SEARCH · Search bioRxiv

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 55 records · Page 3Linked to original sources

bioRxiv2021

Assessment of Genetic Diversity and Population Genetic Structure of Santalum album L. in India by Genic and Genomic SSR markers

Sandalwood (Santalum album L.) is highly valued aromatic tropical tree. It is known for its high quality heartwood and oil. In this study, 39 genic and genomic SSR markers were used to analyze the genetic diversity and population structure of 177 S. album accessions from 14 populations of three states in India. High genetic diversity was observed in terms of number of alleles 127, expected heterozygosity (He) ranged from 0.63-0.87 and the average PIC was 0.85. The selected population had relatively high genetic diversity with Shannons information index (I) >1.0. 0.02 mean coefficient of genetic differentiation (FST) and 10.55 gene flow were observed. AMOVA revealed that 92% of the variation observed within individuals. Based on cluster and Structure result, individuals were not clustered as per their geographical origin. Furthermore the clusters were clearly distinguished by principal component analysis analysis and the result revealed that PC1 reflected the moderate contribution in genetic variation (6%) followed by PC2 (5.5%). From this study, high genetic diversity and genetic differentiation was found in S. album populations. The genetic diversity information of S. album populations can be used for selection of superior genotypes and germplasm conservation to promote the tree improvement of S. album populations.

molecular biology↗

bioRxiv2022

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

Polygenic risk score (PRS) has been widely used to identify the high-risk individuals from the general population, which would be helpful for disease prevention and early treatment. Many methods have been developed to calculate PRS by weighted aggregating the phenotype-associated risk alleles from genome-wide association studies. However, only considering genetic effects may not be sufficient for risk prediction because the disease risk is not only related to genetic factors but also non-genetic factors, e.g., diet, physical exercise et al. But it is still a challenge to integrate these genetic and non-genetic factors into a unified machine learning framework for disease risk prediction. In this paper, we proposed PRSIMD (PRS Integrating Multi-source Data), a machine learning model that applies posterior regularization to integrate genetic and non-genetic factors to improve disease risk prediction. Also, we applied Mendelian Randomization analysis to identify the causal non-genetic risk factors for the selected diseases. We applied PRSIMD to predict type 2 diabetes and coronary artery disease from UK Biobank and observed that PRSIMD was significantly better than the methods to calculate PRS including p-value threshold (P+T), PRSice2, SBLUP, DMSLMM, and LDpred2. In addition, we observed that PRSIMD achieved the better predictive power than the composite risk score.

bioinformatics↗

bioRxiv2025

A global genetic interaction map of a human cell reveals conserved principles of genetic networks

We generated a genome-scale, genetic interaction network from the analysis of more than 4 million double mutants in the haploid human cell line, HAP1. The network maps [~]90,000 genetic interactions, including thousands of extreme synthetic lethal and genetic suppression interactions. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes, pathways, biological processes, and cellular compartments. Comparative analyses showed that general principles of genetic networks are conserved from yeast to human cells. A genetic interaction network mapped in a single genetic background complements the DepMap gene co-essentiality network, recapitulating many of the same biological connections and also capturing unique functional information to reveal roles of uncharacterized genes and molecular determinants of specific cancer cell line genetic dependencies.

systems biology↗

bioRxiv2017

Genetic landscapes reveal how human genetic diversity aligns with geography

Geographic patterns in human genetic diversity carry footprints of population history1,2 and provide insights for genetic medicine and its application across human populations3,4. Summarizing and visually representing these patterns of diversity has been a persistent goal for human geneticists5-10, and has revealed that genetic differentiation is frequently correlated with geographic distance. However, most analytical methods to represent population structure11-15 do not incorporate geography directly, and it must be considered post hoc alongside a visual summary. Here, we use a recently developed spatially explicit method to estimate \"effective migration\" surfaces to visualize how human genetic diversity is geographically structured (the EEMS method16). The resulting surfaces are \"rugged\", which indicates the relationship between genetic and geographic distance is heterogenous and distorted as a rule. Most prominently, topographic and marine features regularly align with increased genetic differentiation (e.g. the Sahara desert, Mediterranean Sea or Himalaya at large scales; the Adriatic, interisland straits in near Oceania at smaller scales). In other cases, the locations of historical migrations and boundaries of language families align with migration features. These results provide visualizations of human genetic diversity that reveal local patterns of differentiation in detail and emphasize that while genetic similarity generally decays with geographic distance, there have regularly been factors that subtly distort the underlying relationship across space observed today. The fine-scale population structure depicted here is relevant to understanding complex processes of human population history and may provide insights for geographic patterning in rare variants and heritable disease risk.

