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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Genetically engineered insects with sex-selection and genetic incompatibility enable population suppression

Engineered Genetic Incompatibility (EGI) is a method to create species-like barriers to sexual reproduction. It has applications in pest control that mimic Sterile Insect Technique when only EGI males are released. This can be facilitated by introducing conditional female-lethality to EGI strains to generate a sex-sorting incompatible male system (SSIMS). Here we demonstrate a proof of concept by combining tetracycline-controlled female lethality constructs with a pyramus-targeting EGI line in the model insect Drosophila melanogaster. We show that both functions (incompatibility and sex-sorting) are robustly maintained in the SSIMS line and that this approach is effective for population suppression in cage experiments. Further we show that SSIMS males remain competitive with wild-type males for reproduction with wild-type females, including at the level of sperm competition.

bioengineering↗

bioRxiv2023

The genetic equidistance and maximum genetic diversity hypothesis: Smoke and mirrors?

As a novel molecular evolution model that was claimed to have an advantage over the molecular clock hypothesis1-3, the maximum genetic diversity (MGD) hypothesis was utilized to study the modern human origins4. Nevertheless, there are serious problems with this hypothesis and both it and its derivative studies should be treated with caution.

evolutionary biology↗

bioRxiv2024

Genetic shifts in viral populations: how sudden vs gradual changes in host species composition affect the stability and dynamics of genetic variability in viral populations

Sudden and gradual changes in host species composition --transitioning from more permissive to less permissive hosts-- have been shown to influence viral fitness, virulence, and rates of molecular evolution. In this study, we analyzed the roles of stochasticity and selection using previously published time-series data from evolving populations of Sindbis virus under different rates of host replacement. First, an approximate Markov model within the Wright-Fisher diffusion framework revealed a reduction in effective population size by approximately half in scenarios involving sudden host changes. These scenarios were also associated with fewer and generally weaker beneficial mutations. Second, genetic distance analysis between populations at consecutive time points indicated that populations undergoing gradual host changes evolved steadily until the original host was no longer present. Distances to the ancestral sequence in these cases exhibited occasional leapfrog phenomena, where the rise of certain haplotypes is not predictable based solely on their relatedness to previously dominant ones. In contrast, populations exposed to sudden host changes displayed less stable compositions and diverged from the ancestral sequence at a more constant rate. Third, we observed that allele frequencies distribution followed a power law consistent with Taylors law. Both treatments exhibited high levels of allele aggregation with significant fluctuations, where neutral, beneficial, and deleterious alleles could be distinguished by their general behavior and their position on Taylors plot. Finally, we found evidence that the host replacement regime influences the temporal distribution of mutations across the genome.

evolutionary biology↗

bioRxiv2025

A novel computational framework to identify translational potential of genetic mouse models in rare genetic obesity

Finding the right animal models has been a critical bottleneck in translational sciences of rare diseases. Traditional methods can be biased, limiting their effectiveness, and do not include cross-analysis with human manifestations of the diseases. This research introduces a novel computational framework that analyzes hundreds of mouse model data and phenotypes simultaneously. This paves the way to make the best disease-specific decisions. We have identified 106 mouse models in genetic obesity with more than 600 different phenotypes. An evaluation of the shared phenotypes across these models unearthed unexpected connections between obesity and 16 other diseases, opening doors for entirely new treatment avenues. This analytical framework has the potential to modernize how we research rare diseases, leading to more effective therapies for a wider range of conditions. Author SummaryA novel computational approach pinpoints ideal mouse models for rare diseases, uncovering hidden connections with other conditions.

bioinformatics↗

bioRxiv2025

Concurrent genetic and non-genetic resistance mechanisms to KRAS inhibition in CRC

