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bioRxiv2023

An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions. Author SummaryAn important question in genetics is how the effects of mutations combine to alter phenotypes. Genetic interactions (epistasis) describe non-additive effects of pairs of mutations, but can also involve higher-order (three- and four-way etc.) combinations. Quantifying higher-order interactions is experimentally very challenging requiring a large number of measurements. Techniques based on deep mutational scanning (DMS) represent valuable sources of data to study epistasis. However, the best way to extract the relevant pairwise and higher-order epistatic coefficients (genetic interaction terms) from this data for the task of phenotypic prediction remains an unresolved problem. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values. Critically, this formalism currently only allows for two alleles (amino acid or nucleotide states) per sequence position, hampering applications in more biologically realistic scenarios. Here we present an extension of the Walsh-Hadamard transform that overcomes this limitation and demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic genetic landscapes.

systems biology↗

bioRxiv2023

Variation in plasticity in response to combined thermal-nutritional stress is genetically independent between sexes and across traits

Phenotypic plasticity helps animals to buffer the effects of increasing thermal and nutritional stress created by climate change. Plastic responses to single and combined stressors can vary among genetically diverged populations. However, less is known about how plasticity in response to combined stress varies among individuals within a population or whether such variation changes across life-history traits. This is important because individual variation within populations shapes population-level responses to environmental change. Here, we used isogenic lines of Drosophila melanogaster to assess plasticity of egg-to-adult viability and sex-specific body size for combinations of two temperatures (25{degrees}C or 28{degrees}C) and three diets (standard diet, low caloric diet, or low protein:carbohydrate ratio diet). Our results reveal substantial within-population genetic variation in plasticity for egg-to-adult viability and wing size in response to combined thermal-nutritional stress. This genetic variation in plasticity was a result of cross-environment genetic correlations that were often < 1 for both traits, as well as changes in the expression of genetic variation across environments for egg-to-adult viability. Cross-sex genetic correlations for body size were weaker when the sexes were reared in different conditions, suggesting that the genetic basis of traits may change with the environment. Further, our results suggest that plasticity in egg-to-adult viability is genetically independent from plasticity in body size. Importantly, plasticity in response to diet and temperature individually differed from plastic shifts in response to diet and temperature in combination. By quantifying plasticity and the expression of genetic variance in response to combined stress across traits, our study reveals the complexity of animal responses to environmental change, and the need for a more nuanced understanding of the potential for populations to adapt to ongoing climate change.

evolutionary biology↗

bioRxiv2023

Dissecting genetic correlation through recombinantperturbations: the role of developmental bias

Genetic correlation represents an important class of evolutionary constraint, which is itself evolvable. Empirical studies have found mixed results on whether genetic correlations change rapidly or slowly. This uncertainty challenges our ability to predict the outcome of selection. Despite the tremendous diversity and complexity of life forms, there are certain forms of life that are never observed. This might be because of developmental biases that restrict how organisms can evolve, or because they have low fitness in any environment yet available on Earth. Given that both developmental bias and selection can generate similar phenotypes, it is difficult to distinguish between the two causes of evolutionary stasis among related taxa. For example, remarkably invariant traits are observed spanning million years, such as wing shape in Drosophila wherein qualitative differences are rare within genera. Here, we ask whether the absence of certain combinations of traits, as indicated by genetic correlation, reflects developmental bias. However, much confusion and controversy remain over definitions of developmental bias, and probing it is challenging. We thus present a novel approach aiming to dissect genetic correlations and estimate the relative contribution of developmental bias in maintaining genetic correlations. We do so by leveraging a common but under-utilized type of data: genetic crosses. Through empirical analyses, we find that our approach can distinguish whether genetically correlated traits are developmentally constrained to covary. We also find that our developmental bias metric is an indicator of genetic correlation stability across conditions. Our framework presents a feasible way to dissect the mechanisms underlying genetic correlation and pleiotropy.

evolutionary biology↗

bioRxiv2023

Spatial autocorrelation of the environment influences the patterns and genetics of local adaptation