evolutionary biology↗

bioRxiv2019

Connecting micro and macroevolution using genetic incompatibilities and natural selection on additive genetic variance

Testing whether local adaptation and intrinsic reproductive isolation share a genetic basis can reveal important connections between adaptation and speciation. Local adaptation arises as advantageous alleles spread through a population, but whether these same advantageous alleles fail on the genetic backgrounds of other populations remains largely unknown. We used a quantitative genetic breeding design to produce a late generation (F4) recombinant hybrid population by equally mating amongst four contrasting ecotypes of a native Australian daisy for four generations. We tracked fitness across generations and measured morphological traits in the glasshouse, and used a reciprocal transplant to quantify fitness in all four parental habitats. In the glasshouse, plants in the second generation showed a reduction in fitness as a loss of fertility, but this was fully recovered in the following generation. The F4 hybrid lacked extreme phenotypes present in the parental ecotypes, suggesting that genes reducing hybrid fitness were linked to traits specific to each ecotype. In the natural habitats, additive genetic variance for fitness was greatest for habitats that showed stronger native-ecotype advantage, suggesting that a loss of genetic variation present in the parental ecotypes were adaptive in the natural habitats. Reductions in genetic variance for fitness were associated with a loss of ecological trade-offs previously described in the parental ecotypes. Furthermore, natural selection on morphological traits differed amongst the parental habitats, but was not predicted to occur towards the morphology of the parental ecotypes. Together, these results suggest that intrinsic reproductive isolation removed adaptive genetic variation present in the parental ecotypes. The evolution of these distinct ecotypes was likely governed by genetic variation that resulted in both ecological trade-offs and intrinsic reproductive isolation among populations adapted to contrasting environments.

evolutionary biology↗

bioRxiv2019

Riverscape genetics in brook lamprey: genetic diversity is less influenced by river fragmentation than by gene flow with the anadromous ecotype

Understanding the effect of human induced landscape fragmentation on gene flow and evolutionary potential of wild populations has become a major concern. Here, we investigated the effect of riverscape fragmentation on patterns of genetic diversity in the freshwater resident brook lamprey (Lampetra planeri) that has a low ability to pass obstacles to migration. We also tested the hypotheses of i) asymmetric gene flow following water current and ii) admixture with the closely related anadromous L. fluviatilis ecotype having a positive effect on L. planeri genetic diversity. We genotyped 2472 individuals, including 225 L. fluviatilis, sampled in 81 sites upstream and downstream from barriers to migration, in 29 West-European rivers. Linear modelling revealed a strong positive relationship between the distance to the source and genetic diversity, consistent with expected patterns of decreased gene flow into upstream populations. However, the presence of anthropogenic barriers had a moderate effect on spatial genetic structure. Accordingly, we found evidence for downstream-directed gene flow, supporting the hypothesis that barriers do not limit dispersal following water flow. Downstream L. planeri populations in sympatry with L. fluviatilis displayed consistently higher genetic diversity. We conclude that genetic drift and slight downstream gene flow mainly drive the genetic make up of upstream L. planeri populations whereas admixture between ecotypes maintains higher levels of genetic diversity in L. planeri populations sympatric with L. fluviatilis. We discuss the implications of these results for the design of conservation strategies of lamprey, and other freshwater organisms with several ecotypes, in fragmented dendritic river networks.