KRAS is mutationally activated in 45-50% of colorectal cancer (CRC) cases, and while KRAS-targeted therapies have shown some clinical promise, upfront and acquired resistance limit their efficacy. To explore the acute response and mechanisms underlying KRAS inhibitor resistance, we used targeted exome sequencing and single-cell spatial transcriptomics to analyze patient-matched pre-treatment, on-treatment, and progression biopsies from patients treated with combined KRASG12C and EGFR inhibition. Acquired genetic events were identified in most patients at progression but were often subclonal and coexisted with transcriptional adaptive states. Mesenchymal, YAP, and fetal-like transcriptional signatures predominated in resistant tumors, while tumor cell-intrinsic inflammatory programs were induced in the early treatment phase. Single-cell spatial analysis revealed significant intratumoral heterogeneity, with diverse adaptive states predominating in different zones of individual tumors. Using human and murine organoid models, we show that these drug-induced inflammatory programs are cancer-cell autonomous and precede the emergence of regenerative fetal-like programs associated with drug resistance. We uncover TBK1 as a promising target to abrogate the early inflammatory adaptive phase and enhance responses to KRAS inhibition.

cancer biology↗

bioRxiv2018

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network is currently complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random. Therefore, attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.

systems biology↗

bioRxiv2019

Fitness functions, genetic and non-genetic inheritance, and why ecological dynamics and evolution are inevitably linked

The Breeders and Price equations are static models of evolution. Recent work has demonstrated how components of these equations, such as selection differentials and heritabilities, can be calculated for each time-step from dynamic, evolutionarily-explicit, structured population models. These dynamic models consist of functions that describe how environmental factors impact associations of genotypes and phenotypic traits with survival, reproduction, development, and non-genetic inheritance. Because evolutionarily explicit versions of these structured models can include feedbacks and can capture all the routes via which environmental variation can impact evolution, they i) provide a more powerful predictive tool for modelling eco-evolution in natural settings than existing approaches, and ii) reveal how key evolutionary parameters incorporated into static models change with time as evolution, or environmental change, proceeds.

evolutionary biology↗

bioRxiv2022

Using large soybean historical data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors

1Soybean (Glycine max (L.) Merr.) provides plant-based protein for global food production and is extensively bred to create cultivars with greater productivity in distinct environments. Plant breeders evaluate new soybean genotypes using multi-environment trials (MET). The application of MET assumes that trial locations provide representative environmental conditions that cultivars are likely to encounter when grown by farmers. In addition, MET are important to depict the patterns of genotype by environment interactions (GEI). To evaluate GEI for soybean seed yield and identify mega-environments (ME), a retrospective analysis of 39,006 data points from experimental soybean genotypes evaluated in preliminary and uniform field trials conducted by public plant breeders from 1989-2019 was considered. ME were identified from phenotypic information from the annual trials, geographic, soil, and meteorological records at the trial locations. Results indicate that yield variation was mostly explained by location and location by year interactions. The static portion of the GEI represented 26.30% of the total yield variance. Estimates of variance components derived from linear mixed models demonstrated that the phenotypic variation due to genotype by location interaction effects was greater than genotype by year interaction effects. A trend analysis indicated a two-fold increase in the genotypic variance between 1989-1995 and 1996-2019. Furthermore, the heterogeneous estimates of genotypic, genotype by location, genotype by year, and genotype by location by year variances, were encapsulated by distinct probability distributions. The observed target population of environments can be divided into at least two and at most three ME, thereby suggesting improvements in the response to selection can be achieved when selecting directly for clustered (i.e., regions, ME) versus selecting across regions. Clusters obtained using phenotypic data, latitude, and soil variables plus elevation, were the most effective. In addition, we published the R package SoyURT which contains the data sets used in this work. 2 HighlightsO_LIMega-environments can be identified with phenotypic, geographic, and meteorological data. C_LIO_LIReliable estimates of variances can be obtained with proper analyses of historical data. C_LIO_LIGenotype by location was more important than genotype by year variation for seed yield. C_LIO_LIThe trend in genotype by environment variances was captured in probability distributions. C_LI

genetics↗

bioRxiv2020

The developmental genetic architecture of vocabulary skills during the first three years of life: Capturing emerging associations with later-life reading and cognition

