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Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBOs gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping.

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. [ABSTRACT FROM AUTHOR]

ABSTRACT Flowering time is a crucial rice trait that influences its adaptation to various environments, cropping schedules, and agronomic characteristics. Rice breeders have exploited spontaneous mutations in heading date genes to regulate the flowering time. In the present study, we investigated how breeders in Fukui Prefecture regulated days to heading while developing promising rice varieties. Genome‐wide association studies (GWAS) identified Hd1, Hd16, and Hd18 as the major genes controlling days to heading in the population. However, we suspected that this highly bred population might exhibit genomic stratification, which could lead to spurious or false correlations in the GWAS. Thus, we also conducted correlation and partial correlation analyses, which uncovered another key heading date gene, Hd17, that GWAS failed to detect because of its linkage disequilibrium with the major effect gene Hd16. Examination of haplotype frequencies across different breeding periods revealed that the early‐heading Hd16 (Hd16(E)) and late‐heading Hd17 (Hd17(L)) were increasingly co‐selected in the Hd1 functional population. Varieties carrying this Hd16(E)/Hd17(L) combination exhibited days to heading in the range of 70–80, which corresponds to the peak temperature and sunshine period and is also optimal for grain quality and yield components in the Fukui environment. The present study highlights that it is imperative to remain vigilant for Type I (false positives) and Type II (false negatives) errors when performing GWAS on highly bred populations and to implement appropriate countermeasures by accounting for gene‐by‐gene interactions established through the breeding process. We also discuss the effectiveness of Hd16(E), which is not used outside Japan for subtle days to heading control but is widely used in Japan at certain latitudes. [ABSTRACT FROM AUTHOR]

Background: At present, embryologists are attempting to use conventional in vitro fertilization (cIVF) as an alternative to intracytoplasmic sperm injection (ICSI) for preimplantation genetic testing (PGT). However, the potential parental contamination origin of sperm cells and cumulus cells is considered the main limiting factor in the inability of cIVF embryos to undergo PGT. Methods: In this study, we established an IVF-PGTA assay for parental contamination tests with a contamination prediction model based on allele frequencies and linkage disequilibrium (LD) to compute the log-likelihood ratio (LLR) under competing ploidy hypotheses, and then verified its sensitivity and accuracy. Finally, comparisons of the effectiveness of SNP-based analysis and LLR-based IVF-PGTA among 40 cIVF embryos was performed, based on both statistical analysis of the parental contamination rate and chromosomal ploidy concordance rate between TE biopsy and ICM isolations. Results: With IVF-PGTA assay, biopsies with 10% maternal contamination could be detected accurately, and contamination caused by sperm cells could be eliminated completely. Utilizing LLR-based or single Nucleotide Polymorphism (SNP) -based analyses, our comprehensive examination of 40 clinically discarded fresh cIVF embryos revealed an absence of paternal contamination. Strikingly, the LLR-based analysis uniquely revealed a mere instance of 24% maternal contamination within the trophectoderm cell (TE) biopsy of 5* embryo. Furthermore, it was solely through this analysis that embryo (9-F) was identified as a triploid of paternal origin. Conclusions: In this study, we developed a new bioinformatics analysis method for identifying parental contamination during IVF-PGT, especially for couples with nonmale factor infertility. [ABSTRACT FROM AUTHOR]

Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes. [ABSTRACT FROM AUTHOR]

Summary: Forests face many threats. While traditional breeding may be too slow to deliver well‐adapted trees, genomic selection (GS) can accelerate the process. We describe a comprehensive study of GS from proof of concept to operational application in western redcedar (WRC, Thuja plicata).Using genomic data, we developed models on a training population (TrP) of trees to predict breeding values (BVs) in a target seedling population (TaP) for growth, heartwood chemistry, and foliar chemistry traits. We used cross‐validation to assess prediction accuracy (PACC) in the TrP; we also validated models for early‐expressed foliar traits in the TaP.Prediction accuracy was high across generations, environments, and ages. PACC was not reduced to zero among unrelated individuals in TrP and was only slightly reduced in the TaP, confirming strong linkage disequilibrium and the ability of the model to generate accurate predictions across breeding generations. Genomic BV predictions were correlated with those from pedigree but displayed a wider range of within‐family variation due to the ability of GS to capture the Mendelian sampling term.Using predicted TaP BVs in multi‐trait selection, we functionally implemented and integrated GS into an operational tree‐breeding program. [ABSTRACT FROM AUTHOR]

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation. [ABSTRACT FROM AUTHOR]

Correlated regions of systemic interindividual variation (CoRSIV) represent a small proportion of the human genome showing DNA methylation patterns that are the same in all human tissues, are different among individuals, and are partially regulated by genetic variants in cis. In this study we aimed at investigating single‐nucleotide polymorphisms (SNPs) within CoRSIVs and their involvement with pancreatic ductal adenocarcinoma (PDAC) risk. We analyzed 29,099 CoRSIV‐SNPs and 133,615 CoRSIV‐mQTLs in 14,394 cases and 247,022 controls of European and Asian descent. We observed that the A allele of the rs2976395 SNP was associated with increased PDAC risk in Europeans (p = 2.81 × 10−5). This SNP lies in the prostate stem cell antigen gene and is in perfect linkage disequilibrium with a variant (rs2294008) that has been reported to be associated with risk of many other cancer types. The A allele is associated with the DNA methylation level of the gene according to the PanCan‐meQTL database and with overexpression according to QTLbase. The expression of the gene has been observed to be deregulated in many tumors of the gastrointestinal tract including pancreatic cancer; however, functional studies are needed to elucidate the function relevance of the association. [ABSTRACT FROM AUTHOR]

