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bioRxiv2023

Genetic architecture of individual meiotic crossover rate and distribution in a large Atlantic Salmon (Salmo salar) breeding population.

Meiotic recombination through chromosomal crossovers ensures proper segregation of homologous chromosomes in meiosis, while also breaking down linkage disequilibrium and shuffling alleles at loci located on the same chromosome. Rates of recombination can vary between species, but also between and within individuals, sex and chromosomes within species. Indeed, the Atlantic salmon genome is known to have clear sex differences in recombination with female biased heterochiasmy and markedly different landscapes of crossovers between males and females. In male meiosis, crossovers occur strictly in the telomeric regions, whereas in female meiosis crossovers tend to occur closer to the centromeres. However, little is known about the genetic control of these patterns and how this differs at the individual level. Here, we investigate genetic variation in individual measures of recombination in >5000 large full-sib families of a Norwegian Atlantic salmon breeding population with high-density SNP genotypes. We show that females had 1.6x higher crossover counts (CC) than males, with autosomal linkage maps spanning a total of 2174 cM in females and 1483 cM in males. However, because of the extreme telomeric bias of male crossovers, female recombination is much more important for generation of new haplotypes with 8x higher intra-chromosomal genetic shuffling than males. CC was heritable in females (h2 = 0.11) and males (h2 = 0.10), and shuffling was also heritable in both sex but with a lower heritability in females (h2 = 0.06) than in males (h2 = 0.11). Inter-sex genetic correlations for both traits were close to zero, suggesting that rates and distribution of crossovers are genetically distinct traits in males and females, and that there is a potential for independent genetic change in both sexes in the Atlantic Salmon. Together, these findings give novel insights into the genetic architecture of recombination in salmonids and contribute to a better understanding of how rates and distribution of recombination may evolve in eukaryotes more broadly.

genetics↗

bioRxiv2023

Genetic similarity between relatives provides evidence on the presence and history of assortative mating

Assortative mating - the non-random mating of individuals with similar traits - is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago. Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives. Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in nine out of sixteen examined traits. The same traits showed elevated similarity between relatives, especially distant relatives. Six of the nine traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits. The implications of this research extend to genetic methodology and the understanding of social and economic disparities.

genetics↗

bioRxiv2023

GetPrimers: a generalized PCR-based genetic targeting primer designer enabling easy and standardized targeted gene modification across multiple systems

Genetic targeting (e.g., gene knockout and tagging) based on polymerase chain reaction (PCR) is a simple yet powerful approach for studying gene functions. Although originally developed in classic budding and fission yeast models, the same principle applies to other eukaryotic systems with efficient homologous recombination. One-step-PCR-based genetic targeting is conventionally used but the sizes of the homologous arms that it generates for recombination-mediated genetic targeting are usually limited. Alternatively, gene targeting can also be performed via fusion PCR, which can create homologous arms that are orders of magnitude larger, therefore substantially increases the efficiency of recombination-mediated genetic targeting. Here we present GetPrimers (https://www.evomicslab.org/app/getprimers/), a generalized computational framework and web tool to assist automatic targeting and verification primer design for both one-step-PCR-based and fusion-PCR-based genetic targeting experiments. Moreover, GetPrimers by design runs for any given genetic background of any species with full genome scalability. Therefore, GetPrimers is capable of empowering high-throughput functional genomics assays at multi-population and multi-species levels. Comprehensive experimental validations have been performed for targeting and verification primers designed by GetPrimers across multiple organism systems and experimental setups. We anticipate GetPrimers to become a highly useful and popular tool to facilitate easy and standardized gene modification across multiple systems. Take AwaysDeveloped GetPrimers for generalized primer design of PCR-based genetic targeting. GetPrimers shines with its high versatility and genome-wide scalability. GetPrimers was validated across multiple organisms and experimental setups.

genetics↗

bioRxiv2024

Controlling for polygenic genetic confounding in epidemiologic association studies