Individual differences in early-life vocabulary measures are heritable and associated with subsequent reading and cognitive abilities, although the underlying mechanisms are little understood. Here, we (i) investigate the developmental genetic architecture of expressive and receptive vocabulary in toddlerhood and (ii) assess origin and developmental stage of emerging genetic associations with mid-childhood verbal and non-verbal skills. Studying up to 6,524 unrelated children from the population-based Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, we dissected the phenotypic variance of longitudinally assessed early-life vocabulary measures (15-38 months) and later-life reading and cognitive skills (7-8 years) into genetic and residual components, by fitting multivariate structural equation models to genome-wide genetic-relationship matrices. Our findings show that the genetic architecture of early-life vocabulary is dynamic, involving multiple distinct genetic factors. Two of them are developmentally stable and contribute to genetic variation in mid-childhood skills: Genetic links with later-life verbal abilities (reading, verbal intelligence) emerged with expressive vocabulary at 24 months. The underlying genetic factor explained 10.1% variation (path coefficient: 0.32(SE=0.06)) in early language, but also 6.4% (path coefficient: 0.25(SE=0.12)) and 17.9% (path coefficient: 0.42(SE=0.13)) variation in mid-childhood reading and verbal intelligence, respectively. An independent stable genetic factor was identified for receptive vocabulary at 38 months, explaining 2.1% (path coefficient: 0.15(SE=0.07)) phenotypic variation. This genetic factor was also linked to both verbal and non-verbal cognitive abilities in mid-childhood, accounting for 24.7% of the variation in non-verbal intelligence (path coefficient: 0.50(SE=0.08)), 33.0% in reading (path coefficient: 0.57(SE=0.07)) and 36.1% in verbal intelligence (path coefficient: 0.60(0.10)), corresponding to the majority of genetic variance ([≥]66.4%). Thus, the genetic foundations of mid-childhood reading and cognition are diverse. They involve at least two independent genetic factors that emerge at different developmental stages during early language development and may implicate differences in cognitive processes that are already detectable during toddlerhood. Author summaryDifferences in the number of words young children produce (expressive vocabulary) and understand (receptive vocabulary) can be partially explained by genetic factors, and are related to reading and cognitive abilities later in life. Here, we studied genetic influences underlying word production and understanding during early development (15-38 months) and their genetic relationship with mid-childhood reading and cognitive skills (7-8 years), based on longitudinal phenotype measures and genome-wide genetic data from up to 6,524 unrelated children. We showed that vocabulary skills assessed at different stages during early development are influenced by distinct genetic factors, two of which also contribute to genetic variation in mid-childhood skills, suggesting developmental stability: Genetic sources emerging for word production skills at 24 months were linked to subsequent verbal abilities, including mid-childhood reading and verbal intelligence performance. A further independent genetic factor was identified that related to word comprehension at 38 months and also contributed to variation in later verbal as well as non-verbal abilities during mid-childhood. Thus, the genetic foundations of mid-childhood reading and cognition involve at least two independent genetic factors that emerge during early-life langauge development and may implicate differences in overarching cognitive mechanisms.

