Linkage Disequilibrium Research Articles

Whole-genome sequencing reveals genomic diversity and selection signatures in Xia’nan cattle

BackgroundThe crossbreeding of specialized beef cattle breeds with Chinese indigenous cattle is a common method of genetic improvement. Xia’nan cattle, a crossbreed of Charolais and Nanyang cattle, is China’s first specialized beef cattle breed with independent intellectual property rights. After more than two decades of selective breeding, Xia’nan cattle exhibit a robust physique, good environmental adaptability, good tolerance to coarse feed, and high meat production rates. This study analyzed the population genetic structure, genetic diversity, and genomic variations of Xia’nan cattle using whole-genome sequencing data from 30 Xia’nan cattle and 178 published cattle genomic data.ResultThe ancestry estimating composition analysis showed that the ancestry proportions for Xia’nan cattle were mainly Charolais with a small amount of Nanyang cattle. Through the genetic diversity studies (nucleotide diversity and linkage disequilibrium decay), we found that the genomic diversity of Xia’nan cattle is higher than that of specialized beef cattle breeds in Europe but lower than that of Chinese native cattle. Then, we used four methods to detect genome candidate regions influencing the excellent traits of Xia’nan cattle. Among the detected results, 42 genes (θπ and CLR) and 131 genes (FST and XP-EHH) were detected by two different detection strategies. In addition, we found a region in BTA8 with strong selection signals. Finally, we conducted functional annotation on the detected genes and found that these genes may influence body development (NR6A1), meat quality traits (MCCC1), growth traits (WSCD1, TMEM68, MFN1, NCKAP5), and immunity (IL11RA, CNTFR, CCL27, SLAMF1, SLAMF7, NAA35, and GOLM1).ConclusionWe elucidated the genomic features and population structure of Xia’nan cattle and detected some selection signals in genomic regions potentially associated with crucial economic traits in Xia’nan cattle. This research provided a basis for further breeding improvements in Xia’nan cattle and served as a reference for genetic enhancements in other crossbreed cattle.

Association between BCL11B gene polymorphisms and age-related hearing loss in the elderly: A case-control study in Qingdao, China.

Age-related hearing loss is a complex disease caused by a combination of genetic and environmental factors, and a study have conducted animal experiments to explore the association between BCL11B heterozygosity and age-related hearing loss. The present study used established genetic models to examine the association between BCL11B gene polymorphisms and age-related hearing loss. A total of 410 older adults from two communities in Qingdao, China, participated in this study. The case group comprised individuals aged ≥ 60 years with age-related hearing loss, and the control group comprised individuals without age-related hearing loss from the same communities. The groups were matched 1:1 for age and sex. The individual characteristics of the participants were analyzed descriptively using the Mann-Whitney U test and the chi-square test. To explore the association between BCL11B gene polymorphisms and age-related hearing loss, conditional logistic regression was performed to construct genetic models for two single-nucleotide-polymorphisms (SNPs) of BCL11B, and haplotype analysis was conducted to construct their haplotype domains. Two SNP sites of the BCL11B gene, four genetic models of rs1152781 (additive, dominant, recessive, and codominant), and five genetic models of rs1152783 (additive, dominant, recessive, codominant, and over dominant) were significantly associated with age-related hearing loss in the models both unadjusted and adjusted for all covariates (P < 0.05). Additionally, a linkage disequilibrium between rs1152781 and rs1152783 was revealed through haplotype analysis. Our study revealed that BCL11B gene polymorphisms were significantly associated with age-related hearing loss.

Computing linkage disequilibrium aware genome embeddings using autoencoders.

The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data. Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L0 +L1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

A novel tomato inter-specific (Solanum lycopersicum var. cerasiforme and S. pimpinellifolium) MAGIC population facilitates trait association and candidate gene discovery in untapped exotic germplasm