evolutionary biology↗

bioRxiv2026

Genetic drift decouples Fisherian trait-preference coevolution from genetic correlations in finite populations

A central mechanism of sexual selection theory is Fisher's process, which refers to the coevolution of male traits and female preferences, in which preference is indirectly selected through genetic association with male trait alleles built up via mate choice. However, empirical studies often fail to detect strong trait-preference genetic correlations, raising doubts about the importance of Fisherian selection in nature. Notably, the theoretical expectation that genetic correlations are essential for trait-preference coevolution through Fisherian selection derives largely from models assuming infinitely large populations, whereas real populations are finite. Using population genetic models, I show that interactions between Fisherian selection and genetic drift can fundamentally decouple trait-preference genetic correlations from the evolution of male traits and female preferences. Genetic drift generally reduces expected trait-preference correlations but simultaneously promotes the expected increase in female preference frequency beyond deterministic predictions. Consequently, in populations of realistic sizes, trait-preference correlations may often be weak or even negative, particularly when recombination among trait and preference loci is infrequent, but substantial trait-preference coevolution can still occur. Therefore, the strength of genetic correlations may be an unreliable indicator of the extent of trait and preference coevolution, offering a potential resolution to a longstanding dilemma in sexual selection theory.

evolutionary biology↗

bioRxiv2016

Local genetic correlation gives insights into the shared genetic architecture of complex traits

Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions contribute to the genome-wide genetic correlation is currently lacking. Here, we introduce{rho} -HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach only requires GWAS summary data and makes no distributional assumption on the causal variant effects sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 35 complex traits, and identified 27 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 7 genomic regions that contribute to the genetic correlation of 12 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we leverage the distribution of local genetic correlations across the genome to assign putative direction of causality for 15 pairs of traits.

genetics↗

bioRxiv2019

Surprising variation in the outcome of two malaria genetic crosses using humanized mice: implications for genetic mapping and malaria biology

Genetic crosses are most powerful for linkage analysis when progeny numbers are high, when parental alleles segregate evenly and, for hermaphroditic organisms, when numbers of inbred progeny are minimized. We previously developed a novel genetic crossing platform for the human malaria parasite Plasmodium falciparum, an obligately sexual, hermaphroditic protozoan, using mice carrying human hepatocytes (the human liver-chimeric FRG NOD huHep mouse) as the vertebrate host. Here we examine the statistical power of two different genetic crosses - (1) between a laboratory parasite (NF54) of African origin and a patient-derived Asian parasite, and (2) between two sympatric patient-derived Asian parasites. We generated >140 unique recombinant clones over a 12-month period from the four parental genotypes, doubling the number of unique recombinant progeny generated in the previous 30 years. Both crosses show bi-parental inheritance of plastid markers amongst recombinant progeny, in contrast to previous crosses (conducted using chimpanzee hosts) which carried single dominant plastid genotypes. Both crosses show distinctive segregation patterns. The allopatric African/Asian cross has minimal levels of inbreeding (2% of clonal progeny are inbred) and extreme skews in marker segregation, while in the sympatric Asian cross, inbred progeny predominate (66% of clonal progeny are inbred) and parental alleles segregate evenly. Using simulations, we demonstrate that these progeny arrays (particularly the sympatric Asian cross) have excellent power to map large-effect mutations to a 31 kb interval and can capture complex, epistatic interactions that were far beyond the capacity of previous malaria crosses to detect. The extreme segregation distortion in the allopatric African/Asian cross erodes power to detect linkage in several genome regions, but the repeatable distortions observed offer promising alternative approaches to identifying genes underlying traits of interest. These crosses show surprising variation in marker segregation, nevertheless, the increased progeny numbers improve our ability to rapidly map biomedically important parasite traits. Author SummaryUnderstanding how genome mutations contribute to newly emerging drug resistance in parasites like Plasmodium falciparum is important to monitor the spread of drug resistance. This scenario has been playing out in Southeast Asia with the emergence and spread of artemisinin resistance. Here we show that new P. falciparum genetic crosses, using mice carrying human liver cells and infused with human red blood cells (the human liver-chimeric FRG NOD huHep/huRBC mouse), provide an important new tool for understanding complex interactions underlying drug resistance phenotypes. We report two new genetic maps with 84 and 60 unique recombinant progeny, which doubles the number of progeny available from 4 previous P. falciparum genetic crosses. Through extensive simulations we show that with 84 progeny we can find association for a gene that controls only 20% of the variation in a phenotype. We also show that a cross generated from Southeast Asian parasites collected from the same geographic region have unique characteristics not previously observed in P. falciparum genetic crosses. This Southeast Asian cross exhibits even segregation across the genome, unbiased inheritance of mitochondria and apicoplast and higher levels of inbreeding than previously observed.

