SEARCH · bioRxiv Science

Results for “genetics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58Linked to original sources

bioRxiv2023

Assessing the potential of germplasm collections for the management of genetic diversity: the case of the French National Cryobank

Through a combination of selective pressure and genetic drift, there has been a notable erosion of genetic diversity in domesticated animal populations. In response, many countries, including France, have developed gene banks in order to conserve reproductive genetic material. Cryopreserved resources can potentially be used to manage the genetic diversity of populations, but this opportunity is seldom exploited. As an initial step toward this goal, we describe here a methodology for the characterization of the genetic diversity of cryopreserved collections. Using the example of the French National Cryobank, this study employs newly proposed biodiversity metrics to conduct a detailed assessment of the status of collections for six livestock species: cattle, sheep, goat, horse, donkey, and pig. Both the amount of resources available and their characteristics varied among species and/or breeds. Overall, breeds with a large commercial distribution had more donors in the collection than local breeds, while the number of doses available was mainly determined by the physiology of the species. An adapted version of the Gini-Simpson equitability index revealed an unbalanced number of donors between breeds for some species. Similarly, estimates of effective donor numbers (De) highlighted the unequal distribution of donors within a breed. Finally, we developed a new index of diversity impact (IDI) to assess the potential of a collection to reintroduce genetic diversity in contemporary populations. The IDI was calculated on the basis of pedigree data for 17 breeds of three livestock species, pig, sheep, and cattle, which differed in both population size and management program. IDI values are negative when the use of cryoconserved sires would decrease the overall kinship of the current population and positive when it would increase it, enabling the most interesting donors to be chosen for immediate use. Negative (favorable) IDI values 41 were found for both local breeds as well as for commercial populations. In general, older collections exhibited better IDI values but recently collected donors could also be useful for populations undergoing strong selection. Within a breed, IDI can be computed individually and thus be used to select the best sires for a given objective. In the absence of pedigree data, IDI values could also be calculated on the basis of marker genotypes. Overall, this study proposes a framework for the assessment of germplasm collections in the service of various objectives. Compared to FAO indicators motivated by breed reconstitution, the Gini-Simpson and De indices can help to plan sampling more efficiently, whereas IDI can guide donor selection in order to manage the diversity of existing populations. These indicators can be calculated at regular intervals to support the planning and management of collections at national and international levels and help population managers to exploit the resources currently available.

genetics↗

bioRxiv2023

Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions

Protein quantitative trait loci (pQTLs) are an invaluable source of information for drug target development as they provide genetic evidence to support protein function, suggest relationships between cis- and trans-associated proteins, and link proteins to disease where they collocate with genetic risk loci for clinical endpoints. Using the recently released Olink proteomics data for 1,463 proteins measured in over 54,000 samples of the UK Biobank we identified and replicated 4,248 associations with 2,821 ratios between protein levels (rQTLs) where the strengths of association at known pQTL loci increased by up to several hundred orders of magnitude. We attribute this increase in statistical power (p-gain) to accounting for genetic and non-genetic variance shared by the two proteins in the ratio pair. Protein pairs with a significant p-gain were 7.6-fold enriched in known protein-protein interactions, suggesting that their ratios reflect biological links between the implicated proteins. We then conducted a GWAS on the 2,821 ratios and identified 2,527 novel rQTLs, increasing the number of discovered genetic signals compared to the original protein-only GWAS by 24.7%. At examples we demonstrate that this approach can identify novel loci of clinical relevance, support causal gene identification, and reveal complex networks of interacting proteins. Taken together, our study adds significant value to the genetic insights that can be derived from the UKB proteomics data and motivates the wider use of ratios in large scale GWAS.

genetics↗

bioRxiv2023

Genetic Association of SLC47A1 Gene Variant (17:19571562C >T) and Bioinformatics Analyses of MATE1 Protein in Chronic Kidney Disease Patients of Pakistani Origin

