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bioRxiv2019

Genetic modifiers of risk and age at onset in GBA associated Parkinson disease and Lewy body dementia

Parkinsons disease (PD) is a genetically complex disorder. Multiple genes have been shown to contribute to the risk of PD, and currently 90 independent risk variants have been identified by genome-wide association studies. Thus far, a number of genes (including SNCA, LRRK2, and GBA) have been shown to contain variability across a spectrum of frequency and effect, from rare, highly penetrant variants to common risk alleles with small effect sizes. Variants in GBA, encoding the enzyme glucocerebrosidase, are associated with Lewy body diseases such as PD and Lewy body dementia (LBD). These variants, which reduce or abolish enzymatic activity, confer a spectrum of disease risk, from 1.4- to >10-fold. An outstanding question in the field is what other genetic factors that influence GBA-associated risk for disease, and whether these overlap with known PD risk variants.\n\nUsing multiple, large case-control datasets, totalling 217,165 individuals (22,757 PD cases, 13,431 PD proxy cases, 622 LBD cases and 180,355 controls), we identified 1,772 PD cases, 711 proxy cases and 7,624 controls with a GBA variant (p.E326K, p.T369M or p.N370S). We performed a genome-wide association study and analysed the most recent PD-associated genetic risk score to detect genetic influences on GBA risk and age at onset. We attempted to replicate our findings in two independent datasets, including the personal genetics company 23andMe, Inc. and whole-genome sequencing data. Our analysis showed that the overall PD genetic risk score modifies risk for disease and decreases age at onset in carriers of GBA variants. Notably, this effect was consistent across all tested GBA risk variants. Dissecting this signal demonstrated that variants in close proximity to SNCA and CTSB (encoding cathepsin B) are the most significant contributors. Risk variants in the CTSB locus were identified to decrease mRNA expression of CTSB. Additional analyses suggest a possible genetic interaction between GBA and CTSB and GBA p.N370S neurons were shown to have decreased Cathepsin B expression compared to controls. These data provide a genetic basis for modification of GBA-associated PD risk and age at onset and demonstrate that variability at genes implicated in lysosomal function exerts the largest effect on GBA associated risk for disease. Further, these results have important implications for selection of GBA carriers for therapeutic interventions.

genetics↗

bioRxiv2019

Shared Genetic Risk between Eating Disorder- and Substance-Use-Related Phenotypes: Evidence from Genome-Wide Association Studies

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use (genetic correlation [rg], twin-based=0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder (MDD). Total sample sizes per phenotype ranged from ~2,400 to ~537,000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg=0.18; false discovery rate q=0.0006), cannabis initiation and AN (rg=0.23; q<0.0001), and cannabis initiation and AN with binge-eating (rg=0.27; q=0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge-eating (rgs=-0.19 to -0.23; qs<0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for MDD loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships between these behaviors.

genetics↗

bioRxiv2019

Inferring the Genetic influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition

Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly non-significant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling (CBPM) has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior. As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of N=3,000 individuals; and test the genetic correlations between brain models and measured IQ in a genetic validation sample of N=13,092 individuals from the UKBiobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single \"candidate\" brain areas. We find that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable than single brain regions (h2=.155-.181) and capture about half of the genetic variance in IQ (rG=.422-.576), while rGs with single brain measures were smaller and non-significant. For the different approaches, LASSO and Ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models. We conclude that multi-system predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.

genetics↗

bioRxiv2019

Detecting Local Genetic Correlations with Scan Statistics

Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to five phenotypically distinct but genetically correlated psychiatric disorders, we identified 49 non-overlapping genome regions associated with multiple disorders, including multiple hub regions showing concordant effects on more than two disorders. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

genetics↗

bioRxiv2019

Sex-specific genetic effects across biomarkers

Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian mixture model, Sex Effects Mixture Model, to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 444 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, CYP3A43, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian Randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.

