood vessels are causally related. To ascertain the causal relationship between COVID-19 and cardiovascular disease (CVD), we carried out a Mendelian randomization (MR) study through a method known as inverse variance weighting (IVW). When analyzing multiple SNPs, MR can meta-aggregate the effects of multiple loci by using IVW meta-pooling method. The weighted median (WM) is the median of the distribution function obtained by ranking all individual SNP effect values according to their weights. WM yields robust estimates when at least 50% of the information originates from valid instrumental variables (IVs). Directed gene pleiotropy in the included IVs is permitted because MR-Egger does not require a regression straight line through the origin. For MR estimation, IVW, WM and MR-Egger were employed. Sensitivity analysis was conducted using funnel plots, Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. SNPs related to exposure to COVID-19 and CVD were compiled. CVD for COVID-19 infection, COVID-19 laboratory/self-reported negative, and other very severe respiratory diagnosis and population were randomly assigned using MR. The COVID-19 laboratory/self-reported negative results and other very severe respiratory confirmed cases versus MR analysis of CVD in the population (p â€‹> â€‹0.05); COVID-19 infection to CVD (p â€‹= â€‹0.033, OR â€‹= â€‹1.001, 95%CI: 1.000-1.001); and the MR-Egger results indicated that COVID-19 infection was associated with CVD risk. This MR study provides preliminary evidence for the validity of the causal link between COVID-19 infection and CVD.

Mendelian randomization analysis does not reveal a causal influence between keratoconus and three major mental disorders.

Background: Observational studies have suggested at a possible link between keratoconus (KC) and various mental disorders, but the exact direction of causation in these associations remains unclear. This study aims to investigate the potential causal link between KC and three prominent mental conditions: Anxiety, Depression, and Schizophrenia. Methods: Using instrumental variables identified from Genome-wide association study (GWAS) data of European individuals, we conducted bidirectional two-sample Mendelian Randomization (MR) analyses to explore potential causal relationships between KC and the three major mental disorders. We primarily employed the Inverse-Variance Weighted (IVW) method to evaluate causality. In addition, we performed four supplementary MR methods (MR-Egger, Weighted Median, Simple Mode, and Weighted Mode). Furthermore, we conducted various sensitivity analyses to assess heterogeneity, horizontal pleiotropy, and result stability. Results: Our findings did not reveal any concrete evidence of a causal link between KC and the three major mental disorders, namely anxiety, depression, and schizophrenia [anxiety: odds ratio (OR)=0.997, 95% confidence interval (CI)=0.988-1.008, p = 0.621; depression: OR=1.008, 95% CI=0.999-1.017, p = 0.084; schizophrenia: OR=1.002, 95% CI= 0.984-1.020, p = 0.840]. Similarly, the three major mental disorders were not caustically associated with KC [anxiety: OR=1.014, 95% CI=0.635-1.620, p = 0.953; depression: OR=1.109, 95% CI= 0.749-1.643, p = 0.604; schizophrenia: OR= 0.969, 95% CI= 0.884-1.062, p = 0.497]. The sensitivity analyses indicated that the results remained robust, with no signs of pleiotropy or heterogeneity. Conclusions: Our study does not support a genetically determined significant causal connection between KC and the three major mental disorders. The increased occurrence of mental disorders observed in KC patients in observational reports likely arises from factors that can be modified. Further research is warranted to unveil the underlying mechanisms behind the associations observed in observational studies.

Genome-wide association analysis and admixture mapping in a Puerto Rican cohort supports an Alzheimer disease risk locus on chromosome 12.

Introduction: Hispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. Puerto Ricans (PR), a three-way admixed (European, African, and Amerindian) population is the second-largest Hispanic group in the continental US. We aimed to conduct a genome-wide association study (GWAS) and comprehensive analyses to identify novel AD susceptibility loci and characterize known AD genetic risk loci in the PR population. Materials and methods: Our study included Whole Genome Sequencing (WGS) and phenotype data from 648 PR individuals (345 AD, 303 cognitively unimpaired). We used a generalized linear-mixed model adjusting for sex, age, population substructure, and genetic relationship matrix. To infer local ancestry, we merged the dataset with the HGDP/1000G reference panel. Subsequently, we conducted univariate admixture mapping (AM) analysis. Results: We identified suggestive signals within the SLC38A1 and SCN8A genes on chromosome 12q13. This region overlaps with an area of linkage of AD in previous studies (12q13) in independent data sets further supporting. Univariate African AM analysis identified one suggestive ancestral block (p = 7.2×10-6) located in the same region. The ancestry-aware approach showed that this region has both European and African ancestral backgrounds and both contributing to the risk in this region. We also replicated 11 different known AD loci -including APOE- identified in mostly European studies, which is likely due to the high European background of the PR population. Conclusion: PR GWAS and AM analysis identified a suggestive AD risk locus on chromosome 12, which includes the SLC38A1 and SCN8A genes. Our findings demonstrate the importance of designing GWAS and ancestry-aware approaches and including underrepresented populations in genetic studies of AD.

