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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Genetic analyses in UK Biobank identifies 78 novel loci associated with urinary biomarkers providing new insights into the biology of kidney function and chronic disease

BackgroundUrine biomarkers, such as creatinine, microalbumin, potassium and sodium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease and diabetes mellitus. Knowledge about the genetic determinants of the levels of these biomarker may shed light on pathophysiological mechanisms underlying the development of these diseases.\n\nMethodsWe performed genome-wide association studies of urinary levels of creatinine, microalbumin, potassium, and sodium in up to 326,441 unrelated individuals of European ancestry from the UK Biobank, a large population-based cohort study of over 500,000 individuals recruited across the United Kingdom in 2006-2010. Further, we explored genetic correlations, tissue-specific gene expression and possible causal genes related to these biomarkers.\n\nResultsWe identified 23 genome-wide significant independent loci associated with creatinine, 20 for microalbumin, 12 for potassium, and 38 for sodium. We confirmed several established associations including between the CUBN locus and microalbumin (rs141640975, p=3.11e-68). Variants associated with the levels of urinary creatinine, potassium, and sodium mapped to loci previously associated with obesity (GIPR, rs1800437, p=9.81e-10), caffeine metabolism (CYP1A1, rs2472297, p=1.61e-8) and triglycerides (GCKR, rs1260326, p=4.37e-16), respectively. We detected high pairwise genetic correlation between the levels of four urinary biomarkers, and significant genetic correlation between their levels and several anthropometric, cardiovascular, glycemic, lipid and kidney traits. We highlight GATM as causally implicated in the genetic control of urine creatinine, and GIPR, a potential diabetes drug target, as a plausible causal gene involved in regulation of urine creatinine and sodium.\n\nConclusionWe report 78 novel genome-wide significant associations with urinary levels of creatinine, microalbumin, potassium and sodium in the UK Biobank, confirming several previously established associations and providing new insights into the genetic basis of these traits and their connection to chronic diseases.\n\nAuthor SummaryUrine biomarkers, such as creatinine, microalbumin, potassium and sodium are strongly associated with several common diseases including chronic kidney disease, cardiovascular disease and diabetes mellitus. Knowledge about the genetic determinants of the levels of these biomarker may shed light on pathophysiological mechanisms underlying the development of these diseases. Here, we performed genome-wide association studies of urinary levels of creatinine, microalbumin, potassium and sodium in up to 326,441 unrelated individuals of European ancestry from the UK Biobank. Further, we explored genetic correlations, tissue-specific gene expression and possible causal genes related to these biomarkers. We identified 78 novel genome-wide significant associations with urinary biomarkers, confirming several previously established associations and providing new insights into the genetic basis of these traits and their connection to chronic diseases. Further, we highlight GATM as causally implicated in the genetic control of urine creatinine, and GIPR, a potential diabetes drug target, as a plausible causal gene involved in regulation of urine creatinine and sodium. The knowledge arising from our work may improve the predictive utility of the respective biomarker and point to new therapeutic strategies to prevent common diseases.

genetics↗
bioRxiv2018

Genetic correlations between pain phenotypes and depression and neuroticism

Correlations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. The purpose of this study was to identify whether eight pain phenotypes, depressive symptoms, major depressive disorders, and neuroticism are correlated for genetic reasons. Eight pain phenotypes were defined by a specific pain-related question in the UK Biobank questionnaire. First we generated genome-wide association summary statistics on each pain phenotype, and estimated the common SNP-based heritability of each trait using GCTA. We then estimated the genetic correlation of each pain phenotype with depressive symptoms, major depressive disorders and neuroticism using the the cross-trait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. Third, we used the LDSC software to calculate genetic correlations among pain phenotypes. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h2=0.31, standard error=0.072). All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. The largest genetic correlations occurred between neuroticism and stomach or abdominal pain (rg=0.70, P=2.4 x 10-9). In contrast, hip pain and knee pain showed weaker evidence of shared genetic architecture with these negative emotional traits. In addition, many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Pain at a variety of body sites is heritable and genetically correlated with depression and neuroticism. This suggests that pain, neuroticism and depression share partially overlapping genetic risk factors.

genetics↗
bioRxiv2026

Genetic diversity within and between polyploid sugarcane (Saccharum spp.) families obtained via caryopsis using microsatellite markers and multicategory model