Environmental heterogeneity can lead to spatially varying selection, which can, in turn, lead to local adaptation. Population genetic models have shown that the pattern of environmental variation in space can strongly influence the evolution of local adaptation. In particular, when environmental variation is highly autocorrelated in space local adaptation will more readily evolve. Despite this long-held prediction, the evolutionary genetic consequences of different patterns of environmental variation have not been thoroughly explored. In this study, simulations are used to model local adaptation to different patterns of environmental variation. The simulations confirm that local adaptation is expected to increase with the degree of spatial autocorrelation in the selective environment, but also show that highly heterogeneous environments are more likely to exhibit high variation in local adaptation, a result not previously described. Spatial autocorrelation in the environment also influences the evolution and genetic architecture of local adaptation, with different combinations of allele frequency and effect size arising under different patterns of environmental variation. These differences influence the ability to characterise the genetic basis of local adaptation in different environments. Finally, I analyse a large-scale provenance trial conducted on lodgepole pine and find suggestive evidence that spatially autocorrelated environmental variation leads to stronger local adaptation in natural populations of lodgepole pine. Overall, this work emphasizes the profound importance that the spatial pattern of selection can have on the evolution of local adaptation and how spatial autocorrelation should be considered when formulating hypotheses in ecological and genetic studies. Lay SummaryMany species exhibit local adaptation to environmental variation across their ranges. Theoretical population genetics predicts that the evolution of local adaptation and patterns of genetic variation underlying it will be influenced by the spatial pattern of variation across a species range. However, this prediction has not been thoroughly explored for cases of complex heterogeneous landscapes. In this paper, I analyse simulations and empirical data to characterise the effects that the spatial pattern of environmental variation can have on the evolution of local adaptation and the genetics underlying it. From these analyses, I show that the pattern of environmental variation influences the average level of local adaptation, variation in local adaptation as well as the genetics underlying this important phenomenon.

evolutionary biology↗

bioRxiv2023

Protein evolvability under rewired genetic codes

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability -- the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether this robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively-parallel sequence-to-function assays to construct and analyze empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we demonstrate that alternative genetic codes can radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering non-standard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the biocontainment of synthetic organisms, respectively. Our findings demonstrate that the standard genetic code, a critical and near-universal cellular information processing system, not only mitigates replication and translation errors as compared to most alternative genetic codes, but also facilitates predictable and directional adaptive evolution by enabling evolving populations to readily find mutational paths to adaptation.

evolutionary biology↗

bioRxiv2023

Branching architecture affects genetic diversity within an individual tree

PremiseWhile a tree grows over many years, somatic mutations accumulate and generate a genetically diverse individual. Trees can transmit such mutations to subsequent generations, potentially enhancing the genetic diversity of the population. We study a mathematical model to understand the relationship between within-individual genetic variation and branching architecture. MethodsWe generate branching architecture by repeatedly adding two new branches (main and lateral daughter branches) to each terminal branch (mother branch). Tree shape is determined by two key parameters: daughter-mother ratio (DM) and main-lateral ratio (ML). During branch elongation, somatic mutations accumulate in the stem cells of a shoot apical meristem (SAM) at the tip of each branch. In branching, all the stem cells are passed on from the mother to the main daughter branch, but only one stem cell is chosen for the lateral daughter branch. We evaluate genetic variation by [Formula], the mean genetic differences between all pairs of branches of a tree, and examine how [Formula] varies with DM and ML while keeping the total branch length constant. Key results and Conclusions(1) [Formula] attains the maximum for an intermediate DM, when stem cells in the SAM are genetically homogeneous; (2) [Formula] decreases monotonically with DM when stem cells are genetically heterogeneous; and (3) [Formula] increases monotonically with ML. Even though the total branch length remains constant, the within-individual genetic variation differs substantially. The results demonstrate the importance of branching architecture in storing genetic diversity.

evolutionary biology↗

bioRxiv2023

Study on genetic differentiation of Schistosome japonicum intermediate hosts Oncomelania hupensis robertsoni in hilly regions of China: using the complete mitochondrial genome