Genetically defined causal effects of natural killer cells related traits in risk of infection: a Mendelian randomization study.

BACKGROUND: The intricate interplay between genetics and immunology often dictates the host's susceptibility to various diseases. This study explored the genetic causal relationship between natural killer (NK) cell-related traits and the risk of infection. METHODS: Single-nucleotide polymorphisms (SNPs) significantly associated with NK cell-related traits were selected as instrumental variables to estimate their genetic causal effects on infection. SNPs from a genome-wide association study (GWAS) on NK cell-related traits, including absolute cell counts, median fluorescence intensities reflecting surface antigen levels, and relative cell counts, were used as exposure instruments. Summary-level GWAS statistics of four phenotypes of infection were used as the outcome data. The exposure and outcome data were analyzed via the two-sample Mendelian randomization method. RESULTS: Each one standard deviation increase in the expression level of human leukocyte antigen (HLA)-DR on HLA-DR+ NK cells was associated with a lower risk of pneumonia (P < 0.05). An increased HLA-DR+ NK/CD3- lymphocyte ratio was related to a lower of risk of pneumonia (P  < 0.05). Each one standard deviation increase in the absolute count of HLA-DR+ NK cells was associated with a lower risk of both bacterial pneumonia and pneumonia (P < 0.05). An increased HLA-DR+ NK/NK ratio was associated with a decreased risk of both pneumonia and bacterial pneumonia (P < 0.05). The results were robust under all sensitivity analyses. No evidence for heterogeneity, pleiotropy, or potential reverse causality was detected. Notably, our analysis did not reveal any significant associations between NK cell-related traits and other phenotypes of infection, including cellulitis, cystitis, and intestinal infection. CONCLUSIONS: HLA-DR+ NK cells could be a novel immune cell trait associated with a lower risk of bacterial pneumonia or pneumonia.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2.

BACKGROUND: As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method. RESULTS: The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics. CONCLUSIONS: Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Genome-wide assessment of population structure and association mapping for agronomic and grain nutritional traits in proso millet (Panicum miliaceum L.).

Proso millet is an important but under-researched and underutilized crop with the potential to become a future smart crop because of its climate-resilient features and high nutrient content. Assessing diversity and marker-trait associations are essential to support the genomics-assisted improvement of proso millet. This study aimed to assess the population structure and diversity of a proso millet diversity panel and identify marker-trait associations for agronomic and grain nutrient traits. In this study, genome-wide single nucleotide polymorphisms (SNPs) were identified by mapping raw genotyping-by-sequencing (GBS) data onto the proso millet genome, resulting in 5621 quality-filtered SNPs in 160 diverse accessions. The modified Roger's Distance assessment indicated an average distance of 0.268 among accessions, with the race miliaceum exhibiting the highest diversity and ovatum the lowest. Proso millet germplasm diversity was structured according to geographic centers of origin and domestication. Genome-wide association mapping identified 40 marker-trait associations (MTAs), including 34 MTAs for agronomic traits and 6 for grain nutrients; 20 of these MTAs were located within genes. Favourable alleles and phenotypic values were estimated for all MTAs. This study provides valuable insights into the population structure and diversity of proso millet, identified marker-trait associations, and reported favourable alleles and their phenotypic values for supporting genomics-assisted improvement efforts in proso millet.

Natural variations of maize ZmLecRK1 determine its interaction with ZmBAK1 and resistance patterns to multiple pathogens.