Genetic diversity analyses are essential for sugarcane (Saccharum spp.) breeding programs. Crossbreeding, based on genetic distances between parental plants, is a tool used to increase genetic variability and enhance plant selection; however, quantifying variation in highly polyploid species remains a challenge. The present study aimed to evaluate the diversity within and between 12 families of sugarcane derived from caryopses, analyzing 120 individual seedlings arranged in an augmented block design. Genotyping was performed using primers for 16 microsatellite loci, five simple sequence repeat (SSR) loci, and 11 expressed sequence tag-SSR (EST-SSR) loci. To accurately account for polyploidy, similarity calculations were performed using Bruvos distances among individuals and RST distances among the families. Analysis of molecular variance (AMOVA) indicated that most of the genetic variability was within families (72%), with only 28% found between them. This high level of intra-family variation demonstrates that a significant reservoir of genetic diversity remains available within the crosses. The highest genetic similarity was observed between the families RB986952 x RB986960 and RB036122 x RB03611, whereas the lowest genetic similarity was observed between the families RB97319 x RB966928 and RB106802 x RB855036. Although the evaluated families shared high genetic similarity, the pronounced genetic variation within them demonstrates a robust recombination potential, indicating that the genetic basis of sugarcane can be better explored using the high variability that already exists in the selection of desirable morpho-agronomic characteristics within the families. Furthermore, this study highlights the importance of using appropriate distances for diversity studies with codominant markers, such as microsatellites, in polyploid species.

genetics↗
bioRxiv2015

When three traits make a line: Evolution of phenotypic plasticity and genetic assimilation through linear reaction norms in stochastic environments

Genetic assimilation results from selection on phenotypic plasticity, but quantitative genetics models of linear reaction norms considering intercept and slope as traits do not fully incorporate the process of genetic assimilation. We argue that intercept-slope reaction norm models are insufficient representations of genetic effects on linear reaction norms, and that considering reaction norm intercept as a trait is unfortunate because the definition of this trait relates to a specific environmental value (zero) and confounds genetic effects on reaction norm elevation with genetic effects on environmental perception. Instead we suggest a model with three traits representing genetic effects that respectively (i) are independent of the environment, (ii) alter the sensitivity of the phenotype to the environment, and (iii) determine how the organism perceives the environment. The model predicts that, given sufficient additive genetic variation in environmental perception, the environmental value at which reaction norms tend to cross will respond rapidly to selection after an abrupt environmental change, and eventually become equal to the new mean environment. This readjustment of the zone of canalization becomes completed without changes in genetic correlations, genetic drift or imposing any fitness costs on maintaining plasticity. The asymptotic evolutionary outcome of this three-trait linear reaction norm generally entails a lower degree of phenotypic plasticity than the two-trait model, and maximum expected fitness does not occur at the mean trait values in the population.

Evolutionary Biology↗
bioRxiv2016

No evidence for the positive relationship between genetic correlations and heritabilities

Quantitative genetics predicts, that traits subjected to strong selection should show low heritability and may yield biased estimates of genetic correlations (rg). Similar pattern may also appear if genetic sources of variation are confounded with non-genetic sources. Thus, a positive relationship between genetic correlations and heritabilities (h2) of underlying traits might be observed. Here we test this prediction using a large dataset of published estimates of genetic correlations and employing a powerful meta-analytical approach. We considered both between-traits and cross-sex genetic correlations. We failed to find support for the prediction about a positive rg - h2 relationship: our analysis based on nearly 1000 published estimates of genetic parameters indicates that the predicted relationship is weak and statistically non-significant. Thus, low heritability does not preclude the possibility of detecting substantial genetic correlations. Our meta-analysis indicates that published estimates of genetic parameters coming from various experimental designs and obtained using different statistical techniques are not significantly biased in case of weakly-heritable traits.