ABSTRACT A major insecticide resistance mechanism in insect pests is knock‐down resistance (kdr) caused by mutations in the voltage‐gated sodium channel (Vgsc) gene. Despite being common in most malaria Anopheles vector species, kdr mutations have never been observed in Anopheles funestus, the principal malaria vector in Eastern and Southern Africa, with resistance mainly being conferred by detoxification enzymes. In a parallel study, we monitored 10 populations of An. funestus in Tanzania for insecticide resistance unexpectedly identified resistance to a banned insecticide, DDT, in the Morogoro region. Through whole‐genome sequencing of 333 An. funestus samples from these populations, we found eight novel amino acid substitutions in the Vgsc gene, including the kdr variant, L976F (equivalent to L995F in An. gambiae), in tight linkage disequilibrium with another (P1842S). The mutants were found only at high frequency in one region and were accompanied by weak signatures of a selective sweep, with a significant decline between 2017 and 2023. Notably, kdr L976F was strongly associated with survivorship to exposure to DDT insecticide, while no clear association was noted with a pyrethroid insecticide (deltamethrin). The WHO prequalifies no DDT products for vector control, and the chemical is banned in Tanzania. Widespread DDT contamination and a legacy of extensive countrywide stockpiles may have selected for this mutation. Continued monitoring is necessary to understand the origin of kdr in An. funestus, and the threat posed to insecticide‐based vector control in Africa. [ABSTRACT FROM AUTHOR]

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. • The genome of S. squalidus , a recent homoploid hybrid species, is presented • Sorting of parental alleles is associated with presence of genetic incompatibilities • Fixed alleles from the major parent are associated with divergent natural selection • Typical patterns of hybrid speciation appear <300 generations after speciation Nevado et al. report genomic reorganization following hybrid speciation. They find that the typical genomic signatures of homoploid hybrid species are well established ∼300 generations after hybridization and that both pre-existing genetic incompatibilities and divergent natural selection play an important role following hybrid speciation. [ABSTRACT FROM AUTHOR]

Background: Multiple studies suggest a potential connection between the gut microbiome and asthma. Our objective is to use advanced genetic and metagenomic techniques to elucidate the causal relationships and underlying mechanisms between gut microbiota and asthma. Methods: The study utilized comprehensive Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) analyses to examine the relationship between 119 gut microbiota genera and asthma, using publicly accessible genome-wide association studies (GWAS). The meta-analysis synthesized summary effect estimates obtained from LDSC, forward MR, and reverse MR. The MiBioGen collaboration, involving 18,340 individuals, identified genetic variations associated with gut bacteria. Asthma data were collected from the UK Biobank, FinnGen, and GERA, encompassing a total of 82,060 cases and 641,049 controls. Results: LDSC analysis revealed significant negative genetic correlations between asthma and RuminococcaceaeUCG004 (Rg = -0.55, p = 7.66 × 10-5) and Subdoligranulum (Rg = -0.35, p = 3.61 × 10-4). Forward MR analysis suggested associations between Butyricicoccus (OR = 0.92, p = 0.01), Turicibacter (OR = 0.95, p = 0.025), Butyrivibrio (OR = 0.98, p = 0.047), and reduced asthma risk. Conversely, Coprococcus2 (OR = 1.10, p = 0.035) and Roseburia (OR = 1.07, p = 0.039) were associated with increased risk. Reverse MR analysis indicated significant associations between genetically predicted asthma and Eubacteriumxylanophilumgroup (Beta = -0.08, p = 9.25 × 10-7), LachnospiraceaeNK4A136group (Beta = -0.05, p = 1.26 × 10-4), and Eisenbergiella (Beta = 0.06, p = 0.015, Rg_P = 0.043). Conclusion: The findings underscore significant genetic correlations and causal relationships between specific gut microbiota and asthma. These insights highlight the potential of gut microbiota as both markers and modulators of asthma risk, offering new avenues for targeted therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: PM2.5 has been associated with various adverse health effects, particularly affecting lung function and chronic respiratory diseases. However, the genetic causality relationship between PM2.5 exposure and lung function as well as chronic respiratory diseases remains poorly understood. Method: We conducted a two-sample Mendelian randomization analysis to investigate the causal impact of PM2.5 on lung function and chronic respiratory diseases. Instrumental variables were carefully selected, with significance thresholds (P < 5 × 10− 8), and linkage disequilibrium with an r2 value below 0.001. Additionally, SNPs with an F-statistic exceeding 10 were included to mitigate potential bias stemming from weak instrumental variables. The primary analytical approach employed the Inverse Variance Weighted method, supplemented by the Weighted Median, MR-Egger, Simple Model, and Weighted Model. Furthermore, pleiotropy and heterogeneity were evaluated through the MR-Egger intercept test and Cochrane's Q test, with a sensitivity analysis conducted using the leave-one-out method. Results: Eight SNPs significantly associated with PM2.5 exposure were identified as Instrumental variables. Mendelian randomization analysis revealed a significant causal association between PM2.5 exposure and lung function (FEV), with an OR of 0.7284 (95% CI: 0.5799–0.9150). Similarly, PM2.5 exposure demonstrated a substantial causal effect on asthma, with an OR of 1.5280 (95% CI: 1.0470–2.2299). However, no causal association was observed between PM2.5 exposure and chronic obstructive pulmonary disease, with an OR of 1.5176 (95% CI: 0.8294–2.7768). Conclusion: These findings emphasize the necessity for continued research efforts in environmental health to develop effective strategies for the prevention and management of chronic respiratory diseases. [ABSTRACT FROM AUTHOR]

Haliotis midae is one of the most important molluscs in South African commercial aquaculture. In this study, a high‐resolution integrated linkage map was constructed, and QTL identified using 2b‐RADseq for genotyping SNPs in three families. The final integrated linkage map was composed by merging the individual family maps, resulting in 3290 informative SNPs mapping to 18 linkage groups, conforming to the known haploid chromosome number for H. midae. The total map spanned 1798.25 cM with an average marker interval of 0.55 cM, representing a genome coverage of 98.76%. QTL analysis, across all three families, resulted in a total of five QTL identified for growth‐related traits, shell width, shell length, and total body weight. For shell width and total body weight, one QTL was identified for each trait respectively, whilst three QTL were identified for shell length. The identified QTL respectively explained between 7.20% and 11.40% of the observed phenotypic variance. All three traits were significantly correlated (r = 0.862–0.970; p < 0.01) and shared overlapping QTL. The QTL for growth traits were mapped back to the H. midae draft genome and BLAST searches revealed the identity of candidate genes, such as egf‐1, megf10, megf6, tnx, sevp1, kcp, notch1, and scube2 with possible functional roles in H. midae growth. The constructed high‐density linkage map and mapped QTL have given valuable insights regarding the genetic architecture of growth‐related traits and will be important genetic resources for marker‐assisted selection. It remains, however, important to validate causal variants through linkage disequilibrium fine mapping in future. [ABSTRACT FROM AUTHOR]

