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Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination, and discuss their implications for the rates of horizontal gene transfer in bacteria.

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Salinity is one of the major environmental factor that can greatly impact the growth, development, and productivity of barley. Our study aims to detect the natural phenotypic variation of morphological and physiological traits under both salinity and potassium nanoparticles (n-K) treatment. In addition to understanding the genetic basis of salt tolerance in barley is a critical aspect of plant breeding for stress resilience. Therefore, a foliar application of n-K was applied at the vegetative stage for 138 barley accessions to enhance salt stress resilience. Interestingly, barley accessions showed high significant increment under n-K treatment compared to saline soil. Based on genome-wide association studies (GWAS) analysis, causative alleles /reliable genomic regions were discovered underlying improved salt resilience through the application of potassium nanoparticles. On chromosome 2H, a highly significant QTN marker (A:C) was located at position 36,665,559 bp which is associated with APX, AsA, GSH, GS, WGS, and TKW under n-K treatment. Inside this region, our candidate gene is HORVU.MOREX.r3.2HG0111480 that annotated as NAC domain protein. Allelic variation detected that the accessions carrying C allele showed higher antioxidants (APX, AsA, and GSH) and barley yield traits (GS, WGS, and TKW) than the accessions carrying A allele, suggesting a positive selection of the accessions carrying C allele that could be used to develop barley varieties with improved salt stress resilience.

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Introduction: FTO gene belongs to the non-heme Fe (II) and 2 oxoglutarate-dependent dioxygenase superfamily. Polymorphisms within the first intron of the FTO gene have been examined across various populations, yielding disparate findings.The present study aimed to determine the impact of two intronic polymorphisms FTO 30685T/G (rs17817449) and -23525T/A (rs9939609) on the risk of obesity in Punjab, India. Methods: Genotypic and biochemical analysis were done for 671 unrelated participants (obese=333 and non-obese=338) (age≥18 years). Genotyping of the polymorphisms was done by PCR-RFLP method. However, 50% of the samples were sequenced by Sanger sequencing. Results: Both the FTO variants 30685 (TT vs GG: odds ratio (OR), 2.30; 95% confidence interval (CI), 1.39-3.79) and -23525 (TT vs AA: odds ratio (OR), 2.78; 95% confidence interval (CI), 1.37-5.64) showed substantial risk towards obesity by conferring it 2 times and 3 times, respectively. The analysis by logistic regression showed a significant association for both the variants 30685T/G (rs17817449) and -23525T/A (rs9939609) (OR=2.29; 95%CI: 1.47-3.57) and (OR=5.25; 95% CI: 2.68-10.28) under the recessive genetic model, respectively. The haplotype combination TA (30685; -23525) develops a 4 times risk for obesity (P=0.0001). Among obese, the G allele of 30685T/G and A- allele of -23525T/A showed variance in Body mass index (BMI), waist circumference (WC), waist-to-height ratio(WHtR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and triglyceride(TG). Conclusion: The present investigation indicated that both the FTO 30685T/G (rs17817449) and -23525T/A (rs9939609) polymorphisms have a key impact on an individual's vulnerability to obesity in this population.

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Oxford ragwort (Senecio squalidus) is one of only two homoploid hybrid species known to have originated very recently, so it is a unique model for determining genomic changes and stabilization following homoploid hybrid speciation. Here, we provide a chromosome-level genome assembly of S. squalidus with 95% of the assembly contained in the 10 longest scaffolds, corresponding to its haploid chromosome number. We annotated 30,249 protein-coding genes and estimated that ∼62% of the genome consists of repetitive elements. We then characterized genome-wide patterns of linkage disequilibrium, polymorphism, and divergence in S. squalidus and its two parental species, finding that (1) linkage disequilibrium is highly heterogeneous, with a region on chromosome 4 showing increased values across all three species but especially in S. squalidus; (2) regions harboring genetic incompatibilities between the two parental species tend to be large, show reduced recombination, and have lower polymorphism in S. squalidus; (3) the two parental species have an unequal contribution (70:30) to the genome of S. squalidus, with long blocks of parent-specific ancestry supporting a very rapid stabilization of the hybrid lineage after hybrid formation; and (4) genomic regions with major parent ancestry exhibit an overrepresentation of loci with evidence for divergent selection occurring between the two parental species on Mount Etna. Our results show that both genetic incompatibilities and natural selection play a role in determining genome-wide reorganization following hybrid speciation and that patterns associated with homoploid hybrid speciation-typically seen in much older systems-can evolve very quickly following hybridization.