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically-unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

genetics↗

bioRxiv2024

The Genetic Basis of Incipient Sexual Isolation in Drosophila melanogaster

Speciation is a fundamental evolutionary process, but the genetic changes accompanying speciation are difficult to determine since true species do not produce viable and fertile offspring. Populations of the same species that are that are partially reproductively isolated are incipient species that can be used to assess genetic changes that occur prior to speciation. Drosophila melanogaster from Zimbabwe, Africa are genetically differentiated and partially sexually isolated from cosmopolitan populations worldwide: cosmopolitan males have poor mating success with Zimbabwe females. We used the cosmopolitan D. melanogaster Genetic Reference Panel (DGRP) to show there is significant genetic variation in mating success of DGRP males with Zimbabwe females, map genetic variants and genes associated with variation in mating success and determine whether mating success to Zimbabwe females is associated with other quantitative traits previously measured in the DGRP. We performed three genome wide association analyses: for the DGRP lines, for selected flies with high or low mating success from an advanced intercross population (AIP) derived from DGRP lines, and for lines derived from 18 generations of divergent selection from the AIP for mating success with Zimbabwe females. The basis of incipient sexual isolation is highly polygenic and associated with the common African inversion In(3R)K and the amount of the sex pheromone 5,9-heptacosadiene in DGRP females. We functionally validated the effect of eight candidate genes using RNA interference. These candidate gene and variant associations provide testable hypotheses for future studies investigating the molecular genetic basis of incipient sexual isolation in D. melanogaster.

genetics↗

bioRxiv2024

The genetic architecture of resistance to flubendiamide insecticides in Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)

Insecticide resistance is a major problem in food production, environmental sustainability, and human health. The cotton bollworm Helicoverpa armigera is a globally distributed crop pest affecting over 300 crop species. H. armigera has rapidly evolved insecticide resistance, making it one of the most damaging pests worldwide. Understanding the genetic basis of insecticide resistance provides insights to develop tools, such as molecular markers, that can be used to slow or prevent the evolution of resistance. We explore the genetic architecture of H. armigera resistance to a widely used insecticide, flubendiamide, using two complementary approaches: genome-wide association studies (GWAS) in wild-caught samples and quantitative trait locus (QTL) mapping in a controlled cross of susceptible and resistant laboratory strains. Both approaches identified one locus on chromosome 2, revealing two SNPs within 976 bp that can be used to monitor field resistance to flubendiamide. This was the only region identified using linkage mapping, though GWAS revealed additional sites associated with resistance. Other loci identified by GWAS in field populations contained known insecticide detoxification genes from the ATP-binding cassette family, ABCA1, ABCA3, ABCF2 and MDR1. Our findings revealed an oligogenic genetic architecture, in contrast to previous reports of monogenic resistance associated with the ryanodine receptor. This work elucidates the genetic basis of rapidly evolving insecticide resistance and will contribute to the development of effective insecticide resistance management strategies. Author summaryInsecticide resistance in agricultural pests challenges food security, environmental sustainability, and human health. The cotton bollworm Helicoverpa armigera is resistant to various insecticide classes as well as Bacillus thuringiensis toxins. Understanding the genetic basis of this resistance is crucial for developing effective insecticide resistance management (IRM) strategies. Our study investigated the genetic architecture of resistance of H. armigera to flubendiamide and identified SNPs associated with resistance. We used two approaches: association mapping in wild-derived samples and QTL mapping in a controlled backcross of susceptible and resistant laboratory strains. One specific region on chromosome 2 was found in both GWAS and QTL mapping analysis and showed significant potential as a PCR-based marker for monitoring resistance to flubendiamide in H. armigera. Additionally, the GWAS identified five more SNPs in the field populations. The candidate genes identified are primarily associated with insecticide detoxification mechanisms and calcium homeostasis. Our study advances the understanding of the complex genetic architecture of flubendiamide resistance in H. armigera, providing valuable tools for IRM programs and establishing a basis for future studies.

genetics↗

bioRxiv2024

Evidence of genetic isolation and differentiation among historically fragmented British populations of common juniper, Juniperus communis L.