genetics↗

bioRxiv2025

Microsatellite Analysis of Genetic Differentiation Among Populations of the Malaria Vector, Anopheles funestus, across Mainland Tanzania Reveals Contrasted Patterns of Geographic Isolation and Gene Flow

BackgroundDespite Anopheles funestus s.s. being a highly competent and widespread malaria vector in Africa, its population structure remains largely understudied in many countries, including Tanzania. Herein, we examine the genetic diversity, geographic isolation, and gene flow of An. funestus populations across 10 administrative regions in mainland Tanzania. MethodsWe employed 12 previously used microsatellite DNA markers to describe genetic diversity, isolation by distance, and gene flow patterns among 10 An. funestus s.s. populations (n = 654) and one An. parensis population (n = 28), used as an outgroup, sampled across 10 regions in mainland Tanzania. ResultsOverall, allelic richness (Na) and genetic diversity (HS) did not differ significantly among populations. Although some loci and populations showed significant departures from Hardy-Weinberg equilibrium, the patterns were not indicative of sub-structuring within locations. Pairwise genetic divergence (FST) values indicated clear separation between An. parensis and An. funestus s.s., with values exceeding 0.2, consistent with species-level differentiation. Among An. funestus s.s. populations, the highest divergence was observed between southeastern coastal populations (Mtwara, Ruvuma, Lindi) and inland populations, with FST values up to 0.288. There was no evidence of isolation by distance. Instead, patterns of genetic divergence suggested connectivity across the Rift Valley and heterogeneity among southeastern populations. Neighbour-joining analysis and Bayesian genotype clustering identified three distinct population groups: (i) An. parensis (Dodoma), (ii) a genetically distinct An. funestus s.s. population from Mtwara, and (iii) a more homogeneous cluster comprising the remaining An. funestus s.s. populations. Notably, the Mtwara population appeared highly differentiated, with divergence approaching that between An. Funestus s.s. and An. parensis, supporting its distinctiveness but not undermining the role of An. parensis as an outgroup. ConclusionExcept for the Mtwara population, whose status will need to be clarified through whole genome sequencing, moderate genetic divergence was found among An. funestus s.s. populations across Tanzania, despite geographical separation and the Rift Valley. The observed genetic structure suggests that anthropogenic gene flow may play a key role in shaping population divergence. Future studies should aim to delineate the effects of local adaptation from recent gene flow to further explore these dynamics.

genetics↗

bioRxiv2026

Sex-stratified insights into the genetics of brain volumes in late adulthood

BackgroundThe prevalence, timing and disease course of mental and neurological disorders vary according to sex, yet the neurogenetic mechanisms underlying sex differences in these disorders remain poorly understood. Methods. Here, we explored the genetic architecture of the anatomical volumes of 257 regions of interest across the brain using multivariate genome-wide analyses in 15,740 male and 15,740 female participants from the UK Biobank, matched on age and scan site. ResultsOur findings revealed that the genetics of brain volume is highly similar between females and males in late adulthood. Yet, we found evidence of possible autosomal sex heterogeneity, particularly in the number of brain-volume associated genes, which was higher in females than in males. Variability in the number of identified genes were marked in limbic regions such as the insula, the cingulate cortex, the hippocampus and the amygdala. Conclusion.Overall, our findings contribute to a better understanding of the genetic determinants of brain volumes in males and females. Because neurogenetics may also influence the risk for sex-prevalent brain disorders, the current findings have the potential to facilitate precision medicine approaches in improving prevention strategies and targeted treatments. Plain English summaryMental and neurobiological disorders often differ between females and males. Some disorders are more common in one sex than the other, and symptoms can present or evolve differently. Variation in brain biology, partly shaped by genetic factors, may contribute to these observed patterns. In this study, we investigated genetic factors linked to brain structure in females and males separately. We focused on the volumes of 257 brain regions and analyzed genetic data from more than 30,000 adults from the UK Biobank. Overall, we found that the patterns of association between genetics and brain volumes are largely similar between females and males in late adulthood. At the same time, we observed evidence of sex-dependent patterns, particularly in the number of genes associated with brain volume, which tended to be higher in females than in males. These differences were marked in brain regions involved in the limbic system. Together, our findings improve our understanding of how genetics contribute to brain structure in females and males. HighlightsGenetic influences on brain volumes are largely shared between females and males in late adulthood. Gene-level variability was observed, with a higher number of brain volumes associated genes identified in females than in male, in both multivariate and univariate analyses. This gene-level variability was most pronounced in limbic regions, such as the insular and cingulate cortices.