ObjectiveOncomelania hupensis robertsoni is the only intermediate host of Schistosoma japonicum in western China, its genetic differentiation directly impacts the susceptibility of Schistosoma japonicum. This study aimed to sequence the complete mitochondrial genome of Oncomelania hupensis robertsoni Yunnan strain and analyze the genetic differentiation of Oncomelania hupensis robertsoni in hilly regions of China. MethodsSamples were from 14 administrative villages in Yunnan Province of China, with 30 Oncomelania hupensis per village, and the complete mitochondrial genome was sequenced. Additional, we retrieved 14 other region Oncomelania hupensis of complete mitochondrial sequences from GenBank, and a comprehensive analysis of the genetic differentiation of Oncomelania hupensis robertsoni was conducted by constructing phylogenetic trees, calculating genetic distances, and analyzing homogeneity. ResultsA total of 26 complete mitochondrial sequences were determined. The length of genome ranged from 15,181 to 15,187 bp, and the base composition of the genome was A+T (67.5%) and G+C content (32.5%). This genome encoded 37 genes, including 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes and a non-coding region rich in A+T. Using the Philippines genotypes as outgroup, the phylogenetic trees and homology analysis confirmed the existence of two distinct phylogroups, Oncomelania hupensis robertsoni and the remaining 9 provincial genotypes. Oncomelania hupensis robertsoni is subdivided into Oncomelania hupensis robertsoni Yunnan strain and Sichuan strain, with a genetic distance of 0.0834. Oncomelania hupensis robertsoni Yunnan strain is subdivided into two subbranches, "Yunnan North" and "Yunnan South", with a genetic distance of 0.0216, and the samples exhibited over 97% homology. ConclusionOncomelania hupensis robertsoni Yunnan strain exhibits a higher level of genetic homology and clear north-south differentiation, the distribution characteristics were closely associated with watershed distribution. This work reported the first mitochondrial genome of Oncomelania hupensis robertsoni Yunnan strain, which could be used as an important reference genome for Oncomelania hupensis, and also provide a theoretical basis for explaining the distribution pattern of Oncomelania hupensis robertsoni and control of schistosomiasis. Author SummaryOncomelania hupensis (O. hupensis) is the only intermediate host of Schistosoma japonicum (S. japonicum), O. hupensis residing in different geographical regions display morphological differences and genetic variations, along with varying susceptibility to S. japonicum. In this study, we sequenced 26 complete mitochondrial genome of O. hupensis robertsoni Yunnan strain (O. h. r. Yunnan strain), the length of genome ranged from 15,181 to 15,187 bp, and the base composition of the genome was A+T (67.5%) and G+C content (32.5%). This genome encoded 37 genes, including 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes and a non-coding region rich in A+T. Additional, we retrieved 14 other region O. hupensis of complete mitochondrial sequences from GenBank. The phylogenetic trees and homology analysis confirmed that O. hupensis robertsoni is subdivided into Yunnan strain and Sichuan strain, and O. h. r. Yunnan strain is subdivided into two subbranches, "Yunnan North" and "Yunnan South", the samples exhibited over 97% homology. This work reported the first mitochondrial genome of O. h. r. Yunnan strain, which could be used as an important reference genome for O. hupensis, and also provide a molecular biology-based theoretical foundation for understanding the genetic differentiation of O. hupensis.

genomics↗

bioRxiv2023

Engineering mice for female-biased progeny without impacting genetic integrity and litter size

The ability to influence the sex ratio of mammalian offspring has applications in agriculture and animal welfare. Here we describe a genetic system in mice that produces predominantly female progeny, reaching approximately ninety percent, without affecting litter size or introducing genetic modifications into the offspring. The system consists of a doxycycline-regulated cassette inserted into the Y chromosome, encoding dCas9 and RNA guides intended to impair the function of Y bearing sperm. While the cassette was originally intended to repress a spermatid maturation gene, our analyses did not detect meaningful changes in gene expression or sperm function that would account for the phenotype. Nevertheless, male mice carrying the cassette consistently produced female biased litters, and the effect was reversed by doxycycline, confirming that it depends on cassette expression. Similar female bias was observed following in vitro fertilization using sperm from transgenic males. These findings demonstrate a reproducible, genetically confined, and conditionally regulated system for sex ratio bias in mice. The strategy may prove adaptable across species, even without a complete mechanistic understanding. Graphical AbstractA single line genetic system producing female biased, genetically unmodified progeny in mice by conditionally impairing Y bearing sperm. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/568055v4_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@5f23b3org.highwire.dtl.DTLVardef@20ed2corg.highwire.dtl.DTLVardef@1b3007dorg.highwire.dtl.DTLVardef@e54cf6_HPS_FORMAT_FIGEXP M_FIG C_FIG One sentence summaryY chromosome manipulation yields mostly female mice, preserving litter size without genetic alteration in females. Significance statementThis study describes a genetic approach for producing predominantly female offspring in mice without altering litter size or transmitting genetic modifications to the progeny. A cassette inserted into the Y chromosome of males induces a strong female bias in offspring through a mechanism that remains unclear. The female offspring do not inherit the transgene and are genetically unmodified. This system provides a simple and conditional platform for sex ratio control in mammals, with potential applications in animal breeding and welfare.