Maize (Zea mays L.) is one of the most important crops in the world, but its yield and quality are seriously affected by diverse diseases. Identifying broad-spectrum resistance genes is crucial for developing effective strategies to control the disease in maize. In a genome-wide study in maize, we identified a G-type lectin receptor kinase ZmLecRK1 as a new resistance protein against Pythium aphanidermatum, one of the causal pathogens of stalk rot in maize. Genetic analysis showed that the specific ZmLecRK1 allele can confer resistance to multiple pathogens in maize. The resistant variant of ZmLecRK1-mediated cell death and disease resistance require the co-receptor leucine-rich repeat kinase ZmBAK1. A naturally occurring A404S variant in the extracellular domain of ZmLecRK1 determines the ZmLecRK1-ZmBAK1 interaction and the formation of ZmLecRK1-related protein complexes. The amino acid S404, as found in the ZmLecRK1 susceptibility variant, constitutes the ancestral version and is conserved among the majority of grass species, while the resistance variant with A404 is only present in a few maize inbred lines. Substitution of S by A at position 404 in ZmLecRK1-like proteins of sorghum and rice greatly enhances their ability to induce cell death. We propose that selection for the ZmLecRK1 resistance variant enhances its binding affinity to the co-receptor ZmBAK1, thereby enhancing the formation of active complexes for defense. Finally, transcriptomic analysis suggests that ZmLecRK1 likely regulates gene expression related to pathways in cell wall organization or biogenesis in response to pathogen infection. Our work highlights the biotechnological potential for generating disease-resistant crops by precisely modulating the activity of ZmLecRK1 and its homologs through targeted base editing.

Genome-wide association study of image-based trait reveals the genetic architecture of dark-induced leaf senescence in rice.

Darkness is often used as an effective measure to induce leaf senescence. Although many senescence-related genes in rice have been reported, the genome-wide genetic architecture underlying leaf senescence remains poorly understood. In our study, indica and japonica rice showed contrasting responses to dark-induced leaf senescence (DILS). Genome-wide association studies (GWAS) combined with transcriptomic analyses revealed 57, 97, and 48 loci involved in the regulation of the onset, progression, and ending of DILS, respectively. Haplotype analyses showed that the senescence-related loci differentially accumulated in indica and japonica accessions and functioned additively to regulate DILS. A total of 357 candidate genes were identified that are involved in various senescence-related processes such as lipid and amino acid catabolism, photosynthesis, response to reactive oxygen species, and regulation of defense response. In addition, functional analyses of the two candidate genes, OsMYB21 and OsSUB1B, revealed that OsMYB21 positively regulates the onset of DILS, while OsSUB1B negatively regulates its progression. Thus, our results provide new insights into the genetic regulation of DILS in rice.

The potential protective effect of 3-Hydroxybutyrate against aortic dissection: a mendelian randomization analysis.

BACKGROUND: 3-Hydroxybutyrate, also called ß-hydroxybutyrate, is a significant constituent of ketone bodies. Previous observational and experimental studies have suggested that ketogenic diet, especially 3-hydroxybutyrate, may have a protective effect against cardiovascular disease. However, the relationship between ketone bodies, especially 3-hydroxybutyrate, and aortic dissection remains uncertain. MATERIALS AND METHODS: Publicly accessible data from genome-wide association study (GWAS) was utilized to obtain information on ketone bodies, including 3-hydroxybutyrate, acetoacetate and acetone as exposure respectively, while GWAS data on aortic dissection was used as outcome. Subsequently, two-sample Mendelian randomization (MR) analysis was conducted to examine the potential relationship between ketone bodies and aortic dissection. Then, reverse and multivariate Mendelian randomization analyses were performed. Additionally, sensitivity tests were conducted to assess the robustness of MR study. RESULTS: The inverse-variance weighted (IVW) method of Mendelian randomization analysis of gene prediction observed a negative correlation between 3-hydroxybutyrate and risk of aortic dissection (OR 0.147, 95% CI 0.053-0.410). Furthermore, consistent findings were obtained through the implementation of the weighted median, simple mode, Mendelian randomization-Egger (MR-Egger), and weighted mode methods. After adjusting acetoacetate (OR 0.143, 95% CI 0.023-0.900) or acetone (OR 0.100, 95% CI 0.025-0.398), MR analysis of gene prediction still observed a negative correlation between 3-hydroxybutyrate and risk of aortic dissection. No indications of heterogeneity or pleiotropy among the SNPs were detected. CONCLUSION: The findings from the MR analysis demonstrated that genetically predicted 3-hydroxybutyrate exhibits a protective effect against aortic dissection.