genetics↗

bioRxiv2020

Extensive genetic diversity in Plasmodium vivax from Sudan and its genetic relationships with other geographical isolates

Plasmodium vivax malaria is a neglected tropical disease in Africa due to low occurrence rates and lack of accurate diagnosis. Recently, there has been a dramatic increase in P. vivax cases in East Africa and reportedly spreading to western countries. This study investigated the geographical origin and genetic diversity of P. vivax in Sudan by 14 microsatellite markers. A total of 113 clinical P. vivax samples were collected from two districts, New Halfa and Khartoum in Sudan. In addition, data from 841 geographical samples retrieved from the database for global genetic analysis were included in the analysis to further the genetic relationships among the P. vivax isolates at regional and worldwide scales. On a regional scale, we observed 91 unique and 8 shared haplotypes amongst the Sudan samples. Such a high genetic diversity compared to other geographical isolates lends support to hypothesis that P. vivax was originated from Africa. On a global scale, as already demonstrated, we observed distinct genetic clustering of P. vivax isolates from Africa, South America, and Asia (including Papua New Guinea and Solomon Island) with limited admixture in all three clusters. The principal component analysis and phylogenetic tree showed similar clustering patterns and highlighted the contribution of the African isolates to the genetic variation observed globally. The East African P. vivax showed similarity with some of the Asian isolates suggesting potential recent introductions. Our results show extensive genetic diversity co-occurring with significant multi-locus linkage disequilibrium, demonstrating the effectiveness of using microsatellite markers to implement effective control measures.

genetics↗

bioRxiv2022

A Genome-Wide Association Analysis of Happiness: Consistent Genetic Effects Across the Lifespan and Across Genetic Ancestries in Multiple Cohorts.

We present a genome-wide association study of a general happiness measure in 118,851 participants from the UK Biobank. Using BOLT-LMM, we identify 3 significant loci with a heritability estimate of 0.8%. Linkage disequilibrium score regression was performed on the big five personality traits finding significant associations with lower neuroticism and higher extraversion and conscientiousness. Using a novel approach, we construct LDpred-inf polygenic risk scores in the Adolescent Brain Cognitive Development (ABCD) cohort and the Add Health cohort. We detected nominally significant associations with several well-being measures in ABCD and significant correlations with a happiness measure in Add Health. Additionally, we tested for associations with several brain regions in a white British subsample of UK Biobank finding significant associations with several brain structure and integrity phenotypes. We demonstrated a genetic basis for general happiness level and brain structure that appears to remain consistent throughout the lifespan and across multiple ancestral backgrounds. Author summaryAt the genetic level, there has been little investigation into whether people may have a baseline happiness level which varies from person to person. Here we perform a genetic analysis in the UK Biobank to identify three genetic loci that associate with general happiness level and preform genetic correlations of our results with the Big Five personality traits, identifying significant correlations with neuroticism, conscientiousness and extraversion. We use the resulting summary statistics to create LDpred-inf polygenic risk scores in UK biobank identifying several brain metrics and regions associate with genetic loading for general happiness level. We also use a novel method to create LDpred-inf polygenic risk scores in two other cohorts, ABCD and Add Health. We found significant correlations with an independent happiness measure in Add Health and nominally significant correlations with several well-being measures in ABCD in both those of European Ancestry and all other ancestries found in these cohorts. We also attempted to replicate our UK Biobank MRi finding in ABCD. We conclude there is evidence that individuals have a general happiness level that is in part genetic which spans across age and ancestry.