genetics↗

bioRxiv2020

The Nature of Genetic Susceptibility to Multiple Sclerosis

OBJECTIVETo explore the nature of MS-susceptibility and, by extension, other complex-genetic diseases. BACKGROUNDBasic-epidemiological parameters of MS (e.g., prevalence, recurrence-risks for siblings and twins, time-dependent changes in sex-ratio, etc.) are well-established. Moreover, >200 genetic-loci are unequivocally MS-associated, especially the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype-association. DESIGN/METHODSWe define the "genetically-susceptible" subset-(G) to include everyone with any non-zero life-time chance of developing MS. We analyze, mathematically, the implications that these epidemiological observations have regarding genetic susceptibility. In addition, we use the sex-ratio change (observed over a 35-year interval), to derive the relationship between MS-probability and an increasing likelihood of a suitable environmental-exposure. RESULTSWe demonstrate that genetic-susceptibitly is restricted to less than 4.7% of populations across Europe and North America. Among carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype, fewer than 20% are even in the subset-(G). Women are less likely to be susceptible than men although their MS-penetrance is considerably greater. Response-curves for MS-probability increase with an increasing likelihood of a suitable environmental-exposure, especially among women. These environmental response-curves plateau at under 50% for women and at a significantly lower level for men. CONCLUSIONSMS is fundamentally a genetic disorder. Despite this, a suitable environmental-exposure is also critical for disease-pathogenesis. Genetic-susceptibility requires specific combinations of non-additive genetic risk-factors. For example, the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype, by itself, poses no MS-risk. Moreover, the fact that environmental-response-curves plateau below 50%, indicates that disease-pathogenesis is partly stochastic. By extension, other diseases for which monozygotic-twin recurrence-risks greatly exceed disease-prevalence (e.g., rheumatoid arthritis, diabetes, and celiac disease), must have a similar genetic basis. Author SummaryWe define a "genetic-susceptible" subset (G) of the general population (Z) to include everyone with any non-zero chance of developing MS over their life-time. Using well-established epidemiological data from across Europe and North America, we establish that genetic-susceptibility is confined to less than 4.7% of these populations. Thus, the large majority of individuals have no chance whatsoever of developing MS, irrespective of any environmental conditions that they may experience during their lifetimes. In this sense, MS is fundamentally a genetic disorder. And, indeed, more than 200 genetic-loci, in multiple genomic locations have now been well-established to be associated with MS. Notably, however, the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 or (H+) haplotype, which has, by far, the strongest MS-association of any, has a carrier frequency in the population of 23% in North America and Europe. Therefore, with genetic susceptibility in the population being less than 4.7%, more than 80% of (H+)-haplotype carriers, must not be genetically-susceptible and, thus, have no chance of developing MS. In this circumstance, genetic susceptibility to MS must arise from a combination of this haplotype with "susceptible states" at other genetic loci. By itself, the (H+)-haplotype poses no risk. Indeed, genetic-susceptibility, generally, seems to require specific combinations of non-additive genetic risk-factors. Naturally, the conclusion that MS is fundamentally genetic does not preclude the possibility the environmental events are also critical to disease-pathogenesis. Using epidemiological data about the world-wide increase in the (F:M) sex-ratio for MS to construct (for men and women separately) the response curves relating an increasing likelihood of MS to an increasing likelihood of a sufficient environmental exposure (i.e., an exposure sufficient to cause MS in a susceptible individual). This analysis provides insight to both disease-susceptibility and disease-pathogenesis. First, men are more likely to be susceptible than women although susceptible women are considerably more likely to actually develop MS. Second, men seem to have a lower environmental threshold than women for developing MS. Nevertheless, women are more responsive to changes in the environmental conditions compared to men. Third, even with a maximal environmental exposure, susceptible women never exceed a 50% chance of developing MS. By contrast, susceptible men have a significantly lower likelihood (<10% chance) of developing MS. This indicates that stochastic factors must also be critical in disease pathogenesis. Finally, the nature of genetic susceptibility developed here for MS is applicable to many other complex genetic disorders. Indeed, for any disease, in which the proband-wise MZ-twin concordance rate greatly exceeds the disease-prevalence in the population (e.g., type I diabetes, rheumatoid arthritis, and celiac disease), only a small fraction of the population can possibly be genetically susceptible, as defined.