genetics↗

bioRxiv2026

The genetic architecture of local adaptation is historically contingent

Revealing the genetic basis of local adaptation is a common goal of evolutionary biology, but despite theoretical progress, general expectations for the genetic architecture of local adaptation are still unclear. Theoretical analyses usually model simplified ecologies or simplified genetic architectures of adaptive traits, so the interplay of these factors is missing from our understanding. In this study, we use simulations to explore how the interplay of ecological and genetic parameters influences the evolution and genetic architecture of local adaptation. With these simulations, we ask: i) What are the features of alleles that made the largest contribution to local adaptation, and how are they affected by polygenicity of adaptive traits, migration rates, demographic history, and the spatial pattern of the environment? And ii) does allele age moderate the confounding effect from population structure in genotype-environmental associations (GEA)? We find that the frequency, number, and phenotypic effect size of locally adaptive alleles are sensitive to trait polygenicity and demographic history, and that these factors shape the evolutionary dynamics of local adaptation. We find that population expansions can leave legacies in the genetic architecture of local adaptation, reducing the expected number of adaptive alleles relative to models with constant population size, and this effect is long-lasting. Compared to range expansion, other ecological variables known to affect the genetic basis of local adaptation had limited effects. Finally, allele age moderated the confounding effect of population structure and modified the causal effect of environmental variables on genotypes. Alleles that arose around the time of environmental changes often made large contributions to local adaptation, but young alleles often had the highest false positive rates and were the most common age category. We describe how incorporating allele age and its interactions with population structure and environmental variables may increase the sensitivity and specificity of GEA analysis. Overall, this work demonstrates the critical importance that a species demographic history can have on its genetic architecture of local adaptation.

genetics↗

bioRxiv2026

Genetic population structure and demographic history of Pacific cod in Japanese waters: Implications for stock identification using SNP markers

Pacific cod is a key species in North Pacific fisheries, and its stock assessment and management units are separated according to biological, geographical, and administrative information. Understanding the fine-scale genetic population structure of this species is crucial for effective management, particularly in regions such as Japan, where complex coastal geography and localised fisheries management prevail. Therefore, in this study, we analysed genome-wide single nucleotide polymorphisms (SNPs; 6,035 loci) in 496 individuals of Pacific cod sampled from 33 sites around the Japanese archipelago via genotyping by random amplicon sequencing-direct (GRAS-Di) analysis. Our analyses revealed three major genetic groups: Japanese Broad Range, Northernmost Honshu-Hokkaido (NHH), and Western Sea of Japan groups. These groups exhibited significant genetic differentiation (global FST = 0.056), distinct levels of nucleotide diversity, and group-specific genome-wide patterns of Tajimas D. Moreover, demographic history reconstruction based on whole-genome sequencing of three representative individuals revealed that each genetic group followed distinct demographic trajectories since the last glacial period. Importantly, the NHH group, related to the Mutsu Bay spawning aggregation and previously shown to exhibit strong natal homing in tagging surveys, was genetically identified for the first time in this study. Isolation-by-distance was observed across Japanese waters and within the Japanese Broad Range group, but not within the NHH group, suggesting that gene flow is generally restricted by geographic distance, except within the NHH group. To evaluate the potential for genetic stock identification, we extended a resampling-based cross-validation framework by incorporating outlier detection to assess marker selection strategies. Over 500 background SNPs were required to achieve >90% assignment accuracy for genetic stock identification, whereas only eight or more outlier SNPs showed comparable performance. These findings suggest that carefully selected SNP panels, particularly those including outlier loci, substantially improve stock discrimination. Overall, our study demonstrates the fine-scale genetic structure and demographic history of Pacific cod in Japanese waters and highlights the utility of practical marker strategies for enhancing the biological realism of fisheries assessment and supporting sustainable fisheries management.