genetics↗

bioRxiv2022

A targeted genetic modifier screen in Drosophila uncovers vulnerabilities in a genetically complex model of colon cancer

Kinases are key regulators of cellular signal transduction pathways. Many diseases including cancer are associated with global alterations in protein phosphorylation networks, as a result, kinases are frequent targets of drug discovery efforts. However, target identification and assessment, a critical step in targeted drug discovery which involves identifying essential genetic mediators of disease phenotypes, can be challenging in complex, heterogeneous diseases like cancer where multiple concurrent genomic alterations are common. Drosophila is a particularly useful genetic model system to identify novel regulators of biological processes through unbiased genetic screens. Here, we report two classic genetic modifier screens focusing on the Drosophila kinome to identify kinase regulators in two different backgrounds: KRAS TP53 PTEN APC, a multigenic cancer model that targets four genes recurrently mutated in human colon tumors and KRAS alone, a simpler model that targets one of the most frequently altered pathways in cancer. These screens identified hits that are shared by both models as well as those unique to each one, emphasizing the importance of capturing the genetic complexity of human tumor genome landscapes in experimental models. Our follow-up analysis of two hits from the KRAS only screen suggest that classical genetic modifier screens in heterozygous mutant backgrounds that result in a modest, non-lethal reduction in candidate gene activity in the context of a whole animal --a key goal of systemic drug treatment-- may be a particularly useful approach to identify most rate limiting genetic vulnerabilities in disease models as ideal candidate drug targets.

genetics↗

bioRxiv2023

Genome size, genetic diversity, and phenotypic variability imply the effect of genetic variation instead of ploidy on trait plasticity in the cross-pollinated tree species ofmulberry

Genome size, genetic diversity, and phenotypic vitiation were estimated to develop a model-based population structure and identify ploidy-associated traits of a wide collection of cross-pollinated, highly heterozygous tree species of Morus.Elucidation of genome size (GS), genetic and phenotypic variation is the fundamental aspect of crop improvement programs. Mulberry is a cross-pollinated, highly heterozygous tree eudicot, and comprised of huge ploidy variation with great adaptability across the world. However, because of inadequate information on GS, ploidy-associated traits, as well as the correlation between genetic and phenotypic variation hinder the further improvement of mulberry. In this present research, a core set of 157 germplasm accessions belonging to eight accepted species of Morus including promising functional varieties were chosen to represent the genetic spectrum from the whole germplasm collection. To estimate the GS, accessions were subjected to flow cytometry (FCM) analysis and the result suggested that four different ploidies (2n=2x, 3x, 4x, and 6x) with GS ranging from 0.72{+/-}0.005 (S-30) to 2.89{+/-}0.015 (M. serrata), accounting [~]4.01 fold difference. The predicted polyploidy was further confirmed with metaphase chromosome count. In addition, the genetic variation was estimated by selecting a representative morphologically, diverse population of 82 accessions comprised of all ploidy variations using simple sequence repeats (SSR). The estimated average Polymorphism Information Content (PIC) and expected heterozygosity showed high levels of genetic diversity. Additionally, three populations were identified by the model-based population structure (k=3) with a moderate level of correlation between the populations and different species of mulberry, which imply the effect of genetic variation instead of ploidy on trait plasticity that could be a consequence of the high level of heterozygosity imposed by natural cross-pollination. Further, the correlation between ploidies, especially diploid and triploid with selected phenotypic traits was identified, however, consistency could not be defined with higher ploidy levels (>3x). Moreover, incite gained here can serve as a platform for future omics approaches to the improvement of mulberry traits.

genetics↗

bioRxiv2023

BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases

GWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power, or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT(https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules, and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

genetics↗

bioRxiv2025

Genetic dissection of tissue composition in genetically diverse mouse populations