Causal relationship between folic acid and prostate cancer risk: Insights from Mendelian randomization analysis.

OBJECTIVE: Folic acid is a commonly used dietary supplement of trace element, but it may increase the risk of prostate cancer (PCa). The aim of this study was to investigate the causal relationship between PCa and folic acid supplementation, as well as dietary folate equivalents, using Mendelian randomization (MR) analysis. METHODS: The Genome-Wide Association Study (GWAS) data of folic acid supplementation and dietary folate equivalents were selected from UK Biobank. Meta-analysis of GWASs of PCa was obtained from PCa Association Group to Investigate Cancer-Associated Alterations in the Genome consortium. MR analysis was performed with inverse variance weighted (IVW) method, MR-Egger regression, simple mode, weighted median, and weighted mode analysis. Heterogeneity and horizontal pleiotropy tests and reverse MR analysis were conducted to assess the robustness and reliability of the causal inference. RESULTS: Six single nucleotide polymorphisms (SNPs) associated with folic acid supplementation and five SNPs associated with dietary folate equivalents were identified as instrumental variables. Genetically predicted folic acid supplementation was associated with an increased risk of PCa (OR 1.200, p < 0.001, by IVW method), and there was no evidence of heterogeneity, horizontal pleiotropy, or significant reverse causality (all p > 0.05). In contrast, dietary folate equivalents showed no significant correlation with PCa (p > 0.05 for all five MR methods). CONCLUSION: This study demonstrated an association between increased risk of PCa and folic acid supplementation, but not with dietary folate equivalents. These findings have implications for public health interventions and personalized preventive strategies for PCa.

Genetically predicted effects of 10 sleep phenotypes on revision of knee arthroplasty: a mendelian randomization study.

BACKGROUND: Accumulating evidence has suggested that sleep disturbances and disorders are common in patients who undergo knee arthroplasty. Revision surgery represents one of the most catastrophic outcomes of knee arthroplasty. However, it remains unclear whether sleep traits are the causes or consequences of knee arthroplasty revision. This study aimed to genetically examine the relationships between sleep traits and knee arthroplasty revision. METHODS: To determine the causal relationship between sleep traits and knee arthroplasty revision, we employed two-sample Mendelian randomization (MR) using summary statistics from the largest publicly available genome-wide association studies (GWASs). The MR design uses genetic variants as instrumental variables to help separate causal relationships from non-causal associations. The main analyses included an inverse variance weighted (IVW) meta-analysis to obtain primary effect estimates. Sensitivity analyses involving the weighted median approach and MR-Egger regression were also conducted to check for potential pleiotropic biases. Numerous complementary sensitivity analyses were also performed to identify statistically significant causal correlations when there were horizontal pleiotropy and heterogeneity across variants. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation. RESULTS: In the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that genetically-predicted short sleep duration short sleep duration (average sleep duration of 24 h is 6 h or less) was positively correlated with the risk of knee arthroplasty revision (odds ratio = 1.03, 95% confidence interval = 1.01-1.05, and P = 0.003), while the association between genetically-predicted long sleep duration and knee arthroplasty was negative. The reverse MR analysis did not yield evidence supporting reverse causality relation between knee arthroplasty revision and sleep phenotypes. CONCLUSION: This research indicated that, of the 10 sleep phenotypes we analyzed, only sleep duration was causally associated with knee arthroplasty revision. These discoveries added to the understanding of the role of sleep traits in the etiology of knee arthroplasty revision, which might further expand our insights into the prevention of knee arthroplasty revision.

Protective effects of spermidine levels against cardiovascular risk factors: An exploration of causality based on a bi-directional Mendelian randomization analysis.

The study investigated the causal relationships between spermidine levels and CVD risk factors using a bi-directional MR approach. Employing genetic variants from extensive GWAS datasets as IVs, the study aimed to determine whether spermidine levels can influence CVD risk factors such as blood pressure, blood glucose, and lipid profiles, and vice versa. The findings suggest a protective role of elevated spermidine levels against hypertension, elevated blood glucose, and lipid profiles (LDL-C and HDL-C). Specifically, increased spermidine levels were significantly associated with lower risk of hypertension (IVW beta = -0.0013453913, p = 0.01597648) and suppression risk of elevated blood glucose (IVW beta = -0.08061330, p = 0.02450205). Additionally, there was a notable association with lipid modulation, showing a decrease in LDL-C (IVW beta = -0.01849161, p = 0.01086728) and an increase in HDL-C (IVW beta = 0.0044608332, P = 0.01760051). Conversely, the influence of CVD risk factors on spermidine levels was minimal, with the exception that elevated blood glucose levels resulted in reduced spermidine levels. (IVW beta = -0.06714391, P = 0.01096123). These results underline the potential of spermidine as a modifiable dietary target for the prevention and management of cardiovascular diseases. Further investigations are warranted to explore the underlying biological mechanisms and the applicability of these findings in broader and diverse populations.