genetics↗

bioRxiv2026

Genetic parameters and genotype-by-diet interactions forgrowth traits in Australian black soldier fly larvae: Implicationsfor selective breeding in the circular bioeconomy

Black soldier fly larvae (Hermetia illucens, Diptera: Stratiomyidae; BSFL) efficiently bio- convert organic waste into high-value protein, which has significant potential in domesticated animal feed formulations. BSFL growth and bioconversion potential can be enhanced through selective breeding, which requires accurate estimates of genetic parameters and knowledge of genotype-by-diet (G x D) interactions. However, comprehensive knowledge of G x D interactions is limited, and reports of genetic parameters are sparse across genetic strains and production environments globally. This study estimated heritabilities, dominance effects and genetic correlations for BSFL growth traits and quantified G x D interactions. Phenotypes of 2,097 fifth-instar larvae reared on three diets were recorded, including larval body weight (LBW), length (LL), width (LW), and surface area (LSA). All larvae were genotyped using a custom 6K Allegro SNP panel. Genetic parameters and G x D interactions were estimated by fitting additive and additive-dominance models. Heritabilities for growth traits were low across diets (0.04-0.14), with diet-specific estimates ranging from low to moderate (0.08-0.37). Dominance effects were significant across the traits (0.10-0.19), and genetic correlations were high among growth traits (>0.79), except between LW and LL (0.47). G x D interactions were moderate across diets (-0.06-0.46). Results suggest that moderate to high genetic gain is achievable over a long-term breeding program, given the genetic basis of growth traits and BSF's short generation interval (38-45 days). However, G x D interactions must be considered, either through combined or diet-specific selection strategies, and the significant dominance effects suggest heterosis could accelerate improvement.

genetics↗

bioRxiv2026

Local population genetic structure of Andrena flavipes (Hymenoptera, Andrenidae), a nest-philopatric communal mining bee, reveals isolation-by-distance and high inbreeding

Wild bee populations are declining globally and understanding how species-intrinsic traits like nest-philopatry shape local population genetic structure is increasingly important for their conservation. However, few studies have explored this at local spatial scales. To fill this gap, we used SNPs in the nuclear vitellogenin (Vg) gene to assess the local population structure and genetic diversity of the philopatric yellow-legged mining bee, Andrena flavipes, in the Netherlands, one of the most fragmented and urbanized countries in Europe. We investigated whether its reported nest-philopatry, combined with a fragmented landscape, would lead to genetic differentiation and isolation-by-distance (IBD) between populations, and low genetic diversity and inbreeding within populations. A total of 144 A. flavipes bees were sequenced for the full-length Vg gene and genotyped for SNPs. Despite high overall genetic variation, we found a heterozygote deficit and positive inbreeding coefficient in most populations, suggesting non-random mating. Low genetic differentiation was detected between populations, with no distinct genetic clusters, but with a pattern of IBD. These results suggest extensive but spatially structured gene flow, consistent with philopatric behaviour, and indicate that increasing isolation between nest-aggregations potentially leads to genetic differentiation over time. Our findings demonstrate that even in a common and widespread species, nest-philopatry and landscape fragmentation can drive inbreeding and spatially structured gene flow at local scales, highlighting the importance of maintaining habitat connectivity and semi-natural elements in agricultural and urbanized landscapes to sustain wild bee populations.