genetics↗

bioRxiv2021

Genetic influence on resting state networks in young male and female adults

Determining genetic versus environmental influences on the human brain is of crucial importance to understand the healthy brain as well as in a variety of disease and disorder states. Here we propose a unique, minimal assumption, approach to investigate genetic influence on the functional connectivity of the brain using 260 subjects" (65 monozygotic (MZ) and 65 dizygotic (DZ) healthy young adult twin pairs) resting state fMRI (rsfMRI) data from the Human Connectome Project (HCP). For any given resting state connection between twin pairs, the connection strengths across pairs were subtracted from each other in both directions. By applying the F-Test for equality of variances per connection, we found that there were a number of significant connections that demonstrated greater variance among dizygotic pairs in comparison to monozygotic pairs, implying these connections were under significant genetic influence. These population (DZ-MZ) results remained true irrespective of gender, with the caveat that certain connections were significant on a gender-specific basis. This is the first study to our knowledge to assess the heritability across young healthy adults both in general and specific to gender. Population Results & DiscussionAt the population level, there appears to be a posterior to anterior gradient of more to less genetic influence on brain connections and networks with visual > temporal, parietal > frontal. There was a high density of genetically-influenced functional connections predominantly involving posterior regions or networks of the brain: Visual Networks (VNs - primary visual, early visual, dorsal stream and ventral stream visual cortices, MT+ complex). These posterior regions of the brain with greater genetic influence are implicated for example in visual, perceptual, dorsal ("where") and ventral ("what") visuospatial processing streams (VNs). There was a low-density or paucity of genetically-influenced functional connections predominantly involving anterior regions or networks of the brain comprising Task Positive Networks (TPNs): FrontoParietal Networks (FPNs - dorsolateral prefrontal, orbital and polar frontal, midcingulate, insular and frontal opercular, superior and inferior parietal cortices); FrontoTemporal Networks (FTNs - inferior frontal, posterior opercular, early auditory, auditory association cortices); Sensorimotor Networks (SMNs - premotor, somatosensory, paralobular, and motor cortices); These anterior regions of the brain with lesser genetic influence are implicated in various TPN processes; for example in high-level cognitive and affective processes such as working memory, executive function, reasoning, attentional and impulse control, emotional judgement and decision making (FPNs); language and auditory processes (FTNs); action-planning and movement processes (SMN). There was a mix of high (posterior) and low (anterior) density of genetically influenced functional connections involving the extended Default Mode Network (eDMN). Specifically, there was a high density of genetically-influenced functional connections involving predominantly posterior-medial regions of eDMN - hippocampus and precuneus/posterior cingulate cortices; There was a low density of genetically influenced connections involving anterior regions (anterior cingulate and medial prefrontal) and lateral (inferior parietal, temporoparietooccipital) regions of the eDMN. The eDMN is involved in low-level cognitive and affective processes such as those involved in episodic memory retrieval, mental imagery, introspection, rumination, evaluation of self and others. These differences in genetic influence on posterior (more) vs. anterior (less) brain regions may have implications in terms of the environmental influence (e.g., education, school and work environment, family and home environment, social interaction with friends and peers, medications, nutrition, sports and physical exercise) on posterior (less) vs. anterior (more) portions of the brain during development and later in life. Gender-Specific Results & DiscussionAs noted at the population level, both males and females were under extensive genetic influence in terms of network interactions involving visual cortices. In addition, males were more genetically influenced in terms of network interactions involving auditory-language related cortices compared to females. This finding suggests that males may be more functionally "hard-wired" and females may be more environmentally influenced and shaped in terms of auditory-language systems than males. As noted at the population level, both males and females were under extensive genetic influence in terms of interactions involving the eDMN which is considered a central hub of the brain for various processes such as internal monitoring, rumination and evaluation of self and others, as noted previously. In addition, males also were more genetically influenced compared to females in terms of intranetwork and internetwork interactions of eDMN and other brain regions (occipital, temporal, parietal, and frontal regions) involved in various task-oriented processes and attending to and interacting with the environment which comprise part of the Task Positive Networks (TPNs). There were also nearly five times more genetically influenced functional connections in males (310) than females (64) suggesting that male brains are more genetically influenced, i.e. functionally "hard-wired", than females. This result suggests differences in genetic predisposition in males (more) vs. females (less) in terms of interplay of attending to task-oriented interactions with the environment (TPNs) vs. internal and external interactions with self and others (eDMN). This finding may also have implications in terms of brain plasticity differences in males (less) versus females (more) in terms of ability to react or adapt/maladapt to environmental influences (e.g. task completion demands, psychosocial stressors, positive and negative feedback, meditation, cognitive behavioral therapy, pharmacotherapy) and their overall malleability. These results reveal the similarities and differences of genetics and environmental influences on different connections, areas, and networks of the resting state functional brain in young healthy males and females with implications in development and later in life. This unique method can be applied in healthy as well as in patient populations to reveal the genetic and environmental influences on the brain. SignificanceThere were high vs. low genetic influences on posterior vs. anterior brain regions involved in low-level visuospatial processes vs. high-level cognitive processes such as reasoning and language respectively. This finding may have implications in terms of the brain to be environmentally influenced (e.g., school, work and home environment) during development and later in life. There were nearly five times more genetically influenced functional connections in males than females in brain regions involved in task-oriented interactions with environment vs. interactions with self and others. This finding may have implications in terms of brain plasticity differences in males (less) versus females (more) in terms of ability to adapt/maladapt to environmental influences (e.g. task completion demands, psychosocial stressors, various therapies) and their overall malleability. This is the first study to our knowledge to assess the heritability across young healthy adults both in general and specific to gender.

neuroscience↗

bioRxiv2021

Potential effects of life history on demographic genetic structure in stage-structured plant populations