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Accurately estimating effective population size (N e) is essential for understanding evolutionary processes and guiding conservation efforts. This study investigates N e estimation methods in spatially structured populations using a population of moor frog (Rana arvalis) as a case study. We assessed the behaviour of N e estimates derived from the linkage disequilibrium (LD) method as we changed the spatial configuration of samples. Moor frog eggs were sampled from 25 breeding patches (i.e., separate vernal ponds, ditches or parts of larger fens) within a single population, revealing an isolation-by-distance pattern and a local spatial genetic structure. Varying buffer sizes around each patch were used to examine the impact of sampling window size on the estimation of effective number of breeders (N b). Our results indicate a downward bias in LD N b estimates with increasing buffer size, suggesting an underestimation of N b. The observed bias is attributed to LD resulting from including genetically divergent individuals (mixture-LD) confounding LD due to drift. This emphasises the significance of considering even subtle spatial genetic patterns. The implications of these findings are discussed, emphasising the need to account for spatial genetic structure to accurately assess population viability and inform conservation efforts. This study contributes to our understanding of the challenges associated with N e estimation in spatially structured populations and underscores the importance of refining methodologies to address population-specific spatial dynamics for effective conservation planning and management.

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Genetic prediction holds immense promise for translating genetic discoveries into medical advances. As the high-dimensional covariance matrix (or the linkage disequilibrium (LD) pattern) of genetic variants often presents a block-diagonal structure, numerous methods account for the dependence among variants in predetermined local LD blocks. Moreover, due to privacy considerations and data protection concerns, genetic variant dependence in each LD block is typically estimated from external reference panels rather than the original training data set. This paper presents a unified analysis of blockwise and reference panel-based estimators in a high-dimensional prediction framework without sparsity restrictions. We find that, surprisingly, even when the covariance matrix has a block-diagonal structure with well-defined boundaries, blockwise estimation methods adjusting for local dependence can be substantially less accurate than methods controlling for the whole covariance matrix. Further, estimation methods built on the original training data set and external reference panels are likely to have varying performance in high dimensions, which may reflect the cost of having only access to summary level data from the training data set. This analysis is based on novel results in random matrix theory for block-diagonal covariance matrix. We numerically evaluate our results using extensive simulations and real data analysis in the UK Biobank.

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Milk production in tropical regions plays a crucial role both economically and socially. Typically, animals are utilized for dual purposes and are genetically obtained by an intense crossbreeding between Zebu and/or locally adapted breeds, alongside specialized breeds for dairy production. However, uncontrolled mating and crossbreeding may affect the establishment of an effective animal breeding program. The objective of this study was to evaluate Genomic diversity of highly crossbred cattle population in a Low and Middle Tropical environment. All sampled animals were genotyped using the Genessek GGP Bovine 100 chip (n = 859) and public genomic information from eight breeds were employed as reference. The genetic structure of the population was estimated using a Principal Component, Bayesian clustering and a linkage disequilibrium analysis. PCA results revealed that PC1 explained 44.39% of the variation, associated with the indicus/taurus differentiation, and PC2 explained 14.6% of the variation, attributed to the differentiation of Creole and European components. This analysis underscored a low population structure, attributed to the absence of genealogical tracking and the implementation of non-directed crossbreeding. The clustering shows an average contribution of Zebu, Creole, and European Taurine components in the population was 53.26%, 27.60%, and 19.13%, respectively. While an average LD of 0.096 was obtained for a maximum distance of 400 kb. The LD value was low in this population, probably due to the almost no selection applied and the recombination events that occurred during its development. These findings underscore the value of crossbreeding in tropical dairy production but emphasize the importance of directing the mattings.