bioengineering↗

bioRxiv2024

Asian Mus musculus: Subspecies Divergence, Genetic Diversity, and Historical Biogeography

The house mouse, Mus musculus, is a model organism that has greatly contributed to evolutionary research. Despite its significance, there remain gaps in our understanding of its phylogeography and population genetic structure in Asian regions. This comprehensive study, aims to elucidate the evolutionary history, genetic diversity, and distribution patterns of M. musculus. A diverse dataset of 281 M. musculus samples from across Asia was collected, covering 101 localities in 19 countries. Phylogenetic analysis using Cytochrome b (Cytb) and D-Loop (DL) region, unveiled well-supported lineages. These lineages correspond to four major subspecies: Mus musculus musculus, M. m. domesticus, M. m. castaneus, and M. m. bactrianus. The analysis suggests a monophyletic origin of these subspecies approximately 0.59 million years ago (Mya), followed by two main lineages: one consisting of M. m. domesticus ([~]0.59 Mya) and the other comprising M. m. castaneus, M. m. bactrianus, and M. m. musculus ([~]0.56 Mya). Genetic diversity varied among subspecies, with M. m. domesticus exhibiting the highest diversity due to its extensive global distribution and M. m. bactrianus exhibiting the lowest diversity due to restriction in the southwest Asia. Pairwise genetic distances and FST values confirmed significant genetic differentiation among the subspecies, underlining their historical isolation. Additionally, a Mantel test indicated that geographical distance played a pivotal role in shaping genetic differentiation. Demographic analysis revealed evidence of population expansions in M. m. domesticus, M. m. musculus, and M. m. castaneus, while M. m. bactrianus showed characteristics of neutral selection and genetic drift. Species distribution modeling, assessing both Current Conditions and the Last Glacial Maximum, indicated habitat shifts and losses during glacial periods, particularly in the eastern and northern regions of Asia. However, each subspecies displayed unique responses to climatic changes, reflecting their distinct ecological adaptations. Historical biogeography analysis pointed to complex origins and a network of dispersal and vicariance events that led to the contemporary distribution of subspecies. Deserts and Xeric Shrublands emerged as critical areas for diversification and speciation. This study contributes to our understanding of the phylogeography and population genetics of M. musculus in Asia, highlighting the significance of geographical factors and climatic fluctuations in shaping its evolutionary history and genetic diversity.

evolutionary biology↗

bioRxiv2024

Predicting nonlinear genetic relationships between traits in multi-trait evaluations by using a GBLUP-assisted Deep Learning model