BackgroundSingle-cell RNAseq data can be harnessed to infer cell-type compositions from spatial or bulk transcriptomes, providing greater biological context underlying systemic tissue or organ dynamics. Here, we used a transcriptome-based systems genetics approach to assess biological factors that influence the cellular composition of tissues, including sex, age and genetic variation in two multiparental mouse resource populations derived from the same 8 parental strains. MethodsWe leveraged publicly available bulk RNAseq data from kidney, heart and bone from 188 Diversity Outbred (DO) mice and male/female pairs from 58 inbred strains of the Collaborative Cross (CC). DO mice were aged 6, 12 or 18 months. We used single-cell RNAseq data from healthy C57BL/6 mice as reference for cell-type decomposition of bulk transcriptomes. We tested for differences in cell type composition based on age and sex. We then performed genetic analysis on the cell-type composition phenotypes, estimating the heritability of cell composition levels, mapping cell composition quantitative trait loci (ccQTL), and assessing candidate gene intermediates of ccQTL based on a genetic mediation approach and support in the literature. ResultsThe heritability of cell-type compositions differed between the inbred CC and outbred DO. Contrasting this, we observed strong and consistent sex differences between the CC and DO. For example, we observed consistency in renal proximal tubule segment 3 (higher in males), T cell abundance in kidney (higher in females), ventricular cardiomyocyte (higher in males) and endocardial cells (higher in females). DO mice exhibited increased levels B and T cells with age, representing an age-related increase in overall renal immune cell content. In heart, DO mice showed decreased ventricular cardiomyocyte composition and increasing endocardial cell content with age. In bone from DO mice, vascular endothelial cells decreased with age and bone marrow stromal cells increased with age. Genome-wide association yielded several cell composition QTLs (ccQTLs) in the three tissues, likely related to known biological phenomena. These include a ccQTL for cardiac fibroblasts in the CC that contains the Frg2 gene family and fibroblast growth factor inhibitor Spry2, and a bone vascular endothelial cell ccQTL in the DO that encompasses 10 genes encoding members of the S100a protein family. In addition, our analysis revealed several ccQTLs for different renal tubular segments, immune cell content, and approximately two thirds of cardiac cell types. ConclusionWe establish a role for transcriptome-inferred cellular states or composition for QTL mapping, which adds further utility to existing resources. Furthermore, differences in heritability and QTL between the CC and DO highlight how variation in allele frequencies and inbred vs outbred genetic backgrounds allow for population-specific genetic effects to be discovered. Several of the renal tubular segment and cardiac cell ccQTLs have narrow confidence regions with the potential to harbor novel biological insight. Overall, we propose that our ccQTL mapping approach can be applied systematically across readily available genetic resource transcriptomes to enable new biological discoveries.

genetics↗

bioRxiv2018

High genetic diversity and presence of genetic structure characterise the Corso-Sardinian endemics Ruta corsica and Ruta lamarmorae (Rutaceae)

Corsica and Sardinia form one of the ten areas with highest biodiversity in the Mediterranean and are considered one of the priority regions for conservation in Europe. In order to preserve the high levels of endemism and biological diversity at different hierarchical levels, knowledge of the evolutionary history and current genetic structure of Corso-Sardinian endemics is instrumental. Microsatellite markers were newly developed and used to study the genetic structure and taxonomic status of Ruta corsica and Ruta lamarmorae, rare endemics of Corsica and Sardinia, respectively, and previously considered a single species. Our analyses identified high levels of genetic variation within each species (P=0.883, He=0.543 for R. corsica; P=0.972, He=0.627 for R. lamarmorae). Intrinsic traits of the species (hermaphroditism, proterandry and polyploidy) and island-dependent factors (i.e. age, origin and history of the islands) might explain the detected high levels of genetic variation. We discovered differentiation between R. corsica and R. lamarmorae, and genetic structure within each species, which are consistent with the observation of low dispersal ability for both species. Our genetic results support the recent taxonomic classification of R. corsica and R. lamarmorae as separate species and suggest that they diverge at only few loci. One R. corsica population (SA) strongly differed from all other studied populations and appeared to be the product of hybridization between the two species in STRUCTURE analyses. Our results provide important insights for the conservation of the two rare endemics. Further genetic analyses are recommended for R. lamarmorae and for population SA (R. corsica).