Evolutionary Biology↗
bioRxiv2016

Assessing the relationship between height growth and molecular genetic variation in Douglas-fir (Pseudotsuga menziesii) provenances

Douglas-fir (Pseudotsuga menziesii) is a conifer tree native to western North America. In central Europe, it shows superior growth performance and is considered a suitable substitute for tree species impaired in vitality due to climate change. Maintenance and improvement of growth performance in a changing environment is a main challenge for forest tree breeders. In this context, genetic variation as a factor underlying phenotypic variation, but also as the basis for future adaptation, is of particular interest. The aims of this study were to analyse (i) genetic diversity of selected Douglas-fir provenances, (ii) variation in height growth among provenances, and (iii) to assess the link between genetic and phenotypic variation height growth. Genotyping was done on microsatellite loci. Effects of provenance, genotype, and site on height growth were assessed by fitting mixed linear models. The most significant genetic differentiation was observed between provenances of the coastal variety, versus a provenance of the interior variety originating from British Columbia. Although genetic differentiation among provenances of the coastal variety was lower, genetic structures within this variety were identified. Moreover, genetic diversity showed a latitudinal gradient with the southernmost provenances being more diverse, probably reflecting the species' evolutionary history. The modelling approach revealed that height growth differed significantly by provenance, site, and the interaction between site and provenance, demonstrating that height growth is under strong genetic control. Additionally, this analysis showed that genetic variation captured by the genotyped microsatellite loci was significantly related to variation in height growth, providing statistical evidence for a genetic component in the observed phenotypic variation.

Plant Biology↗
bioRxiv2016

Genome-wide analysis of ivermectin response by Onchocerca volvulus reveals that genetic drift and soft selective sweeps contribute to loss of drug sensitivity

BackgroundTreatment of onchocerciasis using mass ivermectin administration has reduced morbidity and transmission throughout Africa and Central/South America. Mass drug administration is likely to exert selection pressure on parasites, and phenotypic and genetic changes in several Onchocerca volvulus populations from Cameroon and Ghana - exposed to more than a decade of regular ivermectin treatment - have raised concern that sub-optimal responses to ivermectins anti-fecundity effect are becoming more frequent and may spread.\n\nMethodology/Principal FindingsPooled next generation sequencing (Pool-seq) was used to characterise genetic diversity within and between 108 adult female worms differing in ivermectin treatment history and response. Genome-wide analyses revealed genetic variation that significantly differentiated good responder (GR) and sub-optimal responder (SOR) parasites. These variants were not randomly distributed but clustered in ~31 quantitative trait loci (QTLs), with little overlap in putative QTL position and gene content between countries. Published candidate ivermectin SOR genes were largely absent in these regions; QTLs differentiating GR and SOR worms were enriched for genes in molecular pathways associated with neurotransmission, development, and stress responses. Finally, single worm genotyping demonstrated that geographic isolation and genetic change over time (in the presence of drug exposure) had a significantly greater role in shaping genetic diversity than the evolution of SOR.\n\nConclusions/SignificanceThis study is one of the first genome-wide association analyses in a parasitic nematode, and provides insight into the genomics of ivermectin response and population structure of O. volvulus. We argue that ivermectin response is a polygenically-determined quantitative trait in which identical or related molecular pathways but not necessarily individual genes likely determine the extent of ivermectin response in different parasite populations. Furthermore, we propose that genetic drift rather than genetic selection of SOR is the underlying driver of population differentiation, which has significant implications for the emergence and potential spread of SOR within and between these parasite populations.\n\nAuthor summaryOnchocerciasis is a human parasitic disease endemic across large areas of Sub-Saharan Africa, where more that 99% of the estimated 100 million people globally at-risk live. The microfilarial stage of Onchocerca volvulus causes pathologies ranging from mild itching to visual impairment and ultimately, irreversible blindness. Mass administration of ivermectin kills microfilariae and has an anti-fecundity effect on adult worms by temporarily inhibiting the development in utero and/or release into the skin of new microfilariae, thereby reducing morbidity and transmission. Phenotypic and genetic changes in some parasite populations that have undergone multiple ivermectin treatments in Cameroon and Ghana have raised concern that sub-optimal response to ivermectins anti-fecundity effect may increase in frequency, reducing the impact of ivermectin-based control measures. We used next generation sequencing of small pools of parasites to define genome-wide genetic differences between phenotypically characterised good and sub-optimal responder parasites from Cameroon and Ghana, and identified multiple genomic regions differentiating the response types. These regions were largely different between parasites from both countries but revealed common molecular pathways that might be involved in determining the extent of response to ivermectins anti-fecundity effect. These data reveal a more complex than previously described pattern of genetic diversity among O. volvulus populations that differ in their geography and response to ivermectin treatment.