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Knowledge of past and present genetic diversity within a breed is critical for the design and optimization of breeding programs as well as the development of strategies for the conservation of genetic resources. The Polypay sheep breed was developed at the U.S. Sheep Experiment Station (USSES) in 1968 with the goal of improving productivity in Western U.S. range flocks. It has since flourished in the more intensively managed production systems throughout the U.S. The genetic diversity of the breed has yet to be documented. Therefore, the primary objective of this study was to perform a comprehensive evaluation of the genetic diversity and population structure of U.S. Polypay sheep using both pedigree- and genomic-based methods. Pedigree data from 193 Polypay flocks participating in the National Sheep Improvement Program (NSIP) were combined with pedigree records from USSES (n = 162,997), tracing back to the breed's origin. A subset of these pedigreed sheep from 32 flocks born from 2011 to 2023 were genotyped with the GGP Ovine 50K BeadChip containing 51,867 single nucleotide polymorphisms (SNPs). Four subgroups were used for the pedigree-based analyses: 1) the current generation of animals born in 2020-2022 (n = 20,701), 2) the current generation with a minimum of four generations of known ancestors (n = 12,685), 3) only genotyped animals (n = 1,856), and 4) the sires of the current generation (n = 509). Pedigree-based inbreeding for the full population was 2.2%, with a rate of inbreeding of 0.22% per generation. Pedigree-based inbreeding, Wright's inbreeding, and genomic inbreeding based on runs of homozygosity were 2.9%, 1.3%, and 5.1%, respectively, for the genotyped population. The effective population size ranged from 41 to 249 for the pedigree-based methods and 118 for the genomic-based estimate. Expected and observed heterozygosity levels were 0.409 and 0.403, respectively. Population substructure was evident based on the fixation index (FST), principal component analysis, and model-based population structure. These analyses provided evidence of differentiation from the foundation flock (USSES). Overall, the Polypay breed exhibited substantial genetic diversity and the presence of a population substructure that provides a basis for the implementation of genomic selection in the breed.

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The genome of dromedary camels has been subjected to various evolutionary forces, such as genetic admixture, natural positive selection, and epistatic selection. These forces are considered as main factors associated with the formation of long-range linkage disequilibrium (LRLD) events. We have analyzed whole-genome data of 56 dromedary camel samples from different geographical regions across the Arabian Peninsula for two main purposes: first, to assess the level of linkage disequilibrium, and second, to identify autosomal LRLD events. The analysis revealed a mean r 2 value of 0.25 (±0.028) over the dromedary autosomes, with a continuous decay until reaching a plateau at inter-variant distances >400 kb. A total of 1847 LRLD events were identified within the dromedary autosomes, which harbor 36 prevalent haplotypes. A level of genetic admixture was observed among the dromedary populations analyzed, which might be a source for the observed LRLD events. Four functional interactions were revealed among the genes found within the LRLD events, with some genes overlapping with prevalent haplotypes, indicative of potential epistatic selection. Genes related to renal function, fertility, thermal regulation, bone structure, and insulin regulation were found among the LRLD genes. These genes, along with the defined prevalent haplotypes, can be considered as hotspots for natural positive selection associated with the LRLD distribution on dromedary genomes. In this study, we have for the first time analyzed the genome of dromedary camels for LRLD events possibly influenced by forces including genetic admixture, epistatic and positive selection. The revealed LRLD elements and prevalent haplotypes should be accounted for when designing breeding programmes to conserve the genetic stock of this well-adapted domestic species.