BackgroundGenomic prediction aims to predict the breeding values of multiple complex traits, usually assumed to be normally distributed by the largely used statistical methods, thus imposing linear genetic correlations between traits. While statistical methods are of great value for genomic prediction, these methods do not account for nonlinear genetic relationships between traits. If such relationships exist, although statistical models do perform a fair linear approximation, their prediction accuracy is limited due to the nonlinearity. Deep learning (DL) is a promising methodology for predicting multiple complex traits, in scenarios where nonlinear genetic relationships are present, due to its capacity to capture complex and nonlinear patterns in large data. We proposed a novel hybrid DLGBLUP model which uses the output of the traditional GBLUP, and enhances its PGV by accounting for nonlinear genetic relationships between traits using DL. Using simulated data, we compared the accuracy of the PGV obtained with the proposed hybrid DLGBLUP model, a DL model, and the traditional GBLUP model - the latter being our baseline reference. ResultsWe found that both DL and DLGBLUP models either outperformed GBLUP, or presented equally accurate PGV, with a particular greater accuracy for traits presenting a strongly characterized nonlinear genetic relationship. Overall, DLGBLUP presented the highest prediction accuracy, up to 0.2 points higher than GBLUP, and smallest mean squared error of the PGV for all traits. Additionally, we evolved a base population over seven generations and compared the genetic progress when selecting individuals based on the additive PGV obtained by either DL, DLGBLUP or GBLUP. For all traits with a nonlinear genetic relationship, after the fourth generation, the observed genetic gain when selection was based on the additive PGV from GBLUP was always inferior to the one achieved from either DL or DLGBLUP. ConclusionsThe integration of DL into genomic prediction enables the possibility of modeling nonlinear relationships between traits. Moreover, by identifying these nonlinear genetic relationships, our DL and DLGBLUP models improved prediction accuracy, when compared to GBLUP. The possibility of nonlinear relationships between traits offers a different perspective into multi-trait evaluations and prediction, as well as into the traits evolution over generations, with potential to further improve selection strategies in commercial livestock breeding programs. Moreover, DLGBLUP shows that DL can be used as a complement to statistical methods, by enhancing their performance.

genomics↗

bioRxiv2024

Pervasive tissue-, genetic background-, and allele-specific gene expression effects in Drosophila melanogaster

The pervasiveness of gene expression variation and its contribution to phenotypic variation and evolution is well known. This gene expression variation is context dependent, with differences in regulatory architecture often associated with intrinsic and environmental factors, and is modulated by regulatory elements that can act in cis (linked) or in trans (unlinked) relative to the genes they affect. So far, little is known about how this genetic variation affects the evolution of regulatory architecture among closely related tissues during population divergence. To address this question, we analyzed gene expression in the midgut, hindgut, and Malpighian tubule as well as microbiome composition in the two gut tissues in four Drosophila melanogaster strains and their F1 hybrids from two divergent populations: one from the derived, European range and one from the ancestral, African range. In both the transcriptome and microbiome data, we detected extensive tissue- and genetic background-specific effects, including effects of genetic background on overall tissue specificity. Tissue-specific effects were typically stronger than genetic background-specific effects, although the two gut tissues were not more similar to each other than to the Malpighian tubules. An examination of allele specific expression revealed that, while both cis and trans effects were more tissue-specific in genes expressed differentially between populations than genes with conserved expression, trans effects were more tissue-specific than cis effects. Despite there being highly variable regulatory architecture, this observation was robust across tissues and genetic backgrounds, suggesting that the expression of trans variation can be spatially fine-tuned as well as or better than cis variation during population divergence and yielding new insights into cis and trans regulatory evolution. Author SummaryGenetic variants regulating gene expression can act in cis (linked) or in trans (unlinked) relative to the genes they affect and are thought to be important during adaptation because they can spatially and temporally fine-tune gene expression. In this study, we used the fruit fly Drosophila melanogaster to compare gene expression between inbred parental strains and their offspring in order to characterize the basis of gene expression regulation and inheritance. We examined gene expression in three tissues (midgut, hindgut, and Malpighian tubule) and four genetic backgrounds stemming from Europe and the ancestral range in Africa. Additionally, we characterized the bacterial community composition in the two gut tissues. We detected extensive tissue- and genetic background-specific effects on gene expression and bacterial community composition, although tissue-specific effects were typically stronger than genetic background effects. Genes with cis and trans regulatory effects were more tissue-specific than genes with conserved expression, while those with trans effects were more tissue-specific than those with cis effects. These results suggest that the expression of trans variation can be spatially fine-tuned as well as (or better than) cis variation as populations diverge from one another. Our study yields novel insight into the genetic basis of gene regulatory evolution.

evolutionary biology↗

bioRxiv2024

Comparing newly developed SNP barcode panels with microsatellites to explore population genetics of malaria parasites in the Peruvian Amazon