O_LIPredicting temporal dynamics of genetic diversity is important for assessing long-term population persistence. In stage-structured populations, especially in perennial plant species, genetic diversity is often compared among life history stages, such as seedlings, juveniles, and flowerings, using neutral genetic markers. Because individuals in mature stages will die and be replaced by those in more immature stages over the course of time, the comparison among stages (sometimes referred to as demographic genetic structure) has been regarded as a proxy of potential genetic changes that accompany the turnover of constituent individuals. However, because demographic genetic structure had not been theoretically examined, the basic property and the validity of demographic genetic structure remained unclear. C_LIO_LIWe developed a matrix model which was made up of difference equations of expected heterozygosity, a common proxy of genetic diversity, of each life history stage at a neutral locus in stage-structured plant populations. Based on the model, we formulated demographic genetic structure as well as the annual change rate of expected heterozygosity (denoted as{eta} ). We obtained theoretical expectation of demographic genetic structure and{eta} from our model and compared them with computational results of stochastic simulation for randomly generated 3,000 life histories for model validation. We then examined the relationships of demographic genetic structure with effective population size Ne, which is the determinants of diversity loss per generation time, as well as with{eta} . C_LIO_LITheoretical expectations on{eta} and demographic genetic structure fitted well to the results of stochastic simulation, supporting the validity of our model. Demographic genetic structure varied independently of Ne and{eta} , while having a strong correlation with stable stage distribution: expected heterozygosity was lower in stages with fewer individuals. C_LIO_LIOur results indicate that demographic genetic structure strongly reflects stable stage distribution, rather than temporal genetic dynamics, and that inferring future genetic diversity solely from demographic genetic structure would be misleading. Instead of demographic genetic structure, the newly-defined statistics{eta} will be an useful tool to predict genetic diversity at the same time scale as population dynamics, facilitating evaluation on population viability from a genetic point of view. C_LI

genetics↗

bioRxiv2019

Genomic data enables genetic evaluation using data recorded on LMIC smallholder dairy farms

BackgroundGenetic evaluation is a central component of a breeding program. In advanced economies, most genetic evaluations depend on large quantities of data that are recorded on commercial farms. Large herd sizes and widespread use of artificial insemination create strong genetic connectedness that enables the genetic and environmental effects of an individual animals phenotype to be accurately separated. In contrast to this, herds are neither large nor have strong genetic connectedness in smallholder dairy production systems of many low to middle-income countries (LMIC). This limits genetic evaluation, and furthermore, the pedigree information needed for traditional genetic evaluation is typically unavailable. Genomic information keeps track of shared haplotypes rather than shared relatives. This information could capture and strengthen genetic connectedness between herds and through this may enable genetic evaluations for LMIC smallholder dairy farms. The objective of this study was to use simulation to quantify the power of genomic information to enable genetic evaluation under such conditions.\n\nResultsThe results from this study show: (i) the genetic evaluation of phenotyped cows using genomic information had higher accuracy compared to pedigree information across all breeding designs; (ii) the genetic evaluation of phenotyped cows with genomic information and modelling herd as a random effect had higher or equal accuracy compared to modelling herd as a fixed effect; (iii) the genetic evaluation of phenotyped cows from breeding designs with strong genetic connectedness had higher accuracy compared to breeding designs with weaker genetic connectedness; (iv) genomic prediction of young bulls was possible using marker estimates from the genetic evaluations of their phenotyped dams. For example, the accuracy of genomic prediction of young bulls from an average herd size of 1 (=1.58) was 0.40 under a breeding design with 1,000 sires mated per generation and a training set of 8,000 phenotyped and genotyped cows.\n\nConclusionsThis study demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in-situ records collected from farms with herd sizes of four cows or less. Across a range of breeding designs, genomic data enabled accurate genetic evaluation of phenotyped cows and genomic prediction of young bulls using data sets that contained small herds with weak genetic connections. The use of smallholder dairy data in genetic evaluations would enable the establishment of breeding programs to improve in-situ germplasm and, if required, would enable the importation of the most suitable external germplasm. This could be individually tailored for each target environment. Together this would increase the productivity, profitability and sustainability of LMIC smallholder dairy production systems. However, data collection, including genomic data, is expensive and business models will need to be carefully constructed so that the costs are sustainably offset.

genetics↗

bioRxiv2020

Reporting to the Convention on Biology Diversity: How are countries assessing and protecting their genetic diversity?