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Background: Numerous research studies have indicated a possible association between type 2 diabetes (T2DM) and gut microbiota. To explore specific metabolic pathways connecting gut microbiota and T2DM, we employed Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC) techniques. Methods: This research utilized data from genome-wide association studies (GWAS) that are publicly accessible. We evaluated the genetic correlation between gut microbiota and T2DM using LDSC. Causality was primarily determined through the inverse variance weighted (IVW) method. To verify the robustness of our results, we conducted sensitivity analyses using several approaches, including the weighted median, MR-Egger, and MR-PRESSO. We integrated summary effect estimates from LDSC, along with forward and reverse MR, into a meta-analysis for T2DM using various data sources. Additionally, mediation analysis was performed to explore the impact of plasma metabolites on the relationship between gut microbiota and T2DM. Results: Our study indicated a significant genetic correlation between genus RuminococcaceaeUCG005 (Rg = -0.26, Rg_P = 2.07×10-4) and T2DM. Moreover, the forward MR analysis identified genus RuminococcaceaeUCG010 (OR = 0.857, 95% CI 0.795, 0.924; P = 6.33×10-5) and order Clostridiales (OR = 0.936, 95% CI 0.878, 0.997; P = 0.039) as being significantly associated with a decreased risk of T2DM. The analysis also highlighted several plasma metabolites as significant mediators in these relationships, with metabolites like octadecadienedioate (C18:2-DC) and branched chain 14:0 dicarboxylic acid being notably involved. Conclusion: The findings demonstrate a significant impact of gut microbiota on T2DM via plasma metabolites, suggesting potential metabolic pathways for therapeutic targeting. This study enhances our understanding of the microbiota's role in T2DM pathogenesis and supports the development of microbiota-based interventions.

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Introduction: Targeted single nucleotide polymorphisms (SNPs) have been used in genomic prediction methodologies to enhance the accuracy of associated genetic transmitting abilities in Holstein cows. The objective of this study was to identify and validate SNPs associated with fertility traits impacting early embryo mortality. Methods: The mRNA sequencing data from day 16 normal (n = 9) and embryo mortality (n = 6) conceptuses from lactating multiparous Holstein cows were used to detect SNPs. The selection of specific genes with SNPs as preliminary candidates was based on associations with reproductive and fertility traits. Validation of candidate SNPs and genotype-to-phenotype analyses were conducted in a separate cohort of lactating primiparous Holstein cows (n = 500). After genotyping, candidate SNPs were filtered using a quality control pipeline via PLINK software. Continuous numeric and binary models from reproductive traits were evaluated using the mixed procedure for a generalized linear model-one way ANOVA or logistic regression, respectively. Results: Sixty-nine candidate SNPs were initially identified, but only 23 passed quality control procedures. Ultimately, the study incorporated 466 observations for statistical analysis after excluding animals with missing genotypes or phenotypes. Significant (p <0.05) associations with fertility traits were identified in seven of the 23 SNPs: DSC2 (cows with the A allele were older at first calving); SREBF1 and UBD (cows with the T or G alleles took longer to conceive); DECR1 and FASN (cows with the C allele were less likely to become pregnant at first artificial insemination); SREBF1 and BOLA-DMB (cows with the T allele were less likely to be pregnant at 150 days in milk). It was also determined that two candidate SNPs within the DSC2 gene were tag SNPs. Only DSC2 SNPs had an important allele substitution effect in cows with the G allele, which had a decreased age at first calving by 10 days. Discussion: Candidate SNPs found in this study could be used to develop genetic selection tools to improve fertility traits in dairy production systems.

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The distribution of fitness effects (DFE) of new mutations plays a central role in evolutionary biology. Estimates of the DFE from experimental Mutation Accumulation (MA) lines are compromised by the complete linkage disequilibrium (LD) between mutations in different lines. To reduce LD, we constructed two sets of recombinant inbred lines from a cross of two C. elegans MA lines. One set of lines ("RIAILs") was intercrossed for ten generations prior to ten generations of selfing; the second set of lines ("RILs") omitted the intercrossing. Residual LD in the RIAILs is much less than in the RILs, which affects the inferred DFE when the sets of lines are analyzed separately. The best-fit model estimated from all lines (RIAILs + RILs) infers a large fraction of mutations with positive effects (∼40%); models that constrain mutations to have negative effects fit much worse. The conclusion is the same using only the RILs. For the RIAILs, however, models that constrain mutations to have negative effects fit nearly as well as models that allow positive effects. When mutations in high LD are pooled into haplotypes, the inferred DFE becomes increasingly negative-skewed and leptokurtic. We conclude that the conventional wisdom - most mutations have effects near zero, a handful of mutations have effects that are substantially negative and mutations with positive effects are very rare - is likely correct, and that unless it can be shown otherwise, estimates of the DFE that infer a substantial fraction of mutations with positive effects are likely confounded by LD.