Abstract We developed a novel eight-way tomato multi-parental advanced generation inter-cross (MAGIC) population to improve the accessibility of tomato relatives genetic resources to geneticists and breeders. The inter-specific MAGIC population (ToMAGIC) was obtained by inter-crossing four accessions each of Solanum lycopersicum var. cerasiforme (SLC) and S. pimpinellifolium (SP), which respectively are the weedy relative and the ancestor of cultivated tomato. The eight exotic ToMAGIC founders were selected based on a representation of the genetic diversity and geographical distribution of the two taxa. The resulting MAGIC population comprises 354 lines which were genotyped using a new 12k tomato Single Primer Enrichment Technology (SPET) panel and yielded 6,488 high-quality SNPs. The genotyping data revealed a high degree of homozygosity, an absence of genetic structure, and a balanced representation of the founder genomes. To evaluate the potential of the ToMAGIC population, a proof-of-concept was conducted by phenotyping it for fruit size, plant pigmentation, leaf morphology, and earliness. Genome-wide association studies (GWAS) identified strong associations for the studied traits, pinpointing both previously identified and novel candidate genes near or within the linkage disequilibrium blocks. Domesticated alleles for fruit size were recessive and were found, at low frequencies, in wild/ancestral populations. Our findings demonstrate that the newly developed ToMAGIC population is a valuable resource for genetic research in tomato, offering significant potential for identifying new genes that govern key traits in tomato. ToMAGIC lines displaying a pyramiding of traits of interest could have direct applicability for integration into breeding pipelines providing untapped variation for tomato breeding.

Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets.

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

A multi-ethnic reference panel to impute HLA classical and non-classical class I alleles in admixed samples: Testing imputation accuracy in an admixed sample from Brazil.

The MHC class I region contains crucial genes for the innate and adaptive immune response, playing a key role in susceptibility to many autoimmune and infectious diseases. Genome-wide association studies have identified numerous disease-associated SNPs within this region. However, these associations do not fully capture the immune-biological relevance of specific HLA alleles. HLA imputation techniques may leverage available SNP arrays by predicting allele genotypes based on the linkage disequilibrium between SNPs and specific HLA alleles. Successful imputation requires diverse and large reference panels, especially for admixed populations. This study employed a bioinformatics approach to call SNPs and HLA alleles in multi-ethnic samples from the 1000 genomes (1KG) dataset and admixed individuals from Brazil (SABE), utilising 30X whole-genome sequencing data. Using HIBAG, we created three reference panels: 1KG (n = 2504), SABE (n = 1171), and the full model (n = 3675) encompassing all samples. In extensive cross-validation of these reference panels, the multi-ethnic 1KG reference exhibited overall superior performance than the reference with only Brazilian samples. However, the best results were achieved with the full model. Additionally, we expanded the scope of imputation by developing reference panels for non-classical, MICA, MICB and HLA-H genes, previously unavailable for multi-ethnic populations. Validation in an independent Brazilian dataset showcased the superiority of our reference panels over the Michigan Imputation Server, particularly in predicting HLA-B alleles among Brazilians. Our investigations underscored the need to enhance or adapt reference panels to encompass the target population's genetic diversity, emphasising the significance of multiethnic references for accurate imputation across different populations.

Genome-wide linkage and association of novel genes and pathways with type 2 diabetes in Italian families

BackgroundType 2 diabetes mellitus (T2D) stands as one of the most prevalent chronic diseases globally, posing substantial health and economic burdens on society. Within the spectrum of T2D, familial cases emerge as a distinct entity characterized by a strong familial clustering of the disease. This phenomenon has long suggested that genetics contributes substantially to T2D susceptibility, motivating extensive research into the genetic determinants of familial T2D. MethodsWe recruited 212 multigenerational Italian families with multiple cases of T2D. The families were genotyped using genomic array (≥ 600k) derived from the UK Biobank Axiom Array platform. Informative markers were tested via Pseudomarker for linkage to and linkage disequilibrium (i.e., linkage joint to association) with T2D across the following models: dominant with complete penetrance (D1), dominant with incomplete penetrance (D2), recessive with complete penetrance (R1), and recessive with incomplete penetrance (R2). ResultsWe identified a total of 566 variants reaching genome-wide significant (P < 0.00005) linkage and/or association to/with the risk of T2D in Italian families. Of the 355 genes identified in our study, 341 (96%) are novel and have not been reported with T2D or any of its related phenotypes (i.e., obesity, metabolic syndrome, insulin resistance, polycystic ovary syndrome, and hyperglycemia). ConclusionOur study constitutes the first familial T2D-linkage and association study in the Italian population. However, the functional relevance of the novel variants and genes reported in our study remains to be explored.