Genetic Structure and Genome-Wide Association Analysis of Growth and Reproductive Traits in Fengjing Pigs.

The Fengjing pig is one of the local pig breed resources in China and has many excellent germplasm characteristics. However, research on its genome is lacking. To explore the degree of genetic diversity of the Fengjing pig and to deeply explore its excellent traits, this study took Fengjing pigs as the research object and used the Beadchip Array Infinium iSelect-96|XT KPS_PorcineBreedingChipV2 for genotyping. We analyzed the genetic diversity, relatedness, inbreeding coefficient, and population structure within the Fengjing pig population. Our findings revealed that the proportion of polymorphic markers (PN) was 0.469, and the effective population size was 6.8. The observed and expected heterozygosity were 0.301 and 0.287, respectively. The G-matrix results indicated moderate relatedness within the population, with certain individuals exhibiting closer genetic relationships. The NJ evolutionary tree classified Fengjing boars into five family lines. The average inbreeding coefficient based on ROH was 0.318, indicating a high level of inbreeding. GWAS identified twenty SNPs significantly associated with growth traits (WW, 2W, and 4W) and reproductive traits (TNB and AWB). Notably, WNT8B, RAD21, and HAO1 emerged as candidate genes influencing 2W, 4W, and TNB, respectively. Genes such as WNT8B were verified by querying the PigBiobank database. In conclusion, this study provides a foundational reference for the conservation and utilization of Fengjing pig germplasm resources and offers insights for future molecular breeding efforts in Fengjing pigs.

Colocalised Genetic Associations Reveal Alternative Splicing Variants as Candidate Causal Links for Breast Cancer Risk in 10 Loci.

Genome-wide association studies (GWASs) have revealed numerous loci associated with breast cancer risk, yet the precise causal variants, their impact on molecular mechanisms, and the affected genes often remain elusive. We hypothesised that specific variants exert their influence by affecting cis-regulatory alternative splice elements. An analysis of splicing quantitative trait loci (sQTL) in healthy breast tissue from female individuals identified multiple variants linked to alterations in splicing ratios. Through colocalisation analysis, we pinpointed 43 variants within twelve genes that serve as candidate causal links between sQTL and GWAS findings. In silico splice analysis highlighted a potential mechanism for three genes-FDPS, SGCE, and MRPL11-where variants in proximity to or on the splice site modulate usage, resulting in alternative splice transcripts. Further in vitro/vivo studies are imperative to fully understand how these identified changes contribute to breast oncogenesis. Moreover, investigating their potential as biomarkers for breast cancer risk could enhance screening strategies and early detection methods for breast cancer.

Placental Origins of Preeclampsia: Insights from Multi-Omic Studies.

Preeclampsia (PE) is a major cause of maternal and neonatal morbidity and mortality worldwide, with the placenta playing a central role in disease pathophysiology. This review synthesizes recent advancements in understanding the molecular mechanisms underlying PE, focusing on placental genes, proteins, and genetic variants identified through multi-omic approaches. Transcriptomic studies in bulk placental tissue have identified many dysregulated genes in the PE placenta, including the PE signature gene, Fms-like tyrosine kinase 1 (FLT1). Emerging single-cell level transcriptomic data have revealed key cell types and molecular signatures implicated in placental dysfunction and PE. However, the considerable variability among studies underscores the need for standardized methodologies and larger sample sizes to enhance the reproducibility of results. Proteomic profiling of PE placentas has identified numerous PE-associated proteins, offering insights into potential biomarkers and pathways implicated in PE pathogenesis. Despite significant progress, challenges such as inconsistencies in study findings and lack of validation persist. Recent fetal genome-wide association studies have identified multiple genetic loci associated with PE, with ongoing efforts to elucidate their impact on placental gene expression and function. Future directions include the integration of multi-omic data, validation of findings in diverse PE populations and clinical subtypes, and the development of analytical approaches and experimental models to study the complex interplay of placental and maternal factors in PE etiology. These insights hold promise for improving risk prediction, diagnosis, and management of PE, ultimately reducing its burden on maternal and neonatal health.