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OBJECTIVE: We used a polygenic risk score (PRS) to identify high-risk groups for primary open-angle glaucoma (POAG) within population-based cohorts. DESIGN: Secondary analysis of four prospective population-based studies. PARTICIPANTS: We included four European-ancestry cohorts: the United States (US) based Nurses' Health Study (NHS), NHS2, and the Health Professionals Follow-up Study, and the Rotterdam Study (RS) in the Netherlands. The US cohorts included female nurses and male health professionals aged 55+ years. The RS included residents aged 45 years or older living in Rotterdam, the Netherlands. METHODS: PRS weights were estimated by applying the Lassosum method on imputed genotype and phenotype data from the UK-Biobank. This resulted in 144,020 variants, single nucleotide polymorphism (SNPs) and indels, with non-zero betas that we used to calculate a PRS in the target populations. Using multivariable Cox proportional hazard models, we estimated the relationship between the standardized PRS and relative risk for POAG. Additionally, POAG prediction was tested by calculating these models' concordance (Harrell's C-statistic). Finally, we assessed the association between PRS tertiles and glaucoma-related traits. MAIN OUTCOME MEASURES: The relative risk for POAG and Harrell's C-statistic (the equivalent of an area-under-the-curve for longitudinal models). RESULTS: Among 1,046 cases and 38,809‬ controls, the relative risk (95% confidence interval) for POAG for participants in the highest PRS quintile was 3.99 (3.08, 5.18) in the US cohorts, and 4.89 (2.93, 8.17) in the Rotterdam Study, compared with participants with median genetic risk (3rd quintile). In restricted cubic spline analyses, the relation between continuous PRS and POAG risk increased exponentially in the US and Rotterdam cohorts (Pspline<0.05). Combining age, sex, intraocular pressure >25 mmHg, and family history resulted in a meta-analyzed concordance of 0.75 (0.73, 0.75). Adding the PRS to this model improved the concordance to 0.82 (0.80, 0.84). In a meta-analysis of all cohorts, cases in the highest tertile had a larger cup-disc ratio at diagnosis, by 0.11 (0.07, 0.15), and a 2.07-fold increased risk of requiring glaucoma surgery (1.19, 3.60). CONCLUSIONS: Incorporating a PRS into a POAG predictive model improves identification concordance from 0.75 up to 0.82, supporting its potential for guiding more cost-effective screening strategies.

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Heat stress poses a significant environmental challenge that profoundly impacts wheat productivity. It disrupts vital physiological processes such as photosynthesis, by impeding the functionality of the photosynthetic apparatus and compromising plasma membrane stability, thereby detrimentally affecting grain development in wheat. The scarcity of identified marker trait associations pertinent to thermotolerance presents a formidable obstacle in the development of marker-assisted selection strategies against heat stress. To address this, wheat accessions were systematically exposed to both normal and heat stress conditions and phenotypic data were collected on physiological traits including proline content, canopy temperature depression, cell membrane injury, photosynthetic rate, transpiration rate (at vegetative and reproductive stage and 'stay-green'. Principal component analysis elucidated the most significant contributors being proline content, transpiration rate, and canopy temperature depression, which exhibited a synergistic relationship with grain yield. Remarkably, cluster analysis delineated the wheat accessions into four discrete groups based on physiological attributes. Moreover, to explore the relationship between physiological traits and DNA markers, 158 wheat accessions were genotyped with 186 SSRs. Allelic frequency and polymorphic information content value were found to be highest on genome A (4.94 and 0.688), chromosome 1A (5.00 and 0.712), and marker Xgwm44 (13.0 and 0.916). Population structure, principal coordinate analysis and cluster analysis also partitioned the wheat accessions into four subpopulations based on genotypic data, highlighting their genetic homogeneity. Population diversity and presence of linkage disequilibrium established the suitability of population for association mapping. Additionally, linkage disequilibrium decay was most pronounced within a 15-20 cM region on chromosome 1A. Association mapping revealed highly significant marker trait associations at Bonferroni correction P < 0.00027. Markers Xwmc418 (located on chromosome 3D) and Xgwm233 (chromosome 7A) demonstrated associations with transpiration rate, while marker Xgwm494 (chromosome 3A) exhibited an association with photosynthetic rates at both vegetative and reproductive stages under heat stress conditions. Additionally, markers Xwmc201 (chromosome 6A) and Xcfa2129 (chromosome 1A) displayed robust associations with canopy temperature depression, while markers Xbarc163 (chromosome 4B) and Xbarc49 (chromosome 5A) were strongly associated with cell membrane injury at both stages. Notably, marker Xbarc49 (chromosome 5A) exhibited a significant association with the 'stay-green' trait under heat stress conditions. These results offers the potential utility in marker-assisted selection, gene pyramiding and genomic selection models to predict performance of wheat accession under heat stress conditions.