Improving a polygenic risk score (PRS) for breast cancer (BC) risk assessment in diverse ancestries.

10533 Background: Accurate BC risk assessment is essential to identify women for whom screening and preventive interventions may be lifesaving. Incorporation of PRS into clinical models can improve risk prediction, but most PRS have shown suboptimal performance among non-Europeans. We previously described a multiple-ancestry PRS (MA-149) based on 56 ancestry-informative and 93 BC-associated single-nucleotide polymorphisms (SNPs). MA-149 achieved accuracy for diverse populations by characterizing genetic ancestry at each SNP in terms of fractions attributable to reference ancestries (African, East Asian, European) and applying ancestry-specific risks and frequencies. MA-149 significantly outperformed the Tyrer-Cuzick (TC) model, and integration of MA-149 with TC improved predictive accuracy by roughly two-fold over TC alone. Here, we aimed to improve MA-149 by expanding the set of BC SNPs and refining ancestry-specific risks. Methods: We developed a novel stepwise regression methodology accounting for linkage disequilibrium to select an optimal set of BC SNPs. Women referred for hereditary cancer testing and negative for pathogenic variants in BC genes were divided into consecutive cohorts to (1) refine ancestry-specific SNP risks ( N=58,191 Black/African; N=27,160 East Asian), (2) develop the PRS ( N=184,322), and (3) conduct independent validation ( N=146,110). Predictive accuracy and calibration of the new PRS were evaluated in the full cohort and subpopulations of different ancestries. Odds ratios (OR) from multivariable regression are reported per standard deviation. Results: A set of 385 SNPs (56 ancestry, 329 BC) was selected for the new PRS (MA-385). MA-385 improved upon clinical factors and outperformed MA-149 within each ancestry (Table). Among non-Europeans, MA-385 was a better BC risk predictor (OR=1.47, 95% CI: 1.42-1.52) than MA-149 (OR=1.40, 95% CI: 1.35-1.45). The strongest associations were observed in Ashkenazi Jewish and Hispanic women (Table). MA-385 identified more women at &gt;2-fold increased risk than MA-149 (6.5 % vs 2%). Goodness-of-fit tests showed that MA-385 was well-calibrated while a 385-SNP PRS with European weights was miscalibrated for non-Europeans. Conclusions: MA-385 was well-calibrated, improved upon clinical factors, and outperformed existing PRS in all tested ancestries. Incorporation of MA-385 into risk assessment could improve the early detection and prevention of BC. [Table: see text]

The genetic architecture of dog ownership: large-scale genome-wide association study in 97,552 European-ancestry individuals.

Dog ownership has been associated with several complex traits and there is evidence of genetic influence. We performed a genome-wide association study of dog ownership through meta-analysis of 31,566 Swedish twins in five discovery cohorts and additional 65,986 European-ancestry individuals in three replication cohorts from Sweden, Norway, and the UK. Association test with >7.4 million single-nucleotide polymorphisms were meta-analyzed using a fixed effect model after controlling for population structure and relatedness. We identified two suggestive loci using discovery cohorts, which did not reach genome-wide significance after meta-analysis with replication cohorts. Single-nucleotide polymorphisms-based heritability of dog ownership using linkage disequilibrium score regression was estimated at 0.123 (CI 0.038-0.207) using the discovery cohorts and 0.018 (CI -0.002, 0.039) when adding in replication cohorts. Negative genetic correlation with complex traits including type 2 diabetes, depression, neuroticism and asthma was only found using discovery summary data. Furthermore, we did not identify any genes/gene-sets reaching even suggestive level of significance. This genome-wide association study does not, by itself, provide clear evidence on common genetic variants that influence the dog ownership among European-ancestry individuals.

Polygenic risk of social isolation behavior and its influence on psychopathology and personality.