Simple Summary: Understanding the genetics behind the reproductive traits of pigs is crucial for enhancing productivity and economic outcomes in the pork industry. In this study, we investigated the genetic factors influencing reproductive characteristics in Large White pigs. By analyzing the genetic data of 2237 sows from southern China, we identified specific genes and genetic markers associated with important reproductive traits, such as litter size. Our findings provide valuable insights that could help in developing targeted breeding strategies aimed at improving these traits. This research is not only significant for farmers and breeders but also contributes to a more sustainable and efficient approach to pig farming. (1) Background: Reproductive performance is crucial for the pork industry's success. The Large White pig is central to this, yet the genetic factors influencing its reproductive traits are not well understood, highlighting the need for further research. (2) Methods: This study utilized Genome-Wide Association Studies to explore the genetic basis of reproductive traits in the Large White pig. We collected data from 2237 Large White sows across four breeding herds in southern China, focusing on eight reproductive traits. Statistical analyses included principal component analysis, linkage disequilibrium analysis, and univariate linear mixed models to identify significant single-nucleotide polymorphisms and candidate genes. (3) Results: Forty-five significantly related SNPs and 17 potential candidate genes associated with litter traits were identified. Individuals with the TT genotype at SNP rs341909772 showed an increase of 1.24 in the number of piglets born alive (NBA) and 1.25 in the number of healthy births (NHBs) compared with those with the CC genotype. (4) Conclusions: The SNPs and genes identified in this study offer insights into the genetics of reproductive traits in the Large White pig, potentially guiding the development of breeding strategies to improve litter size. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have identified a locus on chromosome 10q22, where many co-inherited single nucleotide polymorphisms (SNPs) are associated with atrial fibrillation (AF). This study seeks to identify the impact of this locus on gene expression at the transcript isoform level in human left atria and to gain insight into potential causal variants. Bulk RNA sequencing was analyzed to identify myozenin 1 (MYOZ1) and synaptopodin 2-like (SYNPO2L) transcript isoforms and the association of common SNPs in this region with transcript isoform expression levels. Chromatin marks were used to suggest candidate regulatory SNPs in this region. Protein amino acid changes were examined for predicted functional consequences. Transfection of MYOZ1 and two SYNPO2L isoforms were performed to localize their encoded proteins in cardiomyocytes derived from stem cells. We identified one MYOZ1 transcript isoform and four SYNPO2L transcript isoforms, two of which encode proteins, while the other two encode long noncoding RNAs (lncRNAs). The risk allele of the strongest AF susceptibility SNP on chromosome 10q22 is associated with decreased MYOZ1 expression and increased expression of the two SNYPO2L lncRNA isoforms. There are many SNPs co-inherited with the top AF-associated SNP due to linkage disequilibrium (LD), including rs11000728, which we propose as the MYOZ1 regulatory SNP, confirmed by reporter gene transfection. In addition, this LD block includes three missense SNPs in the SYNPO2L gene, with the minor protective haplotype predicted to be detrimental to protein function. MYOZ1 and both protein isoforms of SYNPO2L were localized to the sarcomere. This is a complex locus with the potential for several SNPs in a haplotype to alter AF susceptibility by opposing effects on MYOZ1 and SYNPO2L lncRNA expression, along with effects on SYNPO2L protein function. [ABSTRACT FROM AUTHOR]

Sarcopenia presenting a critical challenge in population‐aging healthcare. The elucidation of the interplay between brain structure and sarcopenia necessitates further research. The aim of this study is to explore the casual association between brain structure and sarcopenia. Linkage disequilibrium score regression (LDSC) was conducted to estimate the genetic correlations; MR was then performed to explore the causal relationship between Brain imaging‐derived phenotypes (BIDPs) and three sarcopenia‐related traits: handgrip strength, walking pace, and appendicular lean mass (ALM). The main analyses were conducted using the inverse‐variance weighted method. Moreover, weighted median and MR–Egger were conducted as sensitivity analyses. Genetic association between 6.41% of BIDPs and ALM was observed, and 4.68% of BIDPs exhibited causal MR association with handgrip strength, 2.11% of BIDPs were causally associated with walking pace, and 2.04% of BIDPs showed causal association with ALM. Volume of ventromedial hypothalamus was associated with increased odds of handgrip strength (OR: 1.18, 95% CI: 1.02 to 1.37) and ALM (OR: 1.05, 95% CI: 1.01 to 1.09). Mean thickness of G‐pariet‐inf‐Angular was associated with decreased odds of handgrip strength (OR: 0.83, 95% CI: 0.70 to 0.97) and walking pace (OR: 0.97, 95% CI: 0.93 to 0.99). As part of the brain structure forward causally influences sarcopenia, which may provide new perspectives for the prevention of sarcopenia and offer valuable insights for further research on the brain‐muscle axis. [ABSTRACT FROM AUTHOR]

The distribution of fitness effects of new mutations plays a central role in evolutionary biology. Estimates of the distribution of fitness effect from experimental mutation accumulation lines are compromised by the complete linkage disequilibrium between mutations in different lines. To reduce the linkage disequilibrium, we constructed 2 sets of recombinant inbred lines from a cross of 2 Caenorhabditis elegans mutation accumulation lines. One set of lines ("RIAILs") was intercrossed for 10 generations prior to 10 generations of selfing; the second set of lines ("RILs") omitted the intercrossing. Residual linkage disequilibrium in the RIAILs is much less than in the RILs, which affects the inferred distribution of fitness effect 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 linkage disequilibrium are pooled into haplotypes, the inferred distribution of fitness effect becomes increasingly negative-skewed and leptokurtic. We conclude that the conventional wisdom—most mutations have effects near 0, 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 distribution of fitness effect that infer a substantial fraction of mutations with positive effects are likely confounded by linkage disequilibrium. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Watermelon (Citrullus spp.) is an economically important crop globally, but it is susceptible to various diseases, including powdery mildew. Previous studies have identified genetic factors associated with powdery mildew resistance. However, further research using diverse genetic approaches is necessary to elucidate the underlying genetic mechanisms of this resistance. In this study, the germplasm collection comprising highly homozygous inbred lines was employed, which enabled the accumulation of consistent data and improved the reliability of the genome-wide association study (GWAS) findings. Our investigation identified two significant single-nucleotide polymorphisms (SNPs), pm2.1 and pm3.1, which were strongly associated with disease resistance. Moreover, several candidate genes were revealed within the linkage disequilibrium (LD) blocks surrounding the significant SNPs. In conclusion, the identification of significant SNPs and their additive effects, combined with the discovery of relevant candidate genes, expanded our understanding of the genetic basis of disease resistance and can pave the way for the development of more resilient watermelon cultivars through marker-assisted selection. [ABSTRACT FROM AUTHOR]