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There is evidence to support a link between abnormal lipid metabolism and Alzheimer's disease (AD) risk. Similarly, observational studies suggest a comorbid relationship between AD and coronary artery disease (CAD). However, the intricate biological mechanisms of AD are poorly understood, and its relationship with lipids and CAD traits remains unresolved. Conflicting evidence further underscores the ongoing investigation into this research area. Here, we systematically assess the cross-trait genetic overlap of AD with 13 representative lipids (from eight classes) and seven CAD traits, leveraging robust analytical methods, well-powered large-scale genetic data, and rigorous replication testing. Our main analysis demonstrates a significant positive global genetic correlation of AD with triglycerides and all seven CAD traits assessed-angina pectoris, cardiac dysrhythmias, coronary arteriosclerosis, ischemic heart disease, myocardial infarction, non-specific chest pain, and coronary artery disease. Gene-level analyses largely reinforce these findings and highlight the genetic overlap between AD and three additional lipids: high-density lipoproteins (HDLs), low-density lipoproteins (LDLs), and total cholesterol. Moreover, we identify genome-wide significant genes (Fisher's combined p value [FCPgene] < 2.60 × 10-6) shared across AD, several lipids, and CAD traits, including WDR12, BAG6, HLA-DRA, PHB, ZNF652, APOE, APOC4, PVRL2, and TOMM40. Mendelian randomisation analysis found no evidence of a significant causal relationship between AD, lipids, and CAD traits. However, local genetic correlation analysis identifies several local pleiotropic hotspots contributing to the relationship of AD with lipids and CAD traits across chromosomes 6, 8, 17, and 19. Completing a three-way analysis, we confirm a strong genetic correlation between lipids and CAD traits-HDL and sphingomyelin demonstrate negative correlations, while LDL, triglycerides, and total cholesterol show positive correlations. These findings support genetic overlap between AD, specific lipids, and CAD traits, implicating shared but non-causal genetic susceptibility. The identified shared genes and pleiotropic hotspots are valuable targets for further investigation into AD and, potentially, its comorbidity with CAD traits.

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This study describes a compact method for determining joint probabilities of identity-by-state (IBS) within and between loci in populations evolving under genetic drift, crossing-over, mutation, and regular inbreeding (partial self-fertilization). Analogues of classical indices of associations among loci arise as functions of these joint identities. This coalescence-based analysis indicates that multi-locus associations reflect simultaneous coalescence events across loci. Measures of association depend on genetic diversity rather than allelic frequencies, as do linkage disequilibrium and its relatives. Scaled indices designed to show monotonic dependence on rates of crossing-over, inbreeding, and mutation may prove useful for interpreting patterns of genome-scale variation.

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Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype x genotype (GxG) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of host and pathogen genomes (co-GWAs) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an Approximate Bayesian Computation method to pinpoint loci with relevant GxG interactions and to infer their underlying interaction matrix. In a combined SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (GxG) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of GxG interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key GxG interactions underpinning HCV adaptation to the European human population after a recent expansion.