genetics↗

bioRxiv2017

Genetic And Epigenetic Insight Into Morphospecies In A Reef Coral

Incongruence between conventional and molecular systematics has left the delineation of many species unresolved. Reef-building corals are no exception, with phenotypic plasticity among the most plausible explanations for alternative morphospecies. As potential molecular signatures of phenotypic plasticity, epigenetic processes may contribute to our understanding of morphospecies. We compared genetic and epigenetic variation in Caribbean branching Porites spp., testing the hypothesis that epigenetics-- specifically, differential patterns of DNA methylation--play a role in alternative morphotypes of a group whose taxonomic status has been questioned. We used reduced representation genome sequencing to analyze over 1,000 single nucleotide polymorphisms and CpG sites in 27 Porites spp. exhibiting a range of morphotypes from a variety of habitats in Belize. We found stronger evidence for genetic rather than epigenetic structuring, identifying three well-defined genetic groups. One of these groups exhibited significantly thicker branches, and branch thickness was a better predictor of genetic groups than depth, habitat, or symbiont type. Epigenetic patterns were more subtle, with no clear groups. The more thickly branched individuals in one of the genetic groups exhibited some epigenetic similarity, suggesting potential covariation of genetics and epigenetics. This covariation was further supported by a positive association between pairwise genetic and epigenetic distance. We speculate that epigenetic patterns are a complex mosaic reflecting inheritance and diverse environmental histories. Given the role of genetics in branching Porites spp. morphospecies we were able to detect with genome-wide sequencing, use of such techniques throughout the geographic range may help settle their phylogeny.

molecular biology↗

bioRxiv2017

Disease as collider: a new case-only method to discover environmental factors in complex diseases with genetic risk estimation

BackgroundCase-only design for gene-environment interaction (CODGEI) relies on the rare disease assumption. A negative association due to collider bias appears between gene and environment when this assumption is not respected. Genetic risk estimation can quantify part of the predisposition of an individual to a disease.\n\nMethodsWe introduce Disease As Collider (DAC), a new case-only methodology to discover environmental factors using genetic risk estimation: a negative correlation between genetic risk and environment in cases provides a signature of a genuine environmental risk marker. Simulation of disease occurrence in a source population allows to estimate the statistical power of DAC and the influence of collider bias in CODGEI. We illustrate DAC in 831 type 1 diabetes (T1D) patients. Results: The power of DAC increases with sample size, prevalence and accuracy of genetic risk estimation. For a prevalence of 1% and a realistic genetic risk estimation, power of 80% is reached for a sample size under 3000. Collider bias offers an alternative interpretation to the results of CODGEI in a published study on breast cancer.\n\nConclusionDAC could provide a new line of evidence for discovering which environmental factors play a role in complex diseases or confirming results obtained in case-control studies. We discuss the circumstances needed for DAC to participate in the dissection of environmental determinants of disease. We provide guidance on the use of CODGEI regarding the rare disease assumption.\n\nO_LSTKey messagesC_LSTO_LIA complex disease is a collider between genetic determinants and environmental factors; it is a consequence of both.\nC_LIO_LICollider bias can affect the results of the case-only design for gene-environment interactions when the disease is common.\nC_LIO_LIUsing genetic risk estimation, collider bias can be used to discover or confirm the association between a disease and an environmental factor in a case-only setting.\nC_LIO_LIStatistical power of this approach is small when the disease is rare. Power increases with sample size, prevalence and genetic risk prediction accuracy.\nC_LI

epidemiology↗

bioRxiv2017

A Century of Guayule: Comprehensive Genetic Characterization of the Guayule (Parthenium argentatum A. Gray) USDA Germplasm Collection