Social isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a genome-wide associations study (GWAS) was carried out to identify genetic variants that contribute specifically to social isolation behavior (SIB) in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP-based heritability estimate. Using the SIB GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with self-reported friendship scores, comprising items related to friendship quality and quantity, at age 12 and 18 to determine whether genetic predisposition manifests during childhood development. At age 18, friendship scores were associated with the SIB PRS, demonstrating that the genetic factors can predict related social traits in late adolescence. Linkage disequilibrium (LD) score correlation using the SIB GWAS demonstrated genetic correlations with autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), educational attainment, extraversion, and loneliness. However, no evidence of causality was found using a conservative Mendelian randomization approach between SIB and any of the traits in either direction. Genomic Structural Equation Modeling (SEM) revealed a common factor contributing to SIB, neuroticism, loneliness, MDD, and ASD, weakly correlated with a second common factor that contributes to psychiatric and psychotic traits. Our results show that SIB contributes a small heritable component, which is associated genetically with other social traits such as friendship as well as psychiatric disorders.

Circulating anti-Müllerian hormone levels in pre-menopausal women: novel genetic insights from a genome-wide association meta-analysis.

Can a genome-wide association study (GWAS) meta-analysis, including a large sample of young premenopausal women from a founder population from Northern Finland, identify novel genetic variants for circulating anti-Müllerian hormone (AMH) levels and provide insights into single-nucleotide polymorphism enrichment in different biological pathways and tissues involved in AMH regulation? The meta-analysis identified a total of six loci associated with AMH levels at P < 5 × 10-8, three of which were novel in or near CHEK2, BMP4, and EIF4EBP1, as well as highlighted significant enrichment in renal system vasculature morphogenesis, and the pituitary gland as the top associated tissue in tissue enrichment analysis. AMH is expressed by preantral and small antral stage ovarian follicles in women, and variation in age-specific circulating AMH levels has been associated with several health conditions. However, the biological mechanisms underlying the association between health conditions and AMH levels are not yet fully understood. Previous GWAS have identified loci associated with AMH levels in pre-menopausal women, in or near MCM8, AMH, TEX41, and CDCA7. We performed a GWAS meta-analysis for circulating AMH level measurements in 9668 pre-menopausal women. We performed a GWAS meta-analysis in which we combined 2619 AMH measurements (at age 31 years) from a prospective founder population cohort (Northern Finland Birth Cohort 1966, NFBC1966) with a previous GWAS meta-analysis that included 7049 pre-menopausal women (age range 15-48 years) (N = 9668). NFBC1966 AMH measurements were quantified using an automated assay. We annotated the genetic variants, combined different data layers to prioritize potential candidate genes, described significant pathways and tissues enriched by the GWAS signals, identified plausible regulatory roles using colocalization analysis, and leveraged publicly available summary statistics to assess genetic and phenotypic correlations with multiple traits. Three novel genome-wide significant loci were identified. One of these is in complete linkage disequilibrium with c.1100delC in CHEK2, which is found to be 4-fold enriched in the Finnish population compared to other European populations. We propose a plausible regulatory effect of some of the GWAS variants linked to AMH, as they colocalize with GWAS signals associated with gene expression levels of BMP4, TEX41, and EIFBP41. Gene set analysis highlighted significant enrichment in renal system vasculature morphogenesis, and tissue enrichment analysis ranked the pituitary gland as the top association. The GWAS meta-analysis summary statistics are available for download from the GWAS Catalogue with accession number GCST90428625. This study only included women of European ancestry and the lack of sufficiently sized relevant tissue data in gene expression datasets hinders the assessment of potential regulatory effects in reproductive tissues. Our results highlight the increased power of founder populations and larger sample sizes to boost the discovery of novel trait-associated variants underlying variation in AMH levels, which aided the characterization of GWAS signals enrichment in different biological pathways and plausible genetic regulatory effects linked with AMH level variation for the first time. This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the MATER Marie Sklodowska-Curie Grant Agreement No. 813707 and Oulu University Scholarship Foundation and Paulon Säätiö Foundation. (N.P.-G.), Academy of Finland, Sigrid Jusélius Foundation, Novo Nordisk, University of Oulu, Roche Diagnostics (T.T.P.). This work was supported by the Estonian Research Council Grant 1911 (R.M.). J.R. was supported by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreements No. 874739 (LongITools), 824989 (EUCAN-Connect), 848158 (EarlyCause), and 733206 (LifeCycle). U.V. was supported by the Estonian Research Council grant PRG (PRG1291). The NFBC1966 received financial support from University of Oulu Grant No. 24000692, Oulu University Hospital Grant No. 24301140, and ERDF European Regional Development Fund Grant No. 539/2010 A31592. T.T.P. has received grants from Roche, Perkin Elmer, and honoraria for scientific presentations from Gedeon Richter, Exeltis, Astellas, Roche, Stragen, Astra Zeneca, Merck, MSD, Ferring, Duodecim, and Ajaton Terveys. For all other authors, there are no competing interests.