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BACKGROUND: Psoriasis is a chronic inflammatory disease that causes significant disability. However, little is known about the underlying metabolic mechanisms of psoriasis. Our study aims to investigate the causality of 975 blood metabolites with the risk of psoriasis. MATERIALS AND METHODS: We mainly applied genetic analysis to explore the possible associations between 975 blood metabolites and psoriasis. The inverse variance weighted (IVW) method was used as the primary analysis to assess the possible association of blood metabolites with psoriasis. Moreover, generalized summary-data-based Mendelian randomization (GSMR) was used as a supplementary analysis. In addition, linkage disequilibrium score regression (LDSC) was used to investigate their genetic correction further. Metabolic pathway analysis of the most suggested metabolites was also performed using MetaboAnalyst 5.0. RESULTS: In our primary analysis, 17 metabolites, including unsaturated fatty acids, phospholipids, and triglycerides traits, were selected as potential factors in psoriasis, with odd ratios (OR) ranging from 0.986 to 1.01. The GSMR method confirmed the above results (ß = 0.001, p < 0.05). LDSC analysis mainly suggested the genetic correlation of psoriasis with genetic correlations (rg) from 0.088 to 0.155. Based on the selected metabolites, metabolic pathway analysis suggested seven metabolic pathways including ketone body that may be prominent pathways for metabolites in psoriasis. CONCLUSION: Our study supports the causal role of unsaturated fatty acid properties and lipid traits with psoriasis. These properties may be regulated by the ketone body metabolic pathway.

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Rice grown under deepwater ecosystem is considered to be natural farming and hence they are considered to be input efficient. Thus, to identify gene responsible for nutritional content under natural conditions, a genome-wide association study (GWAS)was performed. GWAS identified single nucleotide polymorphisms (SNPs) significantly associated with various nutritional quality traits such as Zn (mg/kg), Fe (mg/kg), Protein (%), Oil (%), Amylose (%), Starch (%), Phytic acid (%), Phenol (%) and TDF (%) in 184 deepwater rice accessions evaluated over 2 consecutive years. A total of 278 SNPs distributed across 12 chromosomes were found to be significantly associated with Zn, Oil and Phenol content. Among them, eight high confidence SNPs were significant and identified on chr1 (AX-95933712), chr7 (AX-95957036), and chr8 (AX-95965181) for Zn content. Similarly, on chr2 (AX-95945186), chr8 (AX-95964718), and chr11 (AX-95961099) have been found to be associated with Oil content and on chr3 (AX-95922121) and chr4 (AX-95963889) for Phenol content. Genomic regions of ± 220 kb flanking the three consistent lowest p value containing SNPs for each trait were considered for finding superior haplotypes. These SNPs showed significant phenotypic variations with different identified haplotype blocks. The allelic variations with phenotypes were considered to be superior haplotypes i.e., Block 1: Hap 1 (ACCC) for high Zn content, Block 2: Hap 1 (CT) for high Oil content, and Block 2: Hap 1(CGGG) for low Phenol content. The discovered superior haplotype with high nutritional content could be important for understanding the mechanisms involving nutrient use efficiency. Thus, the present study demonstrated that developing rice varieties with appropriate nutritional quality traits will be possible through the incorporation of such superior haplotypes in breeding programs.

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Introduction: Observational studies have demonstrated strong correlations between metabolic syndrome (MetS) and its related traits. To gain insight into the genetic architecture and molecular mechanism of MetS, we investigated the shared genetic basis of MetS and its related traits and further tested their causal relationships. Methods: Using summary statistics from genome-wide association analyses of about 72,000 subjects from the Korean Genome and Epidemiological Study (KoGES), we conducted genome-wide multi-trait analyses to quantify the overall genetic correlation and Mendelian randomization analyses to infer the causal relationships between traits of interest. Results: Genetic correlation analyses revealed a significant correlation of MetS with its related traits, such as obesity traits (body mass index and waist circumference), lipid traits (triglyceride and high-density lipoprotein cholesterol), glycemic traits (fasting plasma glucose and hemoglobin A1C), and blood pressure (systolic and diastolic). Mendelian randomization analyses further demonstrated that the MetS-related traits showing significant overall genetic correlation with MetS could be genetically determined risk factors for MetS. Discussion: Our study suggests a shared genetic basis of MetS and its related traits and provides novel insights into the biological mechanisms underlying these complex traits. Our findings further inform public health interventions by supporting the important role of the management of metabolic risk factors such as obesity, unhealthy lipid profiles, diabetes, and high blood pressure in the prevention of MetS.