Chronic Kidney Disease (CKD) is a serious human threat worldwide which is associated with a number of environmental, clinical and genetic factors that affect serum creatinine (SCr) and glomerular filtration rate (eGFR). One of the best biochemical and genetic markers to study renal functioning is SLC47A1 which encodes MATE1 protein and can be a promising target to study its genetic causes, subject protein is a cationic transporter involved in the regulation of creatinine and urea levels in the blood which may cause toxicity and renal disorders. Hence, the current study focused to interrogate the putative association of SLC47A1 gene variant (17:19571562C>T) in 75 individuals (cases=50, controls=25) of Pakistani origin using ARMS-PCR genotyping, out of which 10 were found heterozygous, 57 homozygous wild-type and 08 homozygous mutants. PLINK data analysis toolset was used which manifested that sampled population complies the Hardy-Weinberg Equilibrium by {chi}2 (2, N = 75) =, p = 4.756 x 10-005, similarly the Chi-square statistics p = 0.03274 along with odds-ratio of 3.244 showing a significant genotypic association with the subject phenotype indicating that mutant allele is almost 3-times more prevalent in cases vs control cohorts with an alternative allele frequency of 0.22 and 0.08 in CKD patients and healthy controls respectively. Besides, few of the bioinformatics tools i.e., ProtParam, PsiPred, PDB-RCSB, Motif Scan, CTU-TMHMM-2.0, ScanProsite, PRmePRed, GPS PAIL2.0, NetOGlyc4.0, NetPhos3, SIFT, Polyphen-2 and STRING were also used for the prediction of different parameters of both the wild-type and mutant MATE1 proteins such as physiochemical properties, secondary structure prediction, 3-D structure, protein conserved domains, transmembrane structures, posttranslational modifications, SNP prediction/amino acid substitution, pathogenicity and protein-protein interaction networks. Current preliminary genotyping provided an insight into the association of the aforementioned genetic variant with CKD to estimate the genetic risks along with prediction of its functional effects in the system biology context which may be considered for developing new targeted drugs along with genetic counselling initiatives to evade this peril in the population.

genetics↗

bioRxiv2023

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This missing heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

genetics↗

bioRxiv2023

Partitioning heritability using single-cell multi-omics identifies a novel macrophage subpopulation conveying genetic risks of coronary artery disease.

BackgroundCoronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. While over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types like macrophages. MethodsWe utilized single-cell RNA-seq (scRNA-seq) and single-cell multi-omics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis--the formation of lipid-laden foam cells. Meta-analysis of scRNA-seq datasets from 26 human plaque samples was undertaken to provide a comprehensive atlas of lesional macrophages and to correlate subpopulations in vivo and ex vivo. The genetic risk levels of CAD were assessed by partitioning disease heritability across different macrophage subpopulations. ResultsWe identified a novel macrophage subpopulation, termed lipid-handling macrophages, both ex vivo and in vivo, and identified associated marker genes, transcription regulators, and functional pathways. 18,782 cis-regulatory elements were identified by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD GWAS data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of lipid-handling macrophages. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-beta in the causal mechanisms at this locus. ConclusionsOur results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key sub-population through which genetic variants actively influence disease. These findings provide an unbiased framework for functional fine-mapping of GWAS results using single-cell multi-omics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

genetics↗

bioRxiv2023

A fine-scale genetic map of the Japanese population

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 10,092,573 bi-allelic high-quality autosomal markers segregating among 150 unrelated Japanese individuals. These individuals genotypes were determined by high-coverage (30x) whole-genome sequencing, and the genotype quality was carefully controlled by using their parents and offsprings genotypes. The pedigree information was also utilized for haplotype phasing. The resulting genome-wide recombination rate profiles were concordant with those of the HapMap 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.

genetics↗

bioRxiv2023

Heritability within groups is uninformative about differences among groups: cases from behavioral, evolutionary, and statistical genetics