genomics↗
bioRxiv2017

Evolution of genetic variance during adaptive radiation

Genetic correlations between traits can bias adaptation away from optimal phenotypes and constrain the rate of evolution. If genetic correlations between traits limit adaptation to contrasting environments, rapid adaptive divergence across a heterogeneous landscape may be difficult. However, if genetic variance can evolve and align with the direction of natural selection, then abundant allelic variation can promote rapid divergence during adaptive radiation. Here, we explored adaptive divergence among ecotypes of an Australian native wildflower by quantifying divergence in multivariate phenotypes of populations that occupy four contrasting environments. We investigated differences in multivariate genetic variance underlying morphological traits and examined the alignment between divergence in phenotype and divergence in genetic variance. We found that divergence in mean multivariate phenotype has occurred along two major axes represented by different combinations of plant architecture and leaf traits. Ecotypes also showed divergence in the level of genetic variance in individual traits, and the multivariate distribution of genetic variance among traits. Divergence in multivariate phenotypic mean aligned with divergence in genetic variance, with most of the divergence in phenotype among ecotypes associated with a change in trait combinations that had substantial levels of genetic variance in each ecotype. Overall, our results suggest that divergent natural selection acting on high levels of standing genetic variation might fuel ecotypic differentiation during the early stages of adaptive radiation.

evolutionary biology↗
bioRxiv2017

A Map Of Genetic Interactions In Cancer Cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/{beta}-catenin signaling identified GANAB and PRKCSH as new positive regulators of Wnt/{beta}-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

systems biology↗
bioRxiv2017

The human oral microbiome is shaped by shared environment rather than genetics: evidence from a large family of closely-related individuals

The human microbiome is affected by multiple factors, including the environment and host genetics. In this study, we analyzed the oral microbiome of an extended family of Ashkenazi Jewish individuals living in several cities and investigated associations with both shared household and host genetic similarities. We found that environmental effects dominated over genetic ones. While there was weak evidence of geographic structuring at the level of cities, we observed a large and significant effect of shared household on microbiome composition, supporting the role of immediate shared environment in dictating the presence or absence of taxa. This effect was also seen when including adults who had grown up in the same household but moved out prior to the time of sampling, suggesting that the establishment of the oral microbiome earlier in life may affect its long-term composition. We found weak associations between host genetic relatedness and microbiome dissimilarity when using family pedigrees as proxies for genetic similarity. However this association disappeared when using more accurate measures of kinship based on genome-wide genetic markers, indicating that environment rather than host genetics is the dominant factor affecting the composition of the oral microbiome in closely-related individuals. Our results support the concept that there is a consistent core microbiome conserved across global scales, but that small-scale effects due to shared living environment significantly affect microbial community composition.\n\nIMPORTANCEPrevious research shows that relatives have a more similar oral microbiome composition than non-relatives, but it remains difficult to distinguish the effects of relatedness and shared household environment. Furthermore, pedigree measures may not accurately measure host genetic similarity. In this study, we include genetic relatedness based on genome-wide SNPs (rather than pedigree measures) and shared environment in the same analysis. We quantify the relative importance of these factors by studying the oral microbiome in members of a large extended Ashkenazi Jewish family who share a similar diet and lifestyle despite living in different locations. We find that host genetics plays no significant role and that the dominant factor is shared environment at the household level. We also find that this effect appears to persist in individuals who have moved out of the parental household, suggesting that the oral microbiome established earlier in life persists long-term.

microbiology↗
bioRxiv2017

Genetic Load Makes Cancer Cells More Sensitive To Common Drugs: Evidence From Cancer Cell Line Encyclopedia

Genetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, that provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.

cancer biology↗
bioRxiv2017

Comparative analysis of genetic diversity and differentiation of cauliflower (Brassica oleracea var. botrytis) accessions from two ex situ genebanks