Genetic diversity is critically important for all species-domesticated and wild- to adapt to environmental change, and for ecosystem resilience to extreme events. International agreements such as the Convention on Biological Diversity (CBD) have committed to conserve and sustainably and equitably use all levels of biodiversity-genes, species and ecosystems-globally. However, assessment and monitoring of genetic diversity are often overlooked, and there are large knowledge and policy gaps regarding genetic diversity conservation. In this study, we present the first quantitative analysis of genetic diversity assessments conducted by Parties to the CBD. We conducted a detailed, systematic analysis of 114 CBD 5th (submitted 2014) and 6th (submitted 2018) National Reports to quantitatively assess actions, progress on targets, values and indicators related to genetic diversity. First, we found that the importance of genetic diversity is recognised by most Parties to the CBD, and that recognition increased over time. However, genetic targets mainly addressed genetic diversity within cultivated plants, farm animals, and crop wild relatives, with little focus on other wild species. Also, actions for conserving genetic diversity primarily concerned ex-situ facilities and policy, rather than monitoring and intervention for maintaining genetic diversity in situ. The most commonly used indicators of genetic diversity status were the number of genetic resources in conservation facilities, number of threatened breeds, and Red List Index, which are not well correlated to genetic erosion in most species -- highlighting that genetic change is poorly monitored by current indicators. Lastly, analyses of genetic data observations, indigenous use and knowledge of genetic diversity, and strategies being developed and implemented to conserve genetic diversity are highly under-reported. We make several recommendations for the post-2020 CBD Biodiversity Framework to improve awareness, assessment, and monitoring, and facilitate consistent and complete reporting of progress of genetic diversity in future National Reports. Article Impact StatementAn analysis of genetic diversity in CBD National Reports neglects non-domesticated species and demonstrates need for sufficient indicators.

scientific communication and education↗

bioRxiv2023

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. Author SummaryHuman genetics is becoming increasingly useful to help predict human traits across a population owing to findings from large-scale genetic association studies and advances in the power of genetic predictors. This provides an opportunity to potentially identify individuals that deviate from genetic predictions for a common phenotype under investigation. For example, an individual may be genetically predicted to be tall, but be shorter than expected. It is potentially important to identify individuals who deviate from genetic predictions as this can facilitate further follow-up to assess likely causes. Using 158,951 unrelated individuals from the UK Biobank, with height and LDL cholesterol, as exemplar traits, we demonstrate that approximately 0.15% & 0.12% of individuals deviate from their genetically predicted phenotypes respectively. We observed these individuals to be enriched for a range of rare clinical diagnoses, as well as rare genetic factors that may be causal. Our analyses also demonstrate several methods for detecting individuals who deviate from genetic predictions that can be applied to a range of continuous human phenotypes.

genetics↗

bioRxiv2017

Distinguishing genetic correlation from causation across 52 diseases and complex traits

Mendelian randomization (MR) is widely used to identify causal relationships among heritable traits, but it can be confounded by genetic correlations reflecting shared etiology. We propose a model in which a latent causal variable mediates the genetic correlation between two traits. Under the latent causal variable (LCV) model, trait 1 is fully genetically causal for trait 2 if it is perfectly genetically correlated with the latent causal variable, implying that the entire genetic component of trait 1 is causal for trait 2; it is partially genetically causal for trait 2 if it has a high genetic correlation with the latent variable, implying that part of the genetic component of trait 1 is causal for trait 2. To quantify the degree of partial genetic causality, we define the genetic causality proportion (gcp). We fit this model using mixed fourth moments E([Formula]12) and E([Formula]12) of marginal effect sizes for each trait, exploiting the fact that if trait 1 is causal for trait 2 then SNPs affecting trait 1 (large [Formula]) will have correlated effects on trait 2 (large 12), but not vice versa. We performed simulations under a wide range of genetic architectures and determined that LCV, unlike state-of-the-art MR methods, produced well-calibrated false positive rates and reliable gcp estimates in the presence of genetic correlations and asymmetric genetic architectures; we also determined that LCV is well-powered to detect a causal effect. We applied LCV to GWAS summary statistics for 52 traits (average N=331k), identifying partially or fully genetically causal effects (1% FDR) for 59 pairs of traits, including 30 pairs of traits with high gcp estimates (g[c]p > 0.6). Results consistent with the published literature included genetically causal effects on myocardial infarction (MI) for LDL, triglycerides and BMI. Novel findings included a genetically causal effect of LDL on bone mineral density, consistent with clinical trials of statins in osteoporosis. These results demonstrate that it is possible to distinguish between genetic correlation and causation using genetic data.