Habitat fragmentation and populations isolation pose a threat to the genetic diversity and adaptability of many species. The common juniper, Juniperus communis L., a keystone species for juniper scrub habitat and one of only three conifers that are native to the UK, has been in decline for more than a century in the UK and across its European range. Remnant UK juniper populations are now highly fragmented and often small, which has raised concerns for their resilience, especially in the face of climate change and the introduction of novel pathogens, such as Phytophthora austrocedri. This work presents a baseline genetic survey of native UK juniper populations and compares patterns of diversity between populations and among three population centres in southern England, the Lake District, and Scotland using both Single Nucleotide Polymorphism (SNP) and Simple Sequence Repeat (SSR) genetic markers. The aim was to evaluate the standing genetic diversity of native juniper stands, the impacts of habitat fragmentation, and to determine whether juniper populations are genetically isolated from one another. We found that juniper populations, while not completely isolated from one another, face substantial barriers to gene flow, especially between the three population centres. These centres also show different patterns of genetic diversity, indicating varying levels of internal gene flow and inbreeding. Our findings can form a baseline from which to monitor the effectiveness of conservation activities, prioritize populations of concern, and guide genetic rescue efforts.

genetics↗

bioRxiv2024

Sex and genetic background influence intravenous oxycodone self-administration in the Hybrid Rat Diversity Panel

Opioid Use Disorder (OUD) is an ongoing worldwide public health concern. Genetic factors contribute to multiple OUD-related phenotypes, such as opioid-induced analgesia, initiation of opioid use, and opioid dependence. Here, we present findings from a behavioral phenotyping protocol using male and female rats from 15 genetically diverse inbred strains from the Hybrid Rat Diversity Panel (HRDP). We used a self-administration paradigm to measure the acquisition of oxycodone intake during ten 2-hour sessions and escalation of oxycodone use during ten 12-hour sessions. During both the acquisition and escalation phases of self-administration, we observed that genetic background and sex influence oxycodone intake. The heritability of oxycodone intake phenotypes ranged between 0.26 to 0.54, indicating that genetic background plays a major role in the variability of oxycodone consumption. Genetic background and sex also influenced additional phenotypes recorded during oxycodone self-administration including lever discrimination and timeout responding. The genetic contribution to these traits was slightly more moderate, with heritability estimates ranging between 0.25 to 0.42. Measures of oxycodone intake were highly positively correlated between acquisition and escalation phases. Interestingly, the efficacy of oxycodone analgesia was positively correlated with oxycodone intake during the escalation phase, indicating that the initial behavioral responses to oxycodone may predict self-administration phenotypes. Together, these data demonstrate that sex and genetic background are major contributors to oxycodone self-administration phenotypes.

genetics↗

bioRxiv2025

Leveraging ancestral recombination graphs for quantitative genetic analysis of rice yield in indica and japonica subspecies

Rice (Oryza sativa L.) has two main subspecies, indica and japonica, which coexist in many regions but are often treated separately during breeding. Combining both subspecies in quantitative genetic analyses could enhance genetic improvement, however, this requires appropriately modelling their genetic history. The ancestral recombination graph (ARG) is an effective population genetics tool that comprehensively and succinctly represents a species genetic history. This study evaluated the use of an ARG, encoded as a tree sequence, to improve quantitative genetic analyses of indica and japonica rice. Using data from Uruguays National Rice Breeding Program, we inferred ancestral alleles, constructed and dated an ARG, and examined its application in genomic prediction and genome-wide association studies. We compared the predictive ability of a branch-based relationship matrix (BRM) built from an ARG against conventional relationship matrices from pedigree and single nucleotide polymorphism (SNP) site data. We then estimated BRMs SNP site effects to identify potential sites of interest and better understand how these map onto the tree sequence branches. The results showed that the ARG captured key biological signals, encoded genomic data more efficiently than conventional formats, and resulted in the highest predictive ability when combining both subspecies. Although the ARG-based approach did not substantially outperform conventional approaches for between-species prediction, this approach holds promise for plant breeding with larger datasets and could enhance genome-wide association studies by elucidating haplotype ancestry and the evolution of their value. Overall, our results demonstrated the potential of ARGs for the quantitative genetic analysis of diverse populations.