Cauliflower (Brassica oleracea var. botrytis) is an important vegetable crop for human nutrition. We characterized 192 cauliflower accessions from the USDA and IPK genebanks with genotyping by sequencing (GBS). They originated from 26 different countries and represent about 44% of all cauliflower accessions in both genebanks. The analysis of genetic diversity revealed that accessions formed two major groups that represented the two genebanks and were not related to the country of origin. This differentiation was robust with respect to the analysis methods that included principal component analysis, ADMIXTURE and neighbor-joining trees. Genetic diversity was higher in the USDA collection and significant phenotypic differences between the two genebanks were found in three out of six traits investigated. GBS data have a high proportion of missing data, but we observed that the exclusion of single nucleotide polymorphisms (SNPs) with missing data or the imputation of missing SNP alleles produced very similar results. The results indicate that the composition and type of accessions have a strong effect on the structure of genetic diversity of ex situ collections, although regeneration procedures and local adaptation to regeneration conditions may also contribute to a divergence. Fst-based outlier tests of genetic differentiation identified only a small proportion (<1%) of SNPs that are highly differentiated between the two genebanks, which indicates that selection during seed regeneration is not a major cause of differentiation between genebanks. Seed regeneration procedures of both genebanks do not result in different levels of genetic drift and loss of genetic variation. We therefore conclude that the composition and type of accessions mainly influence the level of genetic diversity and explain the strong genetic differentiation between the two ex situ collections. In summary, GBS is a useful method for characterizing genetic diversity in cauliflower genebank material and our results suggest that it may be useful to incorporate routine genotyping into accession management and seed regeneration to monitor the diversity present in ex situ collections and to reduce the loss of genetic diversity during seed regeneration.

plant biology↗
bioRxiv2018

LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studies

Genome-wide association studies have helped to shed light on the genetic architecture of complex traits and diseases. Deep phenotyping of population cohorts is increasingly applied, where multi-to high-dimensional phenotypes are recorded in the individuals. Whilst these rich datasets provide important opportunities to analyse complex trait structures and pleiotropic effects at a genome-wide scale, existing statistical methods for joint genetic analyses are hampered by computational limitations posed by high-dimensional phenotypes. Consequently, such multivariate analyses are currently limited to a moderate number of traits. Here, we introduce a method that combines linear mixed models with bootstrapping (LiMMBo) to enable computationally efficient joint genetic analysis of high-dimensional phenotypes. Our method builds on linear mixed models, thereby providing robust control for population structure and other confounding factors, and the model scales to larger datasets with up to hundreds of phenotypes. We first validate LiMMBo using simulations, demonstrating consistent covariance estimates at greatly reduced computational cost compared to existing methods. We also find LiMMBo yields consistent power advantages compared to univariate modelling strategies, where the advantages of multivariate mapping increases substantially with the phenotype dimensionality. Finally, we applied LiMMBo to 41 yeast growth traits to map their genetic determinants, finding previously known and novel pleiotropic relationships in this high-dimensional phenotype space. LiMMBo is accessible as open source software (https://github.com/HannahVMeyer/limmbo).\n\nAuthor summaryIn multi-trait genetic association studies one is interested in detecting genetic variants that are associated with one or multiple traits. Genetic variants that influence two or more traits are referred to as pleiotropic. Multivariate linear mixed models have been successfully applied to detect pleiotropic effects, by jointly modelling association signals across traits. However, these models are currently limited to a moderate number of phenotypes as the number of model parameters grows steeply with the number of phenotypes, raising a computational burden. We developed LiMMBo, a new approach for the joint analysis of high-dimensional phenotypes. Our method reduces the number of effective model parameters by introducing an intermediate subsampling step. We validate this strategy using simulations, where we apply LiMMBo for the genetic analysis of hundreds of phenotypes, detecting pleiotropic effects for a wide range of simulated genetic architectures. Finally, to illustrate LiMMBo in practice, we apply the model to a study of growth traits in yeast, where we identify pleiotropic effects for traits with formerly known genetic effects as well as revealing previously unconnected traits.

genomics↗
bioRxiv2018

Evidence for gene-environment correlation in child feeding: Links between common genetic variation for BMI in children and parental feeding practices