Malaria molecular surveillance (MMS) can provide insights into transmission dynamics, guiding national control/elimination programs. Considering the genetic differences among parasites from different areas in the Peruvian Amazon, we previously designed SNP barcode panels for Plasmodium vivax (Pv) and P. falciparum (Pf), integrated into AmpliSeq assays, to provide population genetics estimates of malaria parasites. These AmpliSeq assays are ideal for MMS: multiplexing different traits of interest, applicable to many use cases, and high throughput for large numbers of samples. The present study compares the genetic resolution of the SNP barcode panels in the AmpliSeq assays with widely used microsatellite (MS) panels to investigate Amazonian malaria parasites. Malaria samples collected in remote areas of the Peruvian Amazon (51 Pv & 80 Pf samples) were characterized using the Ampliseq assays and MS. Population genetics estimates (complexity of infection, genetic diversity and differentiation, and population structure) were compared using the SNP barcodes (Pv: 40 SNPs & Pf: 28 SNPs) and MS panels (Pv: 16 MS & Pf: 7 MS). The genetic diversity of Pv (expected heterozygosity, He) was similar across the subpopulations for both makers: HeMS = 0.68 - 0.78 (p = 0.23) and HeSNP = 0.36 - 0.38 (p = 0.80). Pairwise genetic differentiation (fixation index, FST) was also comparable: FST-MS = 0.04 - 0.14 and FST-SNP = 0.03 - 0.12 (p = 0.34 - 0.85). No geographic clustering was observed with any panel. In addition, Pf genetic diversity trends (HeMS = 0 - 0.48 p = 0.03 - 1; HeSNP = 0 - 0.09, p = 0.03 - 1) and pairwise FST comparisons (FST-MS= 0.14 - 0.65, FST-SNP = 0.19 - 0.61, p = 0.24 - 0.83) were concordant between the panels. Similar population structure clustering was observed with both SNP and MS, highlighting one Pf subpopulation in an indigenous community. The SNP barcodes in the Pv AmpliSeq v2 Peru and Pf AmpliSeq v1 Peru assays offer comparable results to MS panels when investigating population genetics in Pv and Pv populations. Therefore, the AmpliSeq assays can efficiently characterize malaria transmission dynamics and population structure and support malaria elimination efforts in Peru.

genomics↗

bioRxiv2024

Optimal Prescriptive Treatments for Ovarian Cancer with Genetic Data

Among the cancers affecting the female population, Ovarian Cancer (OC), while being relatively rare, is the leading cause of gynecological cancer-related deaths, with overall 5-year survival rates of approximately 50% for all stages combined. This is because of the challenges associated with the diagnosis, resulting in detection at advanced stages of OC, coupled with the slow progress in effective treatment options since the approval of platinum-based chemotherapy in the late 1970s. There has been a relative lack of sophisticated methods based on Machine Learning (ML) models that use genetic data for better prediction of Ovarian Cancer outcomes and result in more effective treatment recommendations. Therefore, there is an unmet clinical need to create models that allow physicians to make informed decisions based on all available data, including patient demographic, social, health, and genomic data. Hence, we develop new techniques for leveraging genetic information in prescribing optimal treatments for patients with OC, using a publicly available dataset from the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) trial. Our approach is able to transform genotype sequencing information into a simple tabular form that can then be used as the input to any ML model. Coupled with the recorded treatment regimen and clinical parameters of matched patients from the genetic dataset, we estimate the treatment effect in terms of mortality prediction and use it to prescribe the optimal treatment for any given patient. By including the genetic features engineered through our proposed method, our models have a higher accuracy than the models without genetic information embedded. The increase in predictive accuracy demonstrates the improved efficacy of our method in the predictive setting. Furthermore, in the prescriptive setting, the models including genetic features output different treatment choices for patients, showing the impact of their inclusion. This is further highlighted by the feature importance of the genetic features such as mutations in the FAT3, BRCA1, BRCA2, and NF1 genes, where they rank highly with a tighter aggregation of the top features, relative to the sharp drop-off in feature importance after the top feature in the models without genetic data. Taken together, in summary, our models will allow oncologists to make more informed and accurate decisions, incorporating a patients genetic data with all other available clinical information, which has the potential for improved prognosis and better long-term survival outcomes for Ovarian Cancer patients.