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give--directly or indirectly--some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the "between-group heritability" sometimes invoked in behavior genetics, the statistic PST used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

genetics↗

bioRxiv2023

Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis, the bacillus that causes tuberculosis (TB), infects 2 billion people across the globe, and results in 8-9 million new TB cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. We investigated the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using clinical indicators of disease, granuloma histopathological features, and immune response traits identified five new loci on mouse chromosomes 1, 2, 4, 16 and three previously identified loci on chromosomes 3 and 17. Quantitative trait loci (QTLs) on chromosomes 1, 16, and 17, associated with multiple correlated traits and had similar patterns of allele effects, suggesting these QTLs contain important genetic regulators of responses to M. tuberculosis. To narrow the list of candidate genes in QTLs, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks, generating functional scores. The scores were then used to rank candidates for each mapped trait in each locus, resulting in 11 candidates: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Importantly, all 11 candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling. Further, all candidates contain single nucleotide polymorphisms (SNPs), and some but not all SNPs were predicted to have deleterious consequences on protein functions. Multiple methods were used for validation including (i) a statistical method that showed Diversity Outbred mice carrying PWH/PhJ alleles on chromosome 17 QTL have shorter survival; (ii) quantification of S100A8 protein levels, confirming predicted allele effects; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and new functionally relevant gene candidates that may be major regulators of granuloma necrosis and acute inflammation in pulmonary TB.

genetics↗

bioRxiv2024

The genetic landscape, gene flow and adaptation in Ladakh highlanders of trans-Himalayas

Trans-Himalayan Ladakh has witnessed complex cultural movements and demographic changes since the Neolithic period, which is still continue despite the harsh, inhospitable and cold climate. Although geographically isolated from mainland South Asia, Ladakh has historic trade routes and is well connected and accessible to travelers from Tibet and Central Asia. Despite its rich cultural heritage, a detailed description of the genetic landscape of the Ladakh region is completely lacking, particularly with regard to genome-wide analysis and larger sample sizes. Therefore, in the current study, we genotyped 80 individuals from Kargil and Leh districts of the Union Territory of Ladakh, India. Here, we performed a comprehensive genetic analysis based on allele frequency and haplotype sharing. Our analysis revealed the presence of two distinct genetic lineages in the region with quite distinct genetic composition. The population of Leh Region is more similar to East Asian and Southeast Asian populations. In contrast, the population of the Kargil Region (LDKLA) is more similar to Indo-European populations. Demographic modeling suggests that the Leh group shares a genetic history with Tibetans, while the Kargil group showed great affinities with Kashmiri Muslims, Gujjars and Nepalese Brahmins, and both showed recent admixture. Both groups have experienced a founder event around during 11th to 22nd generations ago, the duration of which coincided with the Mughal invasion. The genome-wide scan for a signal of positive selection revealed genetic signatures of high-altitude adaptation (EPAS1 and ELMO2) in the Leh population, while in the Kargil population the key gene signatures were associated with immunity and female fertility.

genetics↗

bioRxiv2024

Distinct genetic pathways to music enjoyment

Humans engage with music for various reasons that range from emotional regulation and relaxation to social bonding. While there are large inter-individual differences in how much humans enjoy music, little is known about the origins of those differences. Here, we disentangled the genetic factors underlying such variation. We collected behavioural data on several facets of music reward sensitivity, music perceptual ability, and general reward sensitivity from a large sample of Swedish twins (N = 9,169). We found that genetic factors substantially explain variance in music reward sensitivity above and beyond genetic influences shared with music perception and general reward sensitivity. Furthermore, multivariate analyses showed that genetic influences on the different facets of music reward sensitivity are partly distinct, uncovering distinct pathways to music enjoyment and different patterns of genetic associations with objectively assessed music perceptual abilities. These results paint a complex picture in which partially distinct sources of genetic variation contribute to different aspects of musical enjoyment and open up new possibilities for using inter-individual differences to gain insights into the biology of a key aspect of human behaviour.