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.Key message: Highlighting the importance of the role of potassium nanoparticles in plant tolerance to abiotic stresses, including salinity is the potential for genetic improvement of barley crop resilience through the enhancement of antioxidant defense systems. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis is a widely prevalent cause of pain and disability among older adults. It is an incurable condition, and most treatments are aimed at alleviating symptoms. Aim: This study aimed to investigate the impact of statins on osteoarthritis by using a two-sample Mendelian randomization approach, using genetic variants associated with statin use as instrumental variables. Method: Information on single nucleotide polymorphisms associated with statin medication was obtained from the FinnGen study, and data on osteoarthritis were sourced from the UK Biobank. The inverse variance weighted method was used as the primary analytical approach for the Mendelian randomization analysis. Sensitivity analyses were conducted to evaluate horizontal pleiotropy and heterogeneity. To examine the genetic relationship between statins and osteoarthritis, linkage disequilibrium score regression-based estimates were used. Results: Mendelian randomization analysis indicated a positive effect of statin use on the treatment of osteoarthritis (odds ratio 0.951, 95% confidence interval 0.914–0.99, p < 0.05). This conclusion was supported by various Mendelian randomization methods. Sensitivity analyses revealed no significant directional pleiotropy or influential single nucleotide polymorphisms that could compromise the overall causal inference. Linkage disequilibrium score regression-based estimates suggested a modest genetic correlation between statin use and osteoarthritis (Rg = 0.098, Se = 0.034, p < 0.05), thus reinforcing the robustness of the Mendelian randomization analysis. Conclusion: Statins reduce the risk of osteoarthritis, aligning with the results of observational studies. Further research is essential to validate these results and explore the underlying mechanisms in detail. [ABSTRACT FROM AUTHOR]

Intermittent fertilization intensity (IFI) is closely related to higher fertilization in chickens, but the genetic basis of IFI is not clearly understood. Here, we sampled a total of 939 Wenchang chickens with IFI. The IFI traits had negative correlation with the fertilization rate and exhibited huge phenotypic variations among individuals of the same strain. Based on SNPs derived from a subset of 499 whole genome data, a genome‐wide association study with mixed linear model and further linkage disequilibrium analysis were performed to test potential associations between IFI traits and genomic variants. We identified 35 SNP variants and a 19.82 kb linkage disequilibrium block on chr8 significantly associated with IFI. This block is in the intron of LOC101750715, which shows significant homology with the human LMO4. Therefore, LOC101750715 and LMO4 may regulate IFI. The oviduct's immune regulation is crucial for fertilization. LMO4, activated by IL‐6 and IL‐23, promotes inflammation in epithelial cells. Thus, LOC101750715 and LMO4 may affect fertilization by regulating oviductal inflammation, impacting IFI. Our findings will provide targets for molecular‐marker selection and genetic manipulation for lines of chickens with lower IFI. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Glaucoma is the most common cause of irreversible blindness, and gut microbiota (GM) is associated with glaucoma. Whether this association represents a causal role remains unknown. This study aims to assess the potential association and causal link between GM and various forms of glaucoma, emphasising the need for cautious interpretation of the strength of these associations.Employing a two‐sample bidirectional Mendelian randomisation (MR) framework with false discovery rate correction and various sensitivity analyses, supplemented by genetic correlation analysis via linkage disequilibrium score regression (LDSC) and colocalisation for European summary‐level data between MiBioGen consortium and FinnGen Study, we sought to explore the relationship between GM and glaucoma.While certain microbial taxa showed potential associations with glaucoma subtypes (e.g., Erysipelotrichaceae with primary angle closure glaucoma, Senegalimassilia with exfoliation glaucoma), the overall findings suggest a complex and not definitively causal relationship between GM and glaucoma. Notably, reverse MR analysis did not establish a significant causal effect of glaucoma on GM composition, and no consistent genetic correlations were observed between GM and glaucoma.While our study provides some evidence of associations between specific GM taxa and glaucoma, the results underscore the complexity of these relationships and the need for further research to clarify the potential causal links. The findings highlight the importance of interpreting the gut‐eye axis with caution and suggest that while GM may play a role in glaucoma, it is unlikely to be a predominant causal factor. [ABSTRACT FROM AUTHOR]

BACKGROUND: A large number of domestic and international documents have confirmed that elevated interleukin-1β is associated with primary frozen shoulder. Interleukin-1B gene polymorphisms can affect the transcription and protein expression of interleukin 1β-related genes, resulting in altered levels of cytokines in vivo, and thus altering the incidence of primary frozen shoulder. Through the study of interleukin-1B gene polymorphism and susceptibility to primary frozen shoulder, this study aimed to explore new breakthroughs in the pathogenesis of primary frozen shoulder from the perspective of molecular biology, and to search for susceptibility genes of primary frozen shoulder. OBJECTIVE: To explore the association between linkage disequilibrium of three gene loci in interleukin-1B gene and susceptibility to primary frozen shoulder. METHODS: A case-control study was conducted. There were two groups in this study. One group consisted of 184 patients with primary frozen shoulder, while the other group included 260 healthy controls. The genotypes of interleukin-1B gene loci -511C/T (rs16944), +3954C/T (rs1143634), and -31C/T (rs1143627) were detected by polymerase chain reaction and restriction fragment length polymorphism. The correlation between the probability of linkage disequilibrium and haplotypes and the risk of primary frozen shoulder disease was compared and analyzed. RESULTS AND CONCLUSION: Unconditional Logistic regression analysis showed that the proportion of CT genotypes at rs1143634 and rs1143627 sites increased significantly in the primary frozen shoulder. Linkage disequilibrium analysis showed that rs16944, rs1143634 and rs1143627 tended to be balanced in the control group (D’ value < 0.1), while there was a certain degree of linkage disequilibrium at rs1143627 and rs1143634 sites in the primary frozen shoulder group (D’ value=0.595). Haplotype TTT increased the risk of primary frozen shoulder by 6.66 times compared with CCT type (TTT, OR=6.66, 95% CI=1.59-27.88, P=0.009 7). To conclude, there is a certain degree of linkage disequilibrium between interleukin-1B gene loci rs1143627and rs1143634 in patients with primary frozen shoulder; haplotype TTT formed by these three gene loci may increase the risk of developing primary frozen shoulder. [ABSTRACT FROM AUTHOR]