Variability of polygenic prediction for body mass index in Africa

BackgroundPolygenic prediction studies in continental Africans are scarce. Africa’s genetic and environmental diversity pose a challenge that limits the generalizability of polygenic risk scores (PRS) for body mass index (BMI) within the continent. Studies to understand the factors that affect PRS variability within Africa are required.MethodsUsing the first multi-ancestry genome-wide association study (GWAS) meta-analysis for BMI involving continental Africans, we derived a multi-ancestry PRS and compared its performance to a European ancestry-specific PRS in continental Africans (AWI-Gen study) and a European cohort (Estonian Biobank). We then evaluated the factors affecting the performance of the PRS in Africans which included fine-mapping resolution, allele frequencies, linkage disequilibrium patterns, and PRS-environment interactions.ResultsPolygenic prediction of BMI in continental Africans is poor compared to that in European ancestry individuals. However, we show that the multi-ancestry PRS is more predictive than the European ancestry-specific PRS due to its improved fine-mapping resolution. We noted regional variation in polygenic prediction across Africa’s East, South, and West regions, which was driven by a complex interplay of the PRS with environmental factors, such as physical activity, smoking, alcohol intake, and socioeconomic status.ConclusionsOur findings highlight the role of gene-environment interactions in PRS prediction variability in Africa. PRS methods that correct for these interactions, coupled with the increased representation of Africans in GWAS, may improve PRS prediction in Africa.

Consequences of domestication in eastern oyster: Insights from whole genomic analyses.

Selective breeding for production traits has yielded relatively rapid successes with high-fecundity aquaculture species. Discovering the genetic changes associated with selection is an important goal for understanding adaptation and can also facilitate better predictions about the likely fitness of selected strains if they escape aquaculture farms. Here, we hypothesize domestication as a genetic change induced by inadvertent selection in culture. Our premise is that standardized culture protocols generate parallel domestication effects across independent strains. Using eastern oyster as a model and a newly developed 600K SNP array, this study tested for parallel domestication effects in multiple independent selection lines compared with their progenitor wild populations. A single contrast was made between pooled selected strains (1-17 generations in culture) and all wild progenitor samples combined. Population structure analysis indicated rank order levels of differentiation as [wild - wild] < [wild - cultured] < [cultured - cultured]. A genome scan for parallel adaptation to the captive environment applied two methodologically distinct outlier tests to the wild versus selected strain contrast and identified a total of 1174 candidate SNPs. Contrasting wild versus selected strains revealed the early evolutionary consequences of domestication in terms of genomic differentiation, standing genetic diversity, effective population size, relatedness, runs of homozygosity profiles, and genome-wide linkage disequilibrium patterns. Random Forest was used to identify 37 outlier SNPs that had the greatest discriminatory power between bulked wild and selected oysters. The outlier SNPs were in genes enriched for cytoskeletal functions, hinting at possible traits under inadvertent selection during larval culture or pediveliger setting at high density. This study documents rapid genomic changes stemming from hatchery-based cultivation of eastern oysters, identifies candidate loci responding to domestication in parallel among independent aquaculture strains, and provides potentially useful genomic resources for monitoring interbreeding between farm and wild oysters.

Discussion on the relationship between gut microbiota and glioma through Mendelian randomization test based on the brain gut axis.