genetics↗

bioRxiv2024

Principal component analysis revisited: fast multi-trait genetic evaluations with smooth convergence

A cornerstone in breeding and population genetics is the genetic evaluation procedure, needed to make important decisions on population management. Multivariate mixed model analysis, in which many traits is considered jointly, utilizes genetic and environmental correlations between traits to improve the accuracy. However, the number of parameters in the multi-trait model grows exponentially with the number of traits which reduces its scalability. Here, we suggest using principal component analysis (PCA) to reduce the dimensions of the response variables, and then using the computed principal components (PC) as separate responses in the genetic evaluation analysis. As PCs are orthogonal to each other, multivariate analysis is no longer needed and separate univariate analyses can be performed instead. We compared the approach to traditional multivariate analysis in terms of computational requirement and rank lists according to predicted genetic merit on two forest tree datasets with 22 and 27 measured traits respectively. Obtained rank lists of the top 50 individuals were in good agreement. Interestingly, the required computational time of the approach only took a few seconds without convergence issues, unlike the traditional approach which required considerably more time to run (seven and ten hours respectively). Our approach can easily handle missing data and can be used with all available linear mixed models software as it does not require any specific implementation. The approach can help to mitigate difficulties with multi-trait genetic analysis in both breeding and wild populations.

genetics↗

bioRxiv2024

Trait genetic architecture and population structure determine model selection for genomic prediction in natural Arabidopsis Thaliana populations.

Genomic prediction applies to a wide range of agronomically relevant traits, with distinct ontologies and genetic architectures. Selecting the most appropriate model for the distribution of genetic effects and their associated allele frequencies in the training population is crucial. Linear regression models are often preferred for genomic prediction. However, linear models may not suit all genetic architectures and training populations. Machine Learning approaches have been proposed to improve genomic prediction owing to their capacity to capture complex biology including epistasis. However, the applicability of different genomic prediction models, including non-linear/non-parametric approaches, have not been rigorously assessed across a wide variety of plant traits in natural outbreeding populations. This study evaluates genomic prediction sensitivity to trait ontology and the impact of population structure on model selection and prediction accuracy. Examining 36 quantitative traits measured for 1000+ natural genotypes of the model plant Arabidopsis thaliana, we assessed the performance of penalised regression, random forest, and multilayer perceptron at producing genomic predictions. Regression models were generally the most accurate, except for biochemical traits where random forest performed best. We link this result to the genetic architecture of each trait - notably that biochemical traits have simpler genetic architecture than macroscopic traits. Moreover, complex macroscopic traits, particularly those related to flowering and yield, were strongly correlated to population structure, while molecular traits were better predicted by fewer, independent markers. This study highlights the relevance of machine learning approaches for simple molecular traits and underscores the need to consider ancestral population history when designing training samples. Article summaryMachine learning and linear models were tested for genomic prediction of multiple traits in the model plant Arabidopsis thaliana. We associate the performance of genomic prediction models to trait ontology, finding machine learning approaches applicable to biochemical traits, and linear models best for macroscopic traits. We link this result to the genetic architecture of each trait and patterns of selection in the association panels ancestral population, thus underscoring the relevance of these two sensitivities to genomic prediction in plant breeding.

genetics↗

bioRxiv2024

Temporal and spatial population genetic variation in Chilean jack mackerel, Trachurus murphyi

The Trachurus murphyi have been studied for population genetic structure for decades, identifying only one large population across the South Pacific Ocean. Although all these studies have extensively examined the spatial genetic pattern, there remains a gap in understanding the potential role of temporality. Our study aims to elucidate temporal and spatial genetic patterns in T. murphyi populations in the South Pacific Ocean, examining genetic composition across seasons, including feeding and spawning seasons, where the latter was not previously investigated. Using 10 microsatellite loci, our study confirms a consistent and stable population genetic pattern in T. murphyi across its geographic distribution, observed over multiple years and seasons. Furthermore, we identify potential genetic markers for monitoring variability in the species.