Objective: Observational studies have revealed a higher probability of hypothyroidism in patients with dermatomyositis (DM) or polymyositis (PM), but there is no consensus on whether hypothyroidism causally influences DM or PM. In the present study, we assessed the causal association between hypothyroidism and the risk of dermatomyositis or polymyositis using two-sample Mendelian randomization (TSMR). Methods: The genome-wide association data of hypothyroidism and dermatomyositis/polymyositis were obtained from the IEU Open GWAS project. Then, TSMR was used to determine whether hypothyroidism is causally associated with DM or PM. Single-nucleotide polymorphisms (SNPs) significantly associated with hypothyroidism were identified and used as instrumental variables (IVs), and the causal relationship between hypothyroidism and DM/PM was examined using TSMR. MR pleiotropy and Cochran's Q test were used to confirm the heterogeneity and pleiotropy of identified IVs, then four different models, including the inverse variance weighted model (IVW), MR-Egger, weighted median and weighted model were applied in this MR analysis. Results: Sixty-eight SNPs for DM and 68 SNPs for PM were selected as the IVs (P<5x10-8; linkage disequilibrium R² <0.001) to assess the causal association between hypothyroidism and DM/PM selected from GWASs on hypothyroidism. The results revealed a positive causal effect of hypothyroidism on both DM and PM (DM: OR 2.563, 95% CI [1.348, 4.874], P = 0.00156; PM: OR1.709, 95% CI [1.157, 2.525], P =0.007). Moreover, there was no heterogeneity or pleiotropy in the results. Conclusion: In conclusion, the MR analysis results provided strong evidence to indicate that hypothyroidism might be causally associated with DM and PM. These findings may have important implications for the pathogenesis and possible future therapies of DM/PM. [ABSTRACT FROM AUTHOR]

Background: Inflammatory bowel disease (IBD) is often clinically associated with conjunctivitis, which may result from genetic associations and causal effects. Methods: Genetic correlations were investigated through the genome-wide association study (GWAS) data on IBD and conjunctivitis using the linkage disequilibrium score regression (LDSC) and heritability estimated in summary statistics (HESS). The causal effect analysis was performed using four methods of Mendelian randomization (MR) and the genetic risk loci common to both diseases were identified by the statistical method of conditional/conjoint false discovery rate (cond/conjFDR), followed by genetic overlap analysis. Finally, a multi-trait GWAS analysis (MTAG) was performed to validate the identified shared loci. Results: IBD (including CD and UC) and conjunctivitis showed a significant overall correlation at the genomic level; however, the local correlation of IBD and CD with conjunctivitis was significant and limited to chromosome 11. MR analysis suggested a significant positive and non-significant negative correlation between IBD (including CD and UC) and conjunctivitis. The conjFDR analysis confirmed the genetic overlap between the two diseases. Additionally, MTAG was employed to identify and validate multiple genetic risk loci. Conclusion: The present study provides evidence of genetic structure and causal effects for the co-morbidity between IBD (both CD and UC) and conjunctivitis, expanding the epidemiologic understanding of the two diseases. [ABSTRACT FROM AUTHOR]

Family-based genome-wide association studies (GWASs) are often claimed to provide an unbiased estimate of the average causal effects (or average treatment effects; ATEs) of alleles, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. We show that this claim does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. This feature will matter if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in linkage disequilibrium patterns. At a single locus, family-based GWAS can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores (PGSs), however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate of the LATE for any subset or weighted average of families. In practice, the potential biases of a family-based GWAS are likely smaller than those that can arise from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, their causal interpretation is less straightforward than has been widely appreciated. [ABSTRACT FROM AUTHOR]

Background Genome-wide association studies have identified several nonfunctional tag single-nucleotide polymorphisms (SNPs) associated with severe malaria. We hypothesized that causal SNPs could play a significant role in severe malaria by altering promoter or enhancer activity. Here, we sought to identify such regulatory SNPs. Methods SNPs in linkage disequilibrium with tagSNPs associated with severe malaria were identified and were further annotated using FUMA. Then, SNPs were prioritized using the integrative weighted scoring method to identify regulatory ones. Gene reporter assays were performed to assess the regulatory effect of a region containing candidates. The association between SNPs and severe malaria was assessed using logistic regression models in a Senegalese cohort. Results Among 418 SNPs, the best candidates were rs116525449 and rs79644959, which were in full disequilibrium between them, and located within the ARL14 promoter. Our gene reporter assay results revealed that the region containing the SNPs exhibited cell-specific promoter or enhancer activity, while the SNPs influenced promoter activity. We detected an association between severe malaria and those 2 SNPs using the overdominance model and we replicated the association of severe malaria with the tagSNP rs116423146. Conclusions We suggest that these SNPs regulate ARL14 expression in immune cells and the presentation of antigens to T lymphocytes, thus influencing severe malaria development. [ABSTRACT FROM AUTHOR]

Objective: This study examines the causal effects of varying exercise intensities on type 2 diabetes mellitus (T2D) through Mendelian randomization (MR) analysis, using genetic variants as instrumental variables. Methods: A two-sample MR analysis was performed, employing Inverse Variance Weighted (IVW) as the primary method, supported by weighted median, MREgger regression, MR-PRESSO, and MR robustness-adjusted contour scores. Data were obtained from the International Exercise Genetics Database (IEGD) and the Global Diabetes Research Consortium (GRC), encompassing over 150,000 individuals for exercise intensity and around 200,000 T2D patients and controls. SNPs linked to exercise intensity were selected based on genome-wide significance (P < 5 × 10^-8) and linkage disequilibrium criteria (distance >10,000 kb, r^2 < 0.001). Results: The IVW analysis suggested that high-intensity exercise might reduce T2D risk, but the association was not statistically significant (OR = 0.667, 95% CI = 0.104-4.255, P = 0.667). The wide confidence interval indicates uncertainty in the effect estimate. Low-intensity exercise showed no significant effect on T2D risk (OR ~ 1.0). Sensitivity analyses, including weighted median and MR-Egger regression, confirmed no significant association between high-intensity exercise and T2D risk. The MR-PRESSO analysis found no significant outliers, and the global test for pleiotropy was non-significant (P = 0.455). Cochran's Q test for heterogeneity in the IVW analysis was non-significant (Q = 12.45, P = 0.234), indicating consistency among SNP-derived estimates. Conclusion: High-intensity exercise potentially reduces T2D risk, but the association is not statistically significant. Further research is needed to understand the complex relationship between exercise intensity and T2D. [ABSTRACT FROM AUTHOR]