cancer biology↗

bioRxiv2024

Population genomics reveals strong impacts of genetic drift without purging and guides conservation of bull and giant kelp

Kelp forests are declining in many parts of the northeast Pacific1-4. In small populations, genetic drift can reduce adaptive variation and increase fixation of recessive deleterious alleles5-7, but natural selection may purge harmful variants8-10. To understand evolutionary dynamics and inform restoration strategies, we investigated genetic structure and the outcomes of genetic drift and purging by sequencing the genomes of 429 bull kelp (Nereocystis luetkeana) and 211 giant kelp (Macrocystis pyrifera sensu lato11; cf. 12) from the coastlines of British Columbia and Washington. We identified 6-7 geographically and genetically distinct clusters in each species. Low effective population size was associated with low genetic diversity and high inbreeding coefficients (including increased selfing rates), with extreme variation in these genetic health indices among bull kelp populations but more moderate variation in giant kelp. We found no evidence that natural selection is purging putative recessive deleterious alleles in either species. Instead, genetic drift has fixed many such alleles in small populations of bull kelp, leading us to predict (1) reduced within-population inbreeding depression in small populations, which may be associated with an observed shift toward increased selfing rate, and (2) hybrid vigour in crosses between small populations. Our genomic findings imply several strategies for optimal sourcing and crossing of populations for restoration and aquaculture, but which require experimental validation. Overall, our work reveals strong genetic structure and suggests that conservation strategies should consider the multiple health risks faced by small populations whose evolutionary dynamics are dominated by genetic drift.

evolutionary biology↗

bioRxiv2024

Genetic diversity and population structure of pedunculate oaks (Quercus robur) in Wytham Woods

O_LIGenetic diversity is fundamental for adaptation to changing environments. It is particularly important in forest trees because of their significant role in natures contribution to people. However, their genetic diversity has been significantly changed by human activities in the past centuries. C_LIO_LIThis paper investigates a focal site, the Wytham Woods, one of the most researched woodlands on Earth, and presents a population genetic study on pedunculate oaks (Quercus robur), a keystone species in the ecosystem. We characterised 210 trees with Genotyping by Sequencing (GbS) and quantified levels of genetic diversity across stands with different histories and management regimes. C_LIO_LIWe detected only a weak population structure within the 218,567 SNPs, such that most genetic variation occurred within but not among stands, which included semi-natural woodland areas and plantations aged between 200 to 50 years ago. We also observed little difference in observed and expected heterozygosity among stand types, but detected some inbreeding in the youngest plantation. We discovered 26,174 SNPs (11.98%) that were highly differentiated and under potential selection. C_LIO_LIWe suggest that the life history traits of oak contribute to its resistance against genetic erosion, which is also observed in beeches, spruces, and pines. Preference for oaks as a timber tree and the tendency to use local seed source might have resulted in the homogeneous population structure. However, tree-to-tree differences may harbour variation in putative adaptive loci. Our study contributes crucial baseline information on for conservation and management of human-modified woodlands, in addition to supporting long-term ecological studies on many other species, which depend on this keystone oak species. C_LI Societal Impact StatementOur study highlights the importance of monitoring and preserving genetic diversity in forest trees, particularly in keystone species like pedunculate oaks. Human activities, including land use changes and forestry practices, could influence their genetic diversity and potentially alter natures contribution to people. We demonstrate how understanding the genetic structure of oaks in stands ranging from semi-natural to plantations could (1) shed light on the natural history and the consequence of human activities in Wytham Woods, (2) support the many continuing, long-term ecological studies including adaptational potential in the oak population, and (3) be translated for other co-occurring species and other woodlands for effective genetic monitoring at large.

genomics↗

bioRxiv2025

Integrative analysis of taste genetics and the dental plaque microbiome in early childhood caries