The parental feeding practices (PFPs) of excessive restriction of food intake ( restriction) and pressure to increase food consumption ( pressure) have been argued to causally influence child weight in opposite directions (high restriction causing overweight; high pressure causing underweight). However child weight could also elicit PFPs. A novel approach is to investigate gene-environment correlation between child genetic influences on BMI and PFPs. Genome-wide polygenic scores (GPS) combining BMI-associated variants were created for 10,346 children (including 3,320 DZ twin pairs) from the Twins Early Development Study using results from an independent genome-wide association study meta-analysis. Parental restriction and pressure were assessed using the Child Feeding Questionnaire. Child BMI standard deviation scores (BMI-SDS) were calculated from childrens height and weight at age 10. Linear regression and fixed family effect models were used to test between-(n=4,445 individuals) and within-family (n=2,164 DZ pairs) associations between the GPS and PFPs. In addition, we performed multivariate twin analyses (n=4,375 twin pairs) to estimate the heritabilities of PFPs and the genetic correlations between BMI-SDS and PFPs. The GPS was correlated with BMI-SDS ({beta}=0.20, p=2.41x10-38). Consistent with the gene-environment correlation hypothesis, child BMI GPS was positively associated with restriction ({beta}=0.05, p=4.19x10-4), and negatively associated with pressure ({beta}=-0.08, p=2.70x10-7). These results remained consistent after controlling for parental BMI, and after controlling for overall family contributions (within-family analyses). Heritabilities for restriction (43% [40-47%]) and pressure (54% [50-59%]) were moderate-to-high. Twin-based genetic correlations were moderate and positive between BMI-SDS and restriction (rA=0.28 [0.23-0.32]), and substantial and negative between BMI-SDS and pressure (rA=-0.48 [-0.52 --0.44]. Results suggest that the degree to which parents limit or encourage childrens food intake is partly influenced by childrens genetic predispositions to higher or lower BMI. These findings point to an evocative gene-environment correlation in which heritable characteristics in the child elicit parental feeding behaviour.\n\nAuthor SummaryIt is widely believed that parents influence their childs BMI via certain feeding practices. For example, rigid restriction has been argued to cause overweight, and pressuring to eat to cause underweight. However, recent longitudinal research has not supported this model. An alternative hypothesis is that child BMI, which has a strong genetic basis, evokes parental feeding practices ( gene-environment correlation). To test this, we applied two genetic methods in a large sample of 10-year-old children from the Twins Early Development Study: a polygenic score analysis (DNA-based score of common genetic variants robustly associated with BMI in genome-wide meta-analyses), and a twin analysis (comparing resemblance between identical and non-identical twin pairs). Polygenic scores correlated positively with parental restriction of food intake ( restriction; {beta}=0.05, p=4.19x10-4), and negatively with parental pressure to increase food intake ( pressure; {beta}=-0.08, p=2.70x10-7). Associations were unchanged after controlling for all genetic and environmental effects shared within families. Results from twin analyses were consistent. Restriction (43%) and pressure (54%) were substantially heritable, and a positive genetic correlation between child BMI and restriction (rA=0.28), and negative genetic correlation between child BMI and pressure (rA=-0.48) emerged. These findings challenge the prevailing view that parental behaviours are the sole cause of child BMI by supporting an alternate hypothesis that child BMI also causes parental feeding behaviour.

genomics↗
bioRxiv2026

Genetic legacy in soil seedbanks after grassland conversion to plantation forests: evidence from Potentilla freyniana

Semi-natural grasslands are important ecosystems supporting biodiversity in Japan, but their area has declined rapidly due to land-use change and abandonment of traditional management practices such as mowing and burning. Although the conservation of genetic diversity is essential for the long-term persistence of grassland plants, little is known about the genetic diversity retained in soil seedbanks following conversion of grasslands to plantation forests. In this study, we compared the genetic diversity and population structure of above-ground and soil seedbank populations of the grassland perennial forb Potentilla freyniana across three sites in each of three land-use types: burned grasslands, deciduous plantation forests, and evergreen plantation forests (plantation ages approximately 21-62 years) on the Kaida Plateau, central Japan. Soil seedbank populations were obtained from soil samples through germination experiments, and genetic analyses were conducted using newly developed simple sequence repeat (SSR) markers. Genetic diversity was assessed using expected heterozygosity, allelic richness, and private allelic richness, population structure was evaluated using analysis of molecular variance (AMOVA), STRUCTURE analyses, and pairwise FST. Soil seedbank populations maintained levels of genetic diversity comparable to those of above-ground populations, and no significant differences were detected between the two population types. Furthermore, soil seedbank populations in evergreen plantation forests, where above-ground individuals of P. freyniana were absent, retained genetic diversity comparable to that observed in burned grasslands. AMOVA detected no significant genetic differentiation between above-ground and soil seedbank populations. These results suggest that high levels of genetic diversity can persist in soil seedbank populations for decades after forest establishment and highlight the potential importance of soil seedbanks as genetic resources for grassland restoration.