Leaf width is a key determinant of planting density and photosynthetic efficiency. In an effort to determine which genes regulate maize plant leaf width, we performed a genome-wide association study (GWAS) of 1.49 × 106 single nucleotide polymorphisms (SNPs) in 80 sequenced backbone inbred maize lines in Jilin Province, China, based upon phenotypic leaf width data from two years. In total, 14 SNPs were identified as being significantly related to leaf width (p < 0.000001), with these SNPs being located on chromosomes 1, 2, 3, 5, 6, 7, 8, and 9. A total of five candidate genes were identified within a mean linkage disequilibrium (LD) distance of 9.7 kb, with a significant SNP being identified within the Zm00001d044327 candidate gene. RNA was then isolated from 12 different inbred maize lines from this GWAS study cohort and was used to conduct qPCR analyses which revealed significant differences in Zm00001d044327 expression among strains exhibiting significant differences in leaf width. Based on an assessment of EMS mutant lines harboring a conserved amino acid stop mutation and two non-synonymous mutations in Zm00001d044327 that exhibited a narrow leaf width, these data suggested that Zm00001d044327 is a key regulator of maize leaf width. [ABSTRACT FROM AUTHOR]

Gene-based association analysis is a powerful tool for identifying genes that explain trait variability. An essential step of this analysis is a conditional analysis. It aims to eliminate the influence of SNPs outside the gene, which are in linkage disequilibrium with intragenic SNPs. The popular conditional analysis method, GCTA-COJO, accounts for the influence of several top independently associated SNPs outside the gene, correcting the z statistics for intragenic SNPs. We suggest a new TauCOR method for conditional gene-based analysis using summary statistics. This method accounts the influence of the full regional polygenic background, correcting the genotype correlations between intragenic SNPs. As a result, the distribution of z statistics for intragenic SNPs becomes conditionally independent of distribution for extragenic SNPs. TauCOR is compatible with any gene-based association test. TauCOR was tested on summary statistics simulated under different scenarios and on real summary statistics for a 'gold standard' gene list from the Open Targets Genetics project. TauCOR proved to be effective in all modelling scenarios and on real data. The TauCOR's strategy showed comparable sensitivity and higher specificity and accuracy than GCTA-COJO on both simulated and real data. The method can be successfully used to improve the effectiveness of gene-based association analyses. [ABSTRACT FROM AUTHOR]

Accurately estimating effective population size (Ne) is essential for understanding evolutionary processes and guiding conservation efforts. This study investigates Ne estimation methods in spatially structured populations using a population of moor frog (Rana arvalis) as a case study. We assessed the behaviour of Ne 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 (Nb). Our results indicate a downward bias in LD Nb estimates with increasing buffer size, suggesting an underestimation of Nb. 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 Ne estimation in spatially structured populations and underscores the importance of refining methodologies to address population‐specific spatial dynamics for effective conservation planning and management. [ABSTRACT FROM AUTHOR]

Generating vast arrays of genetic markers for evolutionary ecology studies has become routine and cost-effective. However, analyzing data from large numbers of loci associated with a small number of finite chromosomes introduces a challenge: loci on the same chromosome do not assort independently, leading to pseudoreplication. Previous studies have demonstrated that pseudoreplication can substantially reduce precision of genetic analyses (and make confidence intervals wider), such as FST and linkage disequilibrium (LD) measures between pairs of loci. In LD analyses, another type of dependency (overlapping pairs of the same loci) also creates pseudoreplication. Building on previous work, we explore the potential of entropy metrics to improve the status quo, particularly total correlation (TC), to assess pseudoreplication in LD studies. Our simulations, performed on a monoecious population with a range of effective population sizes (Ne) and numbers of loci, attempted to isolate the overlapping-pairs-of-loci effect by considering unlinked loci and using entropy to quantify inter-locus relationships. We hypothesized a positive correlation between TC and the number of loci (L), and a negative correlation between TC and Ne. Results from our statistical models predicting TC demonstrate a strong effect of the number of loci, and muted effects of Ne and other predictors, adding support to the use of entropy-based metrics as a tool for estimating the statistical information of complex genetic datasets. Our results also highlight a challenge regarding scalability; computational limitations arise as the number of loci grows, making our current approach limited to smaller datasets. Despite these challenges, this work further refines our understanding of entropy measures, and offers insights into the complex dynamics of genetic information in evolutionary ecology research. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP). [ABSTRACT FROM AUTHOR]

Intermediate wheatgrass (IWG) is a perennial grass that produces nutritious grain while offering substantial ecosystem services. Commercial varieties of this crop are mostly synthetic panmictic populations that are developed by intermating a few selected individuals. As development and generation advancement of these synthetic populations is a multiyear process, earlier synthetic generations are tested by the breeders and subsequent generations are released to the growers. A comparison of generations within IWG synthetic cultivars is currently lacking. In this study, we used simulation models and genomic prediction to analyze population differences and trends of genetic variance in 4 synthetic generations of MN-Clearwater, a commercial cultivar released by the University of Minnesota. Little to no differences were observed among the 4 generations for population genetic, genetic kinship, and genome-wide marker relationships measured via linkage disequilibrium. A reduction in genetic variance was observed when 7 parents were used to generate synthetic populations while using 20 led to the best possible outcome in determining population variance. Genomic prediction of plant height, free threshing ability, seed mass, and grain yield among the 4 synthetic generations showed a few significant differences among the generations, yet the differences in values were negligible. Based on these observations, we make 2 major conclusions: (1) the earlier and latter synthetic generations of IWG are mostly similar to each other with minimal differences and (2) using 20 genotypes to create synthetic populations is recommended to sustain ample genetic variance and trait expression among all synthetic generations. [ABSTRACT FROM AUTHOR]