Early childhood caries (ECC) is a multifactorial disease mainly caused by the oral microbiome; however, it is also influenced by host genetics and environmental factors. The combinatorial analysis of these multiple factors influencing ECC susceptibility requires further research. This study investigated the interplay between genetic variants in taste-related genes and the microbiome in ECC, targeting taste genes because of their role in taste preference and potential interactions with oral fungi and bacteria. Using a case-control design involving 538 children, we obtained dental plaque microbiome profiles and genetic variants across 55 candidate genes through next-generation sequencing. Our association analysis for taste genetics and ECC outcome used the socioeconomic factor index (SEFI) and rural-urban status as confounders. We observed a few taste gene variants associated with ECC and microbial diversity. However, no specific association was observed between the variants and cariogenic species. Furthermore, our analysis indicated that Streptococcus mutans is a partial mediator between these genetic variants and ECC outcomes. Machine learning models integrating microbiome, genetics, and covariates achieved robust ECC vs. caries-free classification (AUROC = 0.96), with Streptococcus mutans, rural-urban status, a bitter taste receptor variant, Candida dubliniensis, and SEFI as the top ECC-associated factors. Our findings highlight the association between host genetics and the oral microbiome, underscoring the need for multiomics approaches in ECC risk assessment. HighlightsO_LIS. mutans, C. dubliniensis, and taste genetic variants are associated with ECC. C_LIO_LIRural-urban status and SEFI score are among the top social markers for ECC prediction. C_LIO_LITaste-related genetic factors modulate the composition of dental plaque microbiome. C_LI

genomics↗

bioRxiv2025

Genetic structure of sugar kelp in the St. Lawrence Estuary and Gulf (Quebec, Canada)

The sugar kelp, Saccharina latissima is cultivated at low scale in Quebec, Canada and current practice involve seeding meiospores or gametophyte stocks onto spools carrying twine and transferring these to a seaweed farm site. As the stocks can originate from locations spanning several hundreds of kilometers from the farm sites, such practice could involve genetic contamination and disrupt local adaptations. Assessing genetic structure can inform of the potential risks associated with this practice. Here we characterized the genetic diversity and structure of S. latissima from locations in the St. Lawrence Estuary and Gulf at both microsatellite loci (308 sporophytes at 22 loci in 16 sites) and genomic markers (228 sporophytes at 6578 single nucleotide polymorphisms in 13 sites). Several populations had low heterozygosity values and significant FIS values at microsatellite loci. No genetic structure was found among populations with microsatellite loci but strong genetic structuring was found with the genomic data. Population structure followed a geographic pattern and was congruent with major currents. Individuals from the wild population in the vicinity of the farm site were genetically distinct from the sporophytes on the growing lines that belong to a genetically distinct group. There was no significant genetic differentiation between wild individuals living in proximity of the farm site and another wild population of the same area. Hence aquaculture practices have not resulted in changes in the genetic composition of the wild population at large scale. Our results are important to guide future conservation efforts and for the seaweed farming industry.

evolutionary biology↗

bioRxiv2025

Evolution under Domestication: Genetic differentiation in black soldier fly (Hermetia illucens) populations subjected to recent selective breeding

The black soldier fly (BSF; Hermetia illucens) is widely utilized in commercial and research applications for waste bioconversion and sustainable protein production. However, prolonged captivity and artificial selection can shape genetic diversity, potentially influencing adaptability and long-term population stability. This study examined how recent selective breeding, genetic drift, and relaxed selection have influenced genetic differentiation in BSF populations over a short timeframe. Using mitochondrial cytochrome oxidase I (CO1) and genome-wide RAD sequencing, population structure, heterozygosity, and selection signatures across eleven BSF populations, including selectively bred, wild-derived, and commercial strains were analysed. Results revealed that rapid genetic shifts have occurred within selectively bred populations (LA to LE) over [~]5 years, driven by artificial selection, subsequent relaxation, and environmental adaptation. The decline in effective population size (Ne) observed post-COVID-19 suggests recent bottlenecks, which may have further contributed to genetic drift and differentiation. While domesticated populations exhibited reduced genetic diversity and signs of inbreeding, wild-type population under short captivity retained higher heterozygosity. Genome-wide analyses further identified adaptive divergence among populations, with balancing selection and selective sweeps shaping genetic variation. Notably, despite shared ancestry, genetic differentiation persisted in selectively bred populations, reinforcing that selection and environmental pressures continue to influence their genomic landscape even after targeted selection was relaxed. These findings underscore the need to monitor genetic diversity in BSF breeding programs to maintain adaptability, enhance resilience, and mitigate risks from artificial selection and population collapse.

evolutionary biology↗