genetics↗

bioRxiv2022

Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a nonparametric test of genetic similarity first introduced by Li et al. for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution as originally recommended by Li and colleagues. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context. Author summaryThe genome-wide association study (GWAS) is a method used to identify genetic variants which contribute to the risk of developing disease. These genetic variants are frequently shared between conditions, such that the study of the genetic basis of one disease can be informed by knowledge of another, similar disease. This approach can be productive where the disease in question is rare such that a GWAS has less power to associate variants with the disease, but there exist larger GWAS of similar diseases. Existing methods do not measure genetic similarity precisely when patients are few. Here we assess a previously published method of testing for genetic similarity between pairs of diseases using GWAS data, the GPS test, against three other methods with the use of real and simulated data. We present a new computational procedure for carrying out the test and show that the GPS test is superior to its comparators in identifying genetic similarity when the sample size is small and when the genetic similarity signal is less strong. Use of the test will enable accurate detection of genetic similarity and the study of rarer conditions using data from better-characterised diseases.

genetics↗

bioRxiv2025

United States stakeholder insights on genetic testing for equine health and breeding

The S1094 United States Department of Agriculture (USDA) Multistate Research Project is a community of scientists working together to utilize equine genetics and genomics to improve the health and well-being of horses while also providing educational and genetic testing resources to those in the equine industry. To assess the knowledge and priorities of stakeholders regarding genetic testing, we conducted an online survey targeting horse owners and enthusiasts in the United States. Data were collected from March to October 2024 with 412 respondents from 44 states completing the survey. Most of the survey participants were horse owners, and the two most common age ranges for respondents were 31-50 (33.6%) and 51-70 (33.3%) years old. The two most common use categories for horses reported by respondents were sport and stock horses. The survey revealed that a majority of respondents were confident in their knowledge of genetics and that they relied primarily on breed and discipline organizations, as well as universities for their genetic information. Secondarily they relied upon social media and their veterinarians. Respondents expressed significant interest in genetic testing for diversity and performance traits. Across all horse use categories, arthritis, colic and intestinal disease, laminitis, metabolic disorders, and tendon and ligament issues had the greatest level of concern - all of which are complex genetic traits and will require collaborative efforts to discern levels of genetic involvement. Results from the survey underscore the need for continued outreach and education of horse owners, as well as further development of genetic testing tools to support informed decision-making among stakeholders in the equine industry. Lay SummaryGenetic testing helps horse owners, breeders, and breed registries and associations verify parentage, evaluate health, and improve performance. The S1094 USDA Multistate Research Project has a mission of improving genomics and genetics tools, as well as providing educational resources for those in the equine industry. To better understand the knowledge and priorities of stakeholders in the equine industry, we conducted an online survey netting responses from 412 individuals in the United States. Respondents were primarily horse owners who self-identified as confident in their knowledge of horse genetics. Furthermore, those responding indicated that their primary resources for information about genetics were breed and discipline association websites and universities. They also expressed interest in genetic testing for diversity and performance traits. Across all horse use categories, arthritis, colic and intestinal disease, laminitis, metabolic disorders, and tendon and ligament issues had the greatest level of concern. Given the complex nature of these conditions, collaborative efforts will be required to discern levels of genetic involvement. Respondents felt that genetic testing and education efforts have significant impacts on the equine industry because they aid owners in making breeding decisions and in the management of horse health and well-being. Teaser TextThis nationwide survey of U.S. horse owners discerned strong stakeholder interest in equine genetic testing, determined sources for knowledge acquisition, and highlighted key priorities for the development of genetic tools and educational materials.

genetics↗