Exploring the etiology of dilated cardiomyopathy using Mendelian randomization.

Background: Observational clinical studies suggest an association between dilated cardiomyopathy (DCM) and various factors including titin, cardiac troponin I (CTnI), desmocollin-2, the perinatal period, alcoholism, Behçet's disease, systemic lupus erythematosus, hyperthyroidism and thyrotoxicosis, hypothyroidism, carnitine metabolic disorder, and renal insufficiency. The causal nature of these associations remains uncertain. This study aims to explore these correlations using the Mendelian randomization (MR) approach. Objective: To investigate the etiology of DCM through Mendelian randomization analysis. Methods: Data mining was conducted in genome-wide association study databases, focusing on variant target proteins (titin, CTnI, desmocollin-2), the perinatal period, alcoholism, Behçet's disease, systemic lupus erythematosus, hyperthyroidism and thyrotoxicosis, hypothyroidism, carnitine metabolic disorder, and renal insufficiency, with DCM as the outcome. The analysis employed various regression models, namely, the inverse-variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode methods. Results: The IVW results showed a correlation between titin protein and DCM, identifying titin as a protective factor [OR = 0.856, 95% CI (0.744-0.985), P = 0.030]. CTnI protein correlated with DCM, marking it as a risk factor [OR = 1.204, 95% CI (1.010-1.436), P = 0.040]. Desmocollin-2 also correlated with DCM and was recognized as a risk factor [OR = 1.309, 95% CI (1.085-1.579), P = 0.005]. However, no causal relationship was found between the perinatal period, alcoholism, Behçet's disease, systemic lupus erythematosus, hyperthyroidism and thyrotoxicosis, hypothyroidism, carnitine metabolic disorder, renal insufficiency, and DCM (P > 0.05). The MR-Egger intercept test indicated no pleiotropy (P > 0.05), affirming the effectiveness of Mendelian randomization in causal inference. Conclusion: Titin, CTnI, and desmocollin-2 proteins were identified as independent risk factors for DCM. Contrasting with previous observational studies, no causal relationship was observed between DCM and the perinatal period, alcoholism, Behçet's disease, systemic lupus erythematosus, hyperthyroidism and thyrotoxicosis, hypothyroidism, carnitine metabolic disorder, or renal insufficiency.

The genetic basis of leaf hair development in upland cotton (Gossypium hirsutum).

Trichomes, which originate from the epidermal cell of aerial organs, provide plants with defense and secretion functions. Although numerous genes have been implicated in trichome development, the molecular mechanisms underlying trichome cell formation in plants remain incompletely understood. Here, we using genome-wide association study (GWAS) across 1037 diverse accessions in upland cotton (Gossypium hirsutum) to identify three loci associated with leaf pubescence (hair) amount, located on chromosome A06 (LPA1), A08 (LPA2) and A11 (LPA3), respectively. GhHD1, a previously characterized candidate gene, was identified on LPA1 and encodes an HD-Zip transcription factor. For LPA2 and LPA3, we identified two candidate genes, GhGIR1 and GhGIR2, both encoding proteins with WD40 and RING domains that act as inhibitors of leaf hair formation. Expression analysis revealed that GhHD1 was predominantly expressed in hairy accessions, whereas GhGIR1 and GhGIR2 were expressed in hairless accessions. Silencing GhHD1 or overexpressing GhGIR1 in hairy accessions induced in a hairless phenotype, whereas silencing GhGIR2 in hairless accessions resulted in a hairy phenotype. We also demonstrated that GhHD1 interact with both GhGIR1 and GhGIR2, and GhGIR1 can interact with GhGIR2. Further investigation indicated that GhHD1 functions as a transcriptional activator, binding to the promoters of the GhGIR1 and GhGIR2 to active their expression, whereas GhGIR1 and GhGIR2 can suppress the transcriptional activation of GhHD1. Our findings shed light on the intricate regulatory network involving GhHD1, GhGIR1 and GhGIR2 in the initiation and development of plant epidermal hairs in cotton.

The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes.

This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of [Formula: see text] and [Formula: see text]. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.