In the realm of Gut-Brain axis research, existing evidence points to a complex bidirectional regulatory mechanism between gut microbiota and the brain. However, the question of whether a causal relationship exists between gut microbiota and specific types of brain tumors, such as gliomas, remains unresolved. To address this gap, we employed publicly available Genome-Wide Association Study (GWAS) and MIOBEN databases, conducting an in-depth analysis using Two-Sample Mendelian Randomization (MR). We carried out two sets of MR analyses. The preliminary analysis included fewer instrumental variables due to a high genome-wide statistical significance threshold (5×10-8). To enable a more comprehensive and detailed analysis, we adjusted the significance threshold to 1×10-5. We performed linkage disequilibrium analysis (R2 <0.001, clumping distance = 10,000kb) and detailed screening of palindromic SNPs, followed by MR analysis and validation through sensitivity analysis. Our findings reveal a causal relationship between gut microbiota and gliomas. Further confirmation via Inverse Variance Weighting (IVW) identified eight specific microbial communities related to gliomas. Notably, the Peptostreptococcaceae and Olsenella communities appear to have a protective effect, reducing glioma risk. This study not only confirms the causal link between gut microbiota and gliomas but also suggests a new avenue for future glioma treatment.

Discovery of knock-down resistance in the major African malaria vector Anopheles funestus.

A major mechanism of insecticide resistance 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. While monitoring 10 populations of An. funestus in Tanzania, we unexpectedly found resistance to DDT, a banned insecticide, in one location. Through whole-genome sequencing of 333 An. funestus samples from these populations, we found 8 novel amino acid substitutions in the Vgsc gene, including the kdr variant, L976F (L1014F in An. gambiae), in tight linkage disequilibrium with another (P1842S). The mutants were found only at high frequency in one region, with a significant decline between 2017 and 2023. Notably, kdr L976F was strongly associated with survivorship to the exposure to DDT insecticide, while no clear association was noted with a pyrethroid insecticide (deltamethrin). Further study is necessary to identify the origin and spread of kdr in An. funestus, and the potential threat to current insecticide-based vector control in Africa.

An antisense lncRNA mediated mechanism controls desmoplakin gene expression

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Department of Innovation, Research and University of the Autonomous Province of Bolzano-South Tyrol (Italy). Background Desmosomal genes are established as definitive genes for Arrhythmogenic Cardiomyopathy (ACM). The desmoplakin encoding gene, DSP, is amongst the most frequently mutated genes in ACM patients. While rare genetic variants within desmosomal genes can cause ACM, common variants might also affect cardiac conduction traits in the general population. Purpose 1) To explore the association between common genetic variants in ACM desmosomal genes (DSC2, DSG2, PKP2, JUP, DSP) and cardiac conduction traits. 2) To investigate the effect of a lncRNA, identified from downstream analyses, on DSP gene expression. Methods P-wave, PR, QRS and QT intervals were measured using standard 12-lead electrocardiograms (ECG) in 4295 participants to the Cooperative Health Research in South Tyrol (CHRIS) study (data release 2). Samples were genotyped using the Illumina HumanOmniExpressExome array and imputed on the 1000 Genome Phase 1 panel. We conducted a genetic association study on 3458 variants within the linkage disequilibrium blocks that encompass desmosomal genes, assuming an additive genetic model, at 1.9x10-4 statistical significance. Mendelian Randomization (MR) analyses integrating ECG traits and Genotype-Tissue Expression (GTEx) database transcription levels were performed. A specific GapmeR was used to downregulate the antisense RP3-512B11.3 lncRNA expression in human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). hiPSC-CMs lysates were analyzed by digital droplet PCR and Wes™ immunoassay system. Results The rs2744389 variant in DSP gene is associated with a shorter QRS interval (-1.10 ms per effect allele copy; p=3.5x10-6). In the GTEx database, rs2744389 is identified as an expression quantitative trait locus for the antisense lncRNA. MR results support a causal effect of the lncRNA on QRS. To assess the hypothesis that the lncRNA regulates DSP gene expression, we identified a Gapmer able to downregulate the lncRNA expression by approximately 60% in hiPSC-CMs. The lncRNA downregulation led to a significant increase of DSP expression, both at mRNA and protein level, compared to hiPSC-CMs treated with a negative control Gapmer (DSP/RPP30: 16.39±3.32 vs 11.84±1.79; p=0.0313), (Desmoplakin/total protein: 2.44±0.29 vs 1.66±0.11; p=0.0156). Conclusions Starting with the identification of an association between a common genetic variant in DSP gene and a significantly shorter QRS interval in a general population sample, we further unrevealed a new mechanism regulating DSP expression through an antisense lncRNA. While requiring further evidence, these findings might pave the way to a potentially novel therapeutic approach for DSP mutations carriers characterized by desmoplakin protein deficit, as for example patients affected by ACM, but also by dilated cardiomyopathy, cardiocutaneous diseases such as the Carvajal syndrome, and some cancer conditions.