The fragility of a single-source, geographically concentrated supply of natural rubber, a critical material of the modern economy, has brought guayule (Parthenium argentatum A. Gray) to the forefront as an alternative source of natural rubber. The improvement of guayule for commercial-scale production has been limited by the lack of genomic tools and well-characterized genetic resources required for genomics-assisted breeding. To address this issue, we developed nearly 50,000 single nucleotide polymorphism (SNP) genetic markers and genotyped 69 accessions of guayule and its sister taxa mariola (Parthenium incanum Kunth), representing the entire available NALPGRU germplasm collection. We identified multiple interspecific hybrid accessions previously considered guayule, including six guayule-mariola hybrids and non-mariola interspecific hybrid accessions AZ-2 and AZ-3, two commonly used high-yielding cultivars. We dissected genetic diversity within the collection to identify a highly diverse subset of guayule accessions, and showed that wild guayule stands in Big Bend National Park, Texas, USA have the potential to provide hitherto untapped guayule genetic diversity. Together, these results provide the most thorough genetic characterization of guayule germplasm to date and lay the foundation for rapid genetic improvement of commercial guayule germplasm.\n\nKey Results O_LISix guayule accessions are guayule-mariola hybrids\nC_LIO_LIGuayule collections from Big Bend National Park contain novel guayule genotypes not present in collections from Mexico\nC_LIO_LICommonly cultivated accessions AZ2 and AZ3 contain introgressions from other Parthenium species\nC_LIO_LIThe triploid accessions 11591, 11646, N576, N565, N565II, and RICHARDSON are generally indistinguishable from each other with respect to genetic background and likely represent the 4265-I source genotype (Johnson, 1950)\nC_LIO_LIOpen pollinated and purposefully outcrossed tetraploid selections derived from 4265-I incorporate further genetic diversity and form distinct genotypes\nC_LI

plant biology↗

bioRxiv2017

Dissecting Population Substructure in India via Correlation Optimization of Genetics and Geodemographics

India represents an intricate tapestry of population substructure shaped by geography, language, culture and social stratification. While geography closely correlates with genetic structure in other parts of the world, the strict endogamy imposed by the Indian caste system and the large number of spoken languages add further levels of complexity to understand Indian population structure. To date, no study has attempted to model and evaluate how these factors have interacted to shape the patterns of genetic diversity within India. We merged all publicly available data from the Indian subcontinent into a data set of 891 individuals from 90 well-defined groups. Bringing together geography, genetics and demographic factors, we developed COGG (Correlation Optimization of Genetics and Geodemographics) to build a model that explains the observed population genetic substructure. We show that shared language along with social structure have been the most powerful forces in creating paths of gene flow in the subcontinent. Furthermore, we discover the ethnic groups that best capture the diverse genetic substructure highlighted by COGG. Integrating data from India with a data set of additional 1,323 individuals from 50 populations we find that Europeans show shared genetic drift with the Indo-European and Dravidian speakers of India, whereas the East Asians have the maximum shared genetic drift with Tibeto-Burman speaking tribal groups.

genomics↗

bioRxiv2017

Inferring Continuous and Discrete Population Genetic Structure Across Space

A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the \"clines versus clusters\" problem in modeling population genetic variation. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.\n\nAuthor summaryOne of the first steps in the analysis of genetic data, and a principal mission of biology, is to describe and categorize natural variation. A continuous pattern of differentiation (isolation by distance), where individuals found closer together in space are, on average, more genetically similar than individuals sampled farther apart, can confound attempts to categorize natural variation into groups. This is because current statistical methods for assigning individuals to discrete clusters cannot accommodate spatial patterns, and so are forced to use clusters to describe what is in fact continuous variation. As isolation by distance is common in nature, this is a substantial shortcoming of existing methods. In this study, we introduce a new statistical method for categorizing natural genetic variation - one that describes variation as a combination of continuous and discrete patterns. We demonstrate that this method works well and can capture patterns in population genomic data without resorting to splitting populations where they can be described by continuous patterns of variation.