genetics↗

bioRxiv2024

Major changes in gene expression between strains of diapausing and non-diapause spruce budworm despite little evidence of genetic divergence

To survive harsh winter conditions, many insects enter a state of dormancy called diapause which allows them to withstand extreme temperatures and lack of food. When diapause is induced, insects enter a state of arrested development and low metabolic rate. Because diapause and environmental conditions are closely linked, variation within and between species in diapause induction, depth, and duration is extremely common. Studies investigating the genetic underpinnings of diapause tend to focus on either different populations and/or environmental variation, which runs the risk of confounding the genetic signal either due to isolation-by-distance or variation in environmental conditions. We use the eastern spruce budworm (Choristoneura fumiferana) to circumvent these issues and investigate within-population variation in diapause. C. fumiferana usually diapauses as second instar larvae, but a small subset of individuals does not diapause and instead continues through development. This non-diapause phenotype can be selected to create a non-diapause strain. Here, we present a chromosome-level assembly of such a non-diapause strain, and compare it to an earlier published genome assembly of the diapause strain. We did not find evidence of major chromosome rearrangements, indicating that the genetic variation between strains is likely small and multi-genic. Gene expression comparisons between the strains indicate major gene expression changes, where genes associated with glycolysis and environmental signaling processing increase in expression in the diapause strain. Lastly, we found that gene expression diverges halfway through the first instar, and not before, indicating that the signal to induce diapause happens early in the first instar. Significance statementThe ability to enter diapause is a crucial adaptation for many insects to survive cold winters. The genetic underpinnings of the diapause phenotype has been the target of many investigations. However, many of these studies focus on variation in diapause on between geographic regions or changes in environmental conditions, which can lead to confounding factors. Here, we focus on within-population variation in diapause in the eastern spruce budworm, with two strains established from the same source population that vary in their ability to induce diapause. We present a chromosome level assembly of the non-diapause strain, and report that genetic changes between the diapause and the non-diapause line are likely small and involve multiple loci. Gene expression differences were extensive, and mostly associated with mechanisms to avoid freezing. This work sets the stage to further investigate how minimal genetic changes can lead to large-scale phenotypic changes as impactful as diapause.

genetics↗

bioRxiv2024

Widespread genetic testing control inherited polycystic kidney disease in cats

A genetic variant of PKD1 in cats, which causes polycystic kidney disease (PKD), is a target for direct-to-consumer genetic testing to assess PKD risk; however, its effect on genetic structure in cat populations remains unexplored. Therefore, in this study, we aimed to examine the changes in feline PKD and the PKD1 variant across breeds and over time using a large dataset of 110,325 insured and 61,968 genetic-tested cats from 14 breeds. Results revealed that the PKD1 variant frequency significantly decreased, with a reduction of 42.6% before 2019 and after 2022. Systematic genomic analysis revealed no differences in genetic structure or inbreeding levels. The effective population size of cats with the PKD1 variant decreased between points. Overall, these findings highlight the potential of direct-to-consumer genetic testing in promoting more optimized breeding practices and enhancing feline welfare.

genetics↗

bioRxiv2025

A systems genetics approach identifies roles for proteasome factors in heart development and congenital heart defects