Caviar yield, caviar color, and body weight are crucial economic traits in sturgeon breeding. Understanding the molecular mechanisms behind these traits is essential for their genetic improvement. In this study, we performed whole-genome sequencing on 673 Russian sturgeons, renowned for their high-quality caviar. With an average sequencing depth of 13.69×, we obtained approximately 10.41 million high-quality single nucleotide polymorphisms (SNPs). Using a genome-wide association study (GWAS) with a single-marker regression model, we identified SNPs and genes associated with these traits. Our findings revealed several candidate genes for each trait: caviar yield: TFAP2A, RPS6KA3, CRB3, TUBB, H2AFX, morc3, BAG1, RANBP2, PLA2G1B, and NYAP1; caviar color: NFX1, OTULIN, SRFBP1, PLEK, INHBA, and NARS; body weight: ACVR1, HTR4, fmnl2, INSIG2, GPD2, ACVR1C, TANC1, KCNH7, SLC16A13, XKR4, GALR2, RPL39, ACVR2A, ADCY10, and ZEB2. Additionally, using the genomic feature BLUP (GFBLUP) method, which combines linkage disequilibrium (LD) pruning markers with GWAS prior information, we improved genomic prediction accuracy by 2%, 1.9%, and 3.1% for caviar yield, caviar color, and body weight traits, respectively, compared to the GBLUP method. In conclusion, this study enhances our understanding of the genetic mechanisms underlying caviar yield, caviar color, and body weight traits in sturgeons, providing opportunities for genetic improvement of these traits through genomic selection. [ABSTRACT FROM AUTHOR]

This study aimed to determine whether using DNA-based markers assigned to individual chromosomes would detect the genetic structures of 446 winter triticale forms originating from two breeding companies more effectively than using the entire pool of markers. After filtering for quality control parameters, 6380 codominant single nucleotide polymorphisms (SNPs) markers and 17,490 dominant diversity array technology (silicoDArT) markers were considered for analysis. The mean polymorphic information content (PIC) values varied depending on the chromosomes and ranged from 0.30 (2R) to 0.43 (7A) for the SNPs and from 0.28 (2A) to 0.35 (6R) for the silicoDArTs. The highest correlation of genetic distance (GD) matrices based on SNP markers was observed among the 5B–5R (0.642), 5B–7B (0.626), and 5A–5R (0.605) chromosomes. When silicoDArTs were used for the analysis, the strongest correlations were found between 5B–5R (0.732) and 2B–5B (0.718). A Bayesian analysis showed that SNPs (total marker pool) allowed for the identification of a more complex structure (K = 4, ΔK = 2460.2) than the analysis based on silicoDArTs (K = 2, ΔK = 128). Triticale lines formed into groups, ranging from two (most of the chromosomes) to four (7A) groups depending on the analyzed chromosome when SNP markers were used for analysis. Linkage disequilibrium (LD) varied among individual chromosomes, ranging from 0.031 for 1A to 0.228 for 7R. [ABSTRACT FROM AUTHOR]

Objective: In the hypothalamic-pituitary-gonadotrophin axis, estrogen plays a key role in the regulation of bone maturation and growth plate closure. This study was designed to explore the link between single nucleotide polymorphisms (SNPs) in the estrogen receptor 1 (ESR1) gene with idiopathic short stature (ISS) susceptibility in a North Indian population. Methods: Four SNPs of ESR1 (rs543650, rs6557177, rs2234693 and rs9340799) were genotyped by Sanger sequencing in ISS patients and controls. Linkage disequilibrium (LD) and haplotyping were done by SNPStat and SHEsisPlus software. The extent of LD was determined by calculating D' and R2 values in SNP paired combinations. Results: Fifty-two ISS patients were compared with 68 controls. A significant positive association was found between rs6557177 and rs543650 genotype and ISS susceptibility. The frequencies of the rs6557177 CC genotype [p=0.030; odds ratio (OR)=0.13; 95% confidence interval (CI): 0.01-1.10] and rs543650 genotype TT (p=0.043; OR=0.29; 95% CI: 0.09-0.92) were increased in the ISS group compared with controls. However, no significant correlation was observed between clinical parameters of patients and these SNPs. rs543650 showed strong LD with rs2234693 and rs9340799, similarly rs2234693 and rs9340799. Conclusion: Our study showed that the CC genotype at rs6557177 and TT genotype at rs543650 of ESR1 constituted a risk factor for developing ISS in North Indian children. These findings may lead to a better understanding of the SNPs associated with ISS susceptibility. [ABSTRACT FROM AUTHOR]

Genetic maps are fundamental resources for linkage and association studies. A fine‐scale genetic map can be constructed by inferring historical recombination events from the genome‐wide structure of linkage disequilibrium—a non‐random association of alleles among loci—by using population‐scale sequencing data. We constructed a fine‐scale genetic map and identified recombination hotspots from 10 092 551 bi‐allelic high‐quality autosomal markers segregating among 150 unrelated Japanese individuals whose genotypes were determined by high‐coverage (30×) whole‐genome sequencing, and the genotype quality was carefully controlled by using their parents' and offspring's genotypes. The pedigree information was also utilized for haplotype phasing. The resulting genome‐wide recombination rate profiles were concordant with those of the worldwide population on a broad scale, and the resolution was much improved. We identified 9487 recombination hotspots and confirmed the enrichment of previously known motifs in the hotspots. Moreover, we demonstrated that the Japanese genetic map improved the haplotype phasing and genotype imputation accuracy for the Japanese population. The construction of a population‐specific genetic map will help make genetics research more accurate. [ABSTRACT FROM AUTHOR]

Host–pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype × genotype (G×G) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of hosts and pathogen genomes (co-genome-wide association studies) 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 G×G interactions and to infer their underlying interaction matrix. In a combined single nucleotide polymorphism dataset 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 (G×G) 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 G×G interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key G×G interactions underpinning HCV adaptation to the European human population after a recent expansion. [ABSTRACT FROM AUTHOR]