genetics↗

bioRxiv2019

Very rare pathogenic genetic variants detected by SNP-chips are usually false positives: implications for direct-to-consumer genetic testing

ObjectivesTo determine the analytical validity of SNP-chips for genotyping very rare genetic variants. DesignRetrospective study using data from two publicly available resources, the UK Biobank and the Personal Genome Project. SettingResearch biobanks and direct-to-consumer genetic testing in the UK and USA. Participants49,908 individuals recruited to UK Biobank, and 21 individuals who purchased consumer genetic tests and shared their data online via the Personal Genomes Project. Main outcome measuresWe assessed the analytical validity of genotypes from SNP-chips (index test) with sequencing data (reference standard). We evaluated the genotyping accuracy of the SNP-chips and split the results by variant frequency. We went on to select rare pathogenic variants in the BRCA1 and BRCA2 genes as an exemplar for detailed analysis of clinically-actionable variants in UK Biobank, and assessed BRCA-related cancers (breast, ovarian, prostate and pancreatic) in participants using cancer registry data. ResultsSNP-chip genotype accuracy is high overall; sensitivity, specificity and precision are all >99% for 108,574 common variants directly genotyped by the UK Biobank SNP-chips. However, the likelihood of a true positive result reduces dramatically with decreasing variant frequency; for variants with a frequency <0.001% in UK Biobank the precision is very low and only 16% of 4,711 variants from the SNP-chips confirm with sequencing data. Results are similar for SNP-chip data from the Personal Genomes Project, and 20/21 individuals have at least one rare pathogenic variant that has been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, the overall performance metrics of the SNP-chips in UK Biobank are sensitivity 34.6%, specificity 98.3% and precision 4.2%. Rates of BRCA-related cancers in individuals in UK Biobank with a positive SNP-chip result are similar to age-matched controls (OR 1.28, P=0.07, 95% CI: 0.98 to 1.67), while sequence-positive individuals have a significantly increased risk (OR 3.73, P=3.5x10-12, 95% CI: 2.57 to 5.40). ConclusionSNP-chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation. SUMMARY BOXO_ST_ABSSection 1: What is already known on this topicC_ST_ABSSNP-chips are an accurate and affordable method for genotyping common genetic variants across the genome. They are often used by direct-to-consumer (DTC) genetic testing companies and research studies, but there several case reports suggesting they perform poorly for genotyping rare genetic variants when compared with sequencing. Section 2: What this study addsOur study confirms that SNP-chips are highly inaccurate for genotyping rare, clinically-actionable variants. Using large-scale SNP-chip and sequencing data from UK Biobank, we show that SNP-chips have a very low precision of <16% for detecting very rare variants (i.e. the majority of variants with population frequency of <0.001% are false positives). We observed a similar performance in a small sample of raw SNP-chip data from DTC genetic tests. Very rare variants assayed using SNP-chips should not be used to guide health decisions without validation.

genetics↗

bioRxiv2019

Genetic correlation between background EEG oscillations and genetic generalized epilepsy

ObjectiveParoxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations. MethodsConfounding factors, including the heterogeneous etiology of epilepsies and medication effects hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a GWAS on epilepsy (n=12,803 people with epilepsy and 24,218 controls) with that from a GWAS on background EEG (n=8,425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta and theta. We replicated our findings in an independent epilepsy replication dataset (n=4851 people with epilepsy and 20,428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores and Mendelian randomization analyses. ResultsOur analyses show strong genetic correlations between genetic generalized epilepsy (GGE) with background EEG oscillations, primarily with the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations. SignificanceOur results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic work-up of epilepsy.

genetics↗