Congenital heart defects (CHDs) occur in about 1% of live births and are the leading cause of infant death due to birth defects. While there have been remarkable efforts to pursue large-scale whole-exome and genome sequencing studies on CHD patient cohorts, it is estimated that these approaches have thus far accounted for only about 50% of the genetic contribution to CHDs. We sought to take a new approach to identify genetic causes of CHDs. By combining analyses of genes that are under strong selective constraint along with published embryonic heart transcriptomes, we identified over 200 new candidate genes for CHDs. We utilized protein-protein interaction (PPI) network analysis to identify a functionally-related subnetwork consisting of known CHD genes as well as genes encoding proteasome factors, in particular POMP, PSMA6, PSMA7, PSMD3, and PSMD6. We used CRISPR screening in zebrafish embryos to preliminarily identify roles for the PPI subnetwork genes in heart development. We then used CRISPR to create new mutant zebrafish strains for two of the proteasome genes in the subnetwork: pomp and psmd6. Phenotypic analyses confirm critical roles for pomp and psmd6 in heart development. In particular, we find defects in myocardial cell shapes and in outflow tract development in pomp and psmd6 mutant zebrafish embryos, and these phenotypes have been observed in other zebrafish CHD-gene mutants. Our study provides a novel systems genetics approach to further our understanding of the genetic causes of human CHDs. Author SummaryCongenital heart defects (CHDs) are birth defects resulting in the abnormal structure and function of the heart. Genetic mutations are a significant cause of CHDs. Many studies have used genome sequencing of CHD patients and their families to gain knowledge of the mutations that cause CHDs. However, these studies have only found about 50 percent of the genes involved in CHDs. Here, we take a new approach to identifying genes that are required for heart development and that may cause CHDs, generating a list of over 200 candidate genes. Using multiple data systems, including human exome sequences, mouse transcriptomes, and protein-protein interactions, we identify a small group of related potential CHD genes that includes multiple genes encoding proteasome factors. These factors are known to be important for assembling the proteasome, a large molecular machine that eliminates unneeded or damaged proteins from the cell, but which has not been shown to contribute to CHD. We use a CRISPR-based approach in zebrafish to specifically eliminate some of these candidate genes and reveal new roles for proteasome genes in heart development. We show that loss of proteasome gene functions leads to zebrafish heart defects that resemble those seen in other zebrafish CHD-gene mutants. This study shows that a proteasome gene family contributes to heart development, advancing our understanding of the causes of CHDs. By increasing our understanding of the genetic causes of CHDs, our work should lead to better screening, more accurate diagnoses, and, ultimately, better treatments for these disorders.

genetics↗

bioRxiv2025

Genetic diversity and conservation insights for the baobab Adansonia suarezensis in northern Madagascar: a whole genome SNP analysis

Madagascar is home to seven unique baobab species in the genus Adansonia, all threatened by climate change, habitat destruction, and deforestation. Previous studies have highlighted the vulnerability of baobabs, particularly A. suarezensis, without delving into the specific genetic structure of its populations. In this paper we examine the genetic structure, diversity, and connectivity of seven populations of A. suarezensis, using genome-wide Single Nucleotide Polymorphism (SNP) data. The results revealed significant genetic differentiation between inland and coastal populations, with the Mahory population forming a distinct genetic cluster characterized by high heterozygosity but low polymorphism, indicative of historical bottlenecks. In contrast, northern populations showed greater admixture and gene flow but higher inbreeding coefficients in coastal regions, such as Ambilo and Cap dAmbre, suggesting localized inbreeding depression. Additionally, historical climatic shifts and potential anthropogenic dispersal are explored as contributing factors to the current genetic patterns. This study highlights the importance of understanding genetic dynamics for conservation, emphasizing habitat restoration and targeted management strategies to preserve the evolutionary potential of A. suarezensis and ensure its long-term survival.

genetics↗

bioRxiv2025

Genetic basis of partner choice

Previous genetic studies of human assortative mating have primarily focused on searching for its genomic footprint but have revealed limited insights into its biological and social mechanisms. Combining insights from the economics of the marriage market with advanced tools in statistical genetics, we perform the first genome-wide association study (GWAS) on a latent index for partner choice. Using 206,617 individuals from four global cohorts, we uncover phenotypic characteristics and social processes underlying assortative mating. We identify a broadly robust genetic component of the partner choice index between sexes and several countries and identify its genetic correlates. We also provide solutions to reduce assortative mating-driven biases in genetic studies of complex traits by conditioning GWAS summary statistics on the genetic associations with the latent partner choice index.

genetics↗