ecology↗
bioRxiv2015

Simple genetic models for autism spectrum disorder

To explore the interplay between new mutation, transmission, and gender bias in genetic disease requires formal quantitative modeling. Autism spectrum disorders offer an ideal case: they are genetic in origin, complex, and show a gender bias. The high reproductive costs of autism ensure that most strongly associated genetic mutations are short-lived, and indeed the disease exhibits both transmitted and de novo components. There is a large body of both epidemiologic and genomic data that greatly constrain the genetic mechanisms that may contribute to the disorder. We develop a computational framework that assumes classes of additive variants, each member of a class having equal effect. We restrict our initial exploration to single class models, each having three parameters. Only one model matches epidemiological data. It also independently matches the incidence of de novo mutation in simplex families, the gender bias in unaffected siblings in simplex populations, and rates of mutation in target genes. This model makes strong and as yet not fully tested predictions, namely that females are the primary carriers in cases of genetic transmission, and that the incidence of de novo mutation in target genes for families at high risk for autism are not especially elevated. In its simplicity, this model does not account for MZ twin concordance or the distorted gender bias of high functioning children with ASD, and does not accommodate all the known mechanisms contributing to ASD. We point to the next steps in applying the same computational framework to explore more complex models.\n\nAuthor summaryFor understanding complex genetic diseases one needs both data and molecular/genetic models. In the absence of any model, it is impossible to do more than summarize observations. A good model will be consistent with much or all of the existing data and puts the data in the context of known genetic principles. Ideally the model will make testable predictions. Where the good models fail often shows the directions that require more thought about mechanisms. In this paper we describe a new computational framework that we use to explore a complex genetic disorder with many gene targets, with both de novo and transmitted variants, and with gender bias. The disorder we consider is autism spectrum disorder (ASD), and our framework rules out some previous models that make unsustainable predictions. We identify a formal model that satisfies diverse epidemiologic and genomic observations. This model makes strong and untested predictions and thereby suggests new studies that would resolve outstanding aspects of autism genetics.

Genetics↗
bioRxiv2016

Predictability of Genetic Interactions from Functional Gene Modules

Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets. Yet experimentally determining whether genes interact is technically non-trivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally-related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high-predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

Genetics↗
bioRxiv2016

Personalized genetic assessment of age associated Alzheimers disease risk

ImportanceIdentifying individuals at risk for developing Alzheimers disease (AD) is of utmost importance. Although genetic studies have identified APOE and other AD associated single nucleotide polymorphisms (SNPs), genetic information has not been integrated into an epidemiological framework for personalized risk prediction.\n\nObjectiveTo develop, replicate and validate a novel polygenic hazard score for predicting age-specific risk for AD.\n\nSettingMulti-center, multi-cohort genetic and clinical data.\n\nParticipantsWe assessed genetic data from 17,008 AD patients and 37,154 controls from the International Genetics of Alzheimers Project (IGAP), and 6,409 AD patients and 9,386 older controls from Phase 1 Alzheimers Disease Genetics Consortium (ADGC). As independent replication and validation cohorts, we also evaluated genetic, neuroimaging, neuropathologic, CSF and clinical data from ADGC Phase 2, National Institute of Aging Alzheimers Disease Center (NIA ADC) and Alzheimers Disease Neuroimaging Initiative (ADNI) (total n = 20,680)\n\nMain Outcome(s) and Measure(s)Use the IGAP cohort to first identify AD associated SNPs (at p < 10-5). Next, integrate these AD associated SNPs into a Cox proportional hazards model using ADGC phase 1 genetic data, providing a polygenic hazard score (PHS) for each participant. Combine population based incidence rates, and genotype-derived PHS for each individual to derive estimates of instantaneous risk for developing AD, based on genotype and age. Finally, assess replication and validation of PHS in independent cohorts.\n\nResultsIndividuals in the highest PHS quantiles developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE {varepsilon}3/3 individuals, PHS modified expected age of AD onset by more than 10 years between the lowest and highest deciles. In independent cohorts, PHS strongly predicted empirical age of AD onset (p = 1.1 x 10-26), longitudinal progression from normal aging to AD (p = 1.54 x 10-10) and associated with markers of AD neurodegeneration.\n\nConclusionsWe developed, replicated and validated a clinically usable PHS for quantifying individual differences in age-specific risk of AD. Beyond APOE, polygenic architecture plays an important role in modifying AD risk. Precise quantification of AD genetic risk will be useful for early diagnosis and therapeutic strategies.

Genetics↗