Cultivated soybean is an important legume crop that is generally sensitive to flooding stress, including submergence and waterlogging treatments. Wild soybeans, the ancestor of cultivated soybeans, have been potential genetic resources for resistance to abiotic or biotic stresses. The present study aimed to evaluate 163 wild soybean accessions for foliar damages at the early vegetative stage and 105 accessions for germination rates, normal seedling rates, and electrical conductivity at the germination stage under submergence stress. In addition, a genome-wide association study (GWAS) was conducted to identify genomic regions associated with phenotypic measurements at these two growth stages by using MLM and FarmCPU models with publicly available genotypic data. The phenotypic evaluation revealed six and three accessions were tolerant to submergence at the early vegetative and germination stages, respectively. Notably, only one wild soybean accession showed a tolerance reaction to submergence at two stages. Through GWAS analysis, 16 and 20 SNPs across different chromosomes were determined for the submergence-related traits at the early vegetative and germination stages, respectively. Based on the linkage disequilibrium block on the detected genomic regions, ten and four putative genes were identified at the early vegetative and germination stages, respectively. Of these genes, certain genes may be related to submergence stress in wild soybeans. Further studies should be performed to validate the function of these putative genes in the responses of wild soybeans to submergence stress. [ABSTRACT FROM AUTHOR]

X-chromosomal genetic markers are frequently employed in forensic parentage determination owing to their distinctive inheritance patterns. The kinship analysis revealed that two sisters who were not identical twins had identical genotypes on the X chromosome, encompassing 36 X-chromosomal short tandem repeats (X-STRs) and 29 X-chromosomal single-nucleotide polymorphisms (X-SNPs) that spanned the whole X chromosome from the p-telomere to the q-telomere. The identical X-STRs and X-SNPs in the daughters could be the result of linkage or a rare chance of occurrence. This highlights the need for careful analysis and interpretation when dealing with X chromosome markers and that in individual cases, even if two women share an allele at each locus, this does not necessarily mean that they are paternal sisters. The likelihood of random concordance due to maternal alleles must be taken into account.

INTRODUCTION: In the hypothalamic-pituitary-gonadotrophin axis, estrogen plays a key role in the regulation of bone maturation and growth plate closure. This study was designed to explore the link between single nucleotide polymorphisms (SNPs) in the estrogen receptor 1 (ESR1) gene with idiopathic short stature (ISS) susceptibility in a North Indian population. METHODS: Four SNPs of ESR1 (rs543650, rs6557177, rs2234693 and rs9340799) were genotyped by Sanger sequencing in ISS patients and controls. Linkage disequilibrium (LD) and haplotyping were done by SNPStat and SHEsisPlus software. The extent of LD was determined by calculating D’ and R2 values in SNP paired combinations. RESULTS: Fifty-two ISS patients were compared with 68 controls. A significant positive association was found between rs6557177 and rs543650 genotype and ISS susceptibility. The frequencies of the rs6557177 CC genotype [p=0.030; odds ratio (OR)=0.13; 95% confidence interval (CI): 0.01-1.10] and rs543650 genotype TT (p=0.043; OR=0.29; 95% CI: 0.09-0.92) were increased in the ISS group compared with controls. However, no significant correlation was observed between clinical parameters of patients and these SNPs. rs543650 showed strong LD with rs2234693 and rs9340799, similarly rs2234693 and rs9340799. DISCUSSION AND CONCLUSION: Our study showed that the CC genotype at rs6557177 and TT genotype at rs543650 of ESR1 constituted a risk factor for developing ISS in North Indian children. These findings may lead to a better understanding of the SNPs associated with ISS susceptibility.

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Abstract 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

Abstract Background Effective population size (N e ) is a pivotal parameter in population genetics as it can provide information on the rate of inbreeding and the contemporary status of genetic diversity in breeding populations. The population with smaller N e can lead to faster inbreeding, with little potential for genetic gain making selections ineffective. The importance of N e has become increasingly recognized in plant breeding, which can help breeders monitor and enhance the genetic variability or redesign their selection protocols. Here, we present the first N e estimates based on linkage disequilibrium (LD) in the pea genome. Results We calculated and compared N e using SNP markers from North Dakota State University (NDSU) modern breeding lines and United States Department of Agriculture (USDA) diversity panel. The extent of LD was highly variable not only between populations but also among different regions and chromosomes of the genome. Overall, NDSU had a higher and longer-range LD than the USDA that could extend up to 500 Kb, with a genome-wide average r 2 of 0.57 (vs 0.34), likely due to its lower recombination rates and the selection background. The estimated N e for the USDA was nearly three-fold higher (N e = 174) than NDSU (N e = 64), which can be confounded by a high degree of population structure due to the selfing nature of pea. Conclusions Our results provided insights into the genetic diversity of the germplasm studied, which can guide plant breeders to actively monitor N e in successive cycles of breeding to sustain viability of the breeding efforts in the long term.

Abstract Background Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population. Methods To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families). Results We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3–4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10−5). Limitations We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification. Conclusions This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for

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. [ABSTRACT FROM AUTHOR]

Background and purpose: Genome-wide association studies (GWAS) of metabolic syndrome (MetS) have predominantly focused on non-Asian populations, with limited representation from East Asian cohorts. Moreover, previous GWAS analyses have primarily emphasized the significance of top single nucleotide polymorphisms (SNPs), poorly explaining other SNP signals in linkage disequilibrium. This study aimed to reveal the interaction between rs651821 and rs2266788, the principal variants of apolipoprotein A5 (APOA5), within the most significant loci identified through GWAS on MetS. Methods: GWAS on MetS and its components was conducted using the data from the Korean Genome and Epidemiology Study (KoGES) city cohort comprising 58,600 individuals with available biochemical, demographic, lifestyle factors, and the most significant APOA5 locus was analyzed further in depth. Results: According to GWAS of MetS and its diagnostic components, a significant association between the APOA5 SNPs rs651821/rs2266788 and MetS/triglycerides/high-density lipoprotein phenotypes was revealed. However, a conditional analysis employing rs651821 unveiled a reversal in the odds ratio for rs2266788. Therefore, rs651821 and rs2266788 emerged as independent and opposing signals in the extended GWAS analysis, i.e., the multilayered effects. Further gene-environment interaction analyses regarding lifestyle factors such as smoking, alcohol consumption, and physical activity underscored these multilayered effects. Conclusion: This study unveils the intricate interplay between rs651821 and rs2266788 derived from MetS GWAS. Removing the influence of lead SNP reveals an independent protective signal associated with rs2266788, suggesting a multilayered effect between these SNPs. These findings underline the need for novel perspectives in future MetS GWAS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]