evolutionary biology↗

bioRxiv2020

Acquisition of Oral Microbiota is Driven by Environment, Not Host Genetics

The human oral microbiota is acquired early in an organized pattern, but the factors driving this acquisition are not well understood. Microbial "heritability" could have far-reaching consequences for health, yet no studies have specifically examined the fidelity with which the oral microbiota are passed from parents to offspring. Some previous studies comparing monozygotic (MZ) and dizygotic (DZ) twins had suggested that host genetics has a role in shaping oral microbial communities, and also identified so called "heritable" taxa. However, these findings are likely to be confounded by shared environmental factors resulting from the well-established greater behavioral similarity among MZ twins. In addition, MZ and DZ twins share an equal portion of their parents genome, and so this model is not informative for studying direct parent to offspring transmission. To specifically examine the contribution of genetics to the fidelity of transmission of bacteria from parents to offspring, we used a novel study design comparing fraction of shared species and strains between our genetically related group consisting of children and their biological mothers, with that of children and their adoptive mothers, constituting our genetically unrelated group. Fifty-five biological and 50 adoptive mother-child pairs were recruited along with 23 biological fathers and 16 siblings. Subjects were carefully selected to ensure the two groups were matched on childs age. Three distinct habitats within the oral cavity: the saliva/soft tissue surface, supragingival biofilm, and subgingival biofilm, were sampled to comprehensively profile the oral microbiome. Our recently developed strategy for subspecies level characterization of bacterial communities by targeted sequencing of the ribosomal 16-23S intergenic spacer region (ISR) was utilized in the present study to track strain sharing between subjects, in addition to 16S rRNA gene sequencing for species analysis. Results showed that oral bacterial community profiles of adoptive and biological mother-child pairs were equally similar, indicating no effect of host genetics on the fidelity of transmission. This was consistent at both species and strain level resolutions, and across all three habitats sampled. We also found that all children more closely resembled their own mother as compared to unrelated women, suggesting that contact and shared environment were the major factors shaping the oral microbiota. Individual analysis of the most abundant species also did not detect any effect of host genetics on strain sharing between mother and child. Mother-child strain similarity increased with the age of the child, ruling out early effects that are lost over time. No effect on the fidelity of mother-child strain sharing from vaginal birth or breast feeding was seen. Analysis of extended families showed that fathers and mothers were equally similar to their children. Cohabitating couples showed even greater strain similarity than mother-child pairs, further supporting the role of age, contact and shared environment as determinants of microbial similarity. Based on these findings we suggest that the genetic effects on oral microbial acquisition observed in twin studies are more likely the result of confounding environmental factors based on greater behavioral similarity among MZ twins. Our findings suggest that these host mechanisms are universal to humans, since no effect of genetic relatedness on fidelity of microbial transmission could be detected. Instead, our findings point toward contact and shared environment being the driving factors of microbial transmission, with a unique combination of these factors ultimately shaping a highly personalized human oral microbiome.

microbiology↗

bioRxiv2020

Genetic diversity, structure, and kinship analysis of Trachemys venusta venusta in Wildlife Management Units and wild populations in south Mexico. Implications for conservation and management.

The Meso-American slider turtle (Trachemys venusta) is a freshwater turtle endemic to Mexico and Central America. Due to the overexploitation of its natural populations, it is in the at risk category formulated by the Official Mexican Standard NOM-059-ECOL-2010. In the state of Tabasco, Management Units for the Conservation of Wildlife (UMA) were created to reduce the impact of overexploitation of freshwater turtles. However, no genetic management plan was considered. This study presents the level of genetic diversity of the founder individuals in order to develop a management plan which will optimize reproduction in the UMA. Genetic diversity was compared between captive (n = 45) and wild (n = 86) individuals using 14 microsatellite molecular markers. Level of genetic diversity could be considered as low (He < 0.6) for a species of turtle and suggests that a higher level of protection is required for this particular species. Furthermore, values were slightly higher for the captive group reflecting the mix of genetic sources (founding individuals from different localities) and demonstrating that the captive population is genetically representative of natural populations. The genetic structure analysis revealed a relationship between captive and wild populations, indicating the influence of the two principal river basins in this region on the population of freshwater turtles. Finally, according to the results obtained from the analysis conducted using SO_SCPLOWTORMC_SCPLOW and ML-RO_SCPLOWELATEC_SCPLOW programs, we recommend the use of 19 females and 13 males, generating a potential of 247 dyads with no relationship. These first results of genetic management in a Mexican UMA, demonstrate the importance of molecular approaches at the time of managing and conserving species in captivity.

molecular biology↗