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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.

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.

OBJECTIVE: 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.
METHODS: 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.

Genetic variants at the low end of the penetrance spectrum have historically been challenging to interpret because their high population frequencies exceed the disease prevalence of the associated condition, leading to a lack of clear segregation between the variant and disease. There is currently substantial variation in the classification of these variants, and no formal classification framework has been widely adopted. The Clinical Genome Resource Low Penetrance/Risk Allele Working Group was formed to address these challenges and promote harmonization within the clinical community.
The work presented here is the product of internal and community Likert-scaled surveys in combination with expert consensus within the Working Group.
We formally recognize risk alleles and low-penetrance variants as distinct variant classes from those causing highly penetrant disease that require special considerations regarding their clinical classification and reporting. First, we provide a preferred terminology for these variants. Second, we focus on risk alleles and detail considerations for reviewing relevant studies and present a framework for the classification these variants. Finally, we discuss considerations for clinical reporting of risk alleles.
These recommendations support harmonized interpretation, classification, and reporting of variants at the low end of the penetrance spectrum.

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.
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. 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.

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.

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.

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.
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.
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 associated with rheumatoid arthritis and type 1 diabetes were 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.

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.