Identifying potential drug targets for non-alcoholic fatty liver disease: a drug target Mendelian randomization study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Research and Innovation Training Grant: Centre for Doctoral Training in AI-enabled Healthcare Systems Background Non-alcoholic fatty liver disease (NAFLD), a range of conditions caused by the build-up of fat in the liver, is the most common form of chronic liver disease, with an estimated prevalence of ~25% globally. NAFLD is associated with increased risk of all-cause mortality, with CVD emerging as the primary cause of death amongst affected individuals. The aetiology of NAFLD is not yet clearly understood, and currently no drugs exist for its treatment, with therapeutics aimed at symptom management instead. Purpose To identify and prioritise candidate protein drug targets and pathways relevant for therapeutic intervention in NAFLD. Methods To identify plasma proteins causally associated with NAFLD, we analysed genetic associations on 1,561 proteins from 35,559 participants in the deCODE study, and compared these to associations from a GWAS of biopsy confirmed NAFLD cases (1,483 cases, 17,781 controls). We sourced genetic variants from a 400kbp region around each protein-coding gene, pruning variants on an F-statistic of 15, minor allele frequency threshold of 1% and linkage disequilibrium r-squared of 0.4. cis-Mendelian randomization (MR) was used to identify proteins associated with NAFLD. Bias due to pleiotropy was limited by excluding variants with leverage statistic more than 3 times the mean, or outlier statistic larger than 10.83. We used colocalization analysis to identify proteins with a posterior probability of at least 0.8, indicating the presence of common causal variants. We further prioritised proteins by annotating them with information on druggability (i.e. whether a protein is a target for a known drug, or predicted to be a target for a drug), mRNA expression in pathology-relevant tissues, and involvement in known biological pathways. Results We identified 5 proteins with a multiplicity-corrected (p&amp;lt;3.2×10-5) association with NAFLD, and strong evidence of genetic colocalization: CYB5A, NT5C, NCAN, TGFBI, DAPK2. TGFBI and DAPK2 are potentially druggable, with all five found to be expressed in both liver and adipose tissues. Pathway analysis indicated the involvement of NT5C, CYB5A, NCAN in Reactome pathway R-HSA-1430728 reflecting cellular energy metabolism, including mitochondrial lipid metabolism, which is strongly implicated with NAFLD. Conclusion We identified a prioritised set of five plasma proteins influencing NAFLD, including druggable proteins informing de novo and ongoing drug development programs. Figure: MR effect estimates of proteins on NAFLD A: Effect of each protein on NAFLD. Each point represents a protein, effect estimates are reported in log(odds ratio) and statistical significance in -log(p-value). Coloured points represent proteins passing the multiplicity-corrected p-value threshold B: MR effect estimates of prioritised proteins. Estimates are reported in log(odds ratio). Proteins are annotated according to their druggability based on the British National Formulary and ChEMBL

Structural variants in linkage disequilibrium with GWAS-significant SNPs

With the recent expansion of structural variant identification in the human genome, understanding the role of these impactful variants in disease architecture is critically important. Currently, a large proportion of genome-wide-significant genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are functionally unresolved, raising the possibility that some of these SNPs are associated with disease through linkage disequilibrium with causal structural variants. Hence, understanding the linkage disequilibrium between newly discovered structural variants and statistically significant SNPs may provide a resource for further investigation into disease-associated regions in the genome. Here we present a resource cataloging structural variant-significant SNP pairs in high linkage disequilibrium. The database is composed of (i) SNPs that have exhibited genome-wide significant association with traits, primarily disease phenotypes, (ii) newly released structural variants (SVs), and (iii) linkage disequilibrium values calculated from unphased data. All data files including those detailing SV and GWAS SNP associations and results of GWAS-SNP-SV pairs are available at the SV-SNP LD Database and can be accessed at ‵https://github.com/hliang-SchrodiLab/SV_SNPs. Our analysis results represent a useful fine mapping tool for interrogating SVs in linkage disequilibrium with disease-associated SNPs. We anticipate that this resource may play an important role in subsequent studies which investigate incorporating disease causing SVs into disease risk prediction models.