genetics↗

bioRxiv2025

Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats

The intestinal microbiome influences health and disease. Its composition is affected by host genetics and environmental exposures. Understanding host genetic effects is critical but challenging in humans, due to the difficulty of detecting, mapping and interpreting them. To address this, we analysed host genetic effects in four cohorts of outbred laboratory rats exposed to distinct but controlled environments. We found that polygenic host genetic effects were consistent across environments. We identified three replicated microbiome-associated loci, one of which involved a sialyltransferase gene and Paraprevotella. We found a similar association in a human cohort, between ST6GAL1 and Paraprevotella, both of which have been linked with immune and infectious diseases. Moreover, we found evidence of indirect genetic effects on microbiome phenotypes, which substantially increased their total genetic variance. Finally, we identified a novel mechanism whereby indirect genetic effects can contribute to "missing heritability".

genetics↗

bioRxiv2025

Genetic and environmental influences on the distributions of three chromosomal drive haplotypes in maize

Meiotic drive elements are regions of the genome that are transmitted to progeny at frequencies that exceed Mendelian expectations, often to the detriment of the organism. In maize there are three prevalent chromosomal drive elements known as Abnormal chromosome 10 (Ab10), K10L2, and the B chromosome. There has been much speculation about how these drivers might interact with each other and the environment in traditional maize landraces and their teosinte ancestors. Here we used genotype-by-sequencing data to score more than 10,000 maize and teosinte lines for the presence or absence of each driver. Less than [~]0.5% of modern inbred lines carry chromosomal drivers. Among individuals from 5331 open-pollinated landraces, 6.32% carried Ab10, 5.16% carried K10L2, and 12.28% carried at least one B chromosome. Using a GWAS approach we identified unlinked loci that associate with the presence or absence of the selfish genetic elements. Many genetic modifiers are positively associated with the drivers, suggesting that there may have been selection for alleles that ameliorate their negative fitness consequences. We then assessed the contributions of population structure, associated loci, and the environment on the distribution of each chromosomal driver. There was no significant relationship between any chromosomal driver and altitude, contrary to conclusions based on smaller studies. Our data suggest that the distribution of the major chromosomal drivers is primarily influenced by neutral processes and the deleterious fitness consequences of the drivers themselves. While each driver has a unique relationship to genetic background and the environment, they are largely unconstrained by either. AUTHOR SUMMARYMeiotic drivers are selfish genetic elements that are transmitted to progeny at frequencies that exceed what is expected by chance. There are three such meiotic drivers in maize known as Ab10, K10L2 and the B chromosome. Prior data indicate that each driver lowers plant fitness to some degree, partially explaining the fact that they are usually observed at low frequencies, but the impact of environment and genetic modifiers has not been investigated. Here we identified the presence or absence of each driver using low coverage sequencing data from over 10,000 maize inbreds, open pollinated landraces, and accessions of teosinte -- the grass-like ancestor of maize. We found that they exist at 5-12% frequencies in open pollinated varieties, but are rare in modern inbreds. We went on to use location data to assess the impact of environmental variables on their distribution, and applied genome-wide association methods to look for genetic modifiers that might contribute to their prevalence. We found that neither the environment nor genetic modifiers have strong influences on the distribution of the three meiotic drive elements, suggesting that their low but consistent frequencies are determined primarily by the fitness consequences of the drivers themselves.

genetics↗

bioRxiv2025

Impact of trait measurement error on quantitative genetic analysis of computer vision derived traits

BackgroundQuantitative genetic analysis of image- or video-derived phenotypes is increasingly being performed for a wide range of traits. Pig body weight values estimated by a conventional approach or a computer vision system can be considered as two different measurements of the same trait, but with different sources of phenotyping error. Previous studies have shown that trait measurement error, defined as the difference between manually collected phenotypes and image-derived phenotypes, can be influenced by genetics, suggesting that the error is systematic rather than random and is more likely to lead to misleading quantitative genetic analysis results. Therefore, we investigated the effect of trait measurement error on genetic analysis of pig body weight (BW). ResultsCalibrated scale-based and image-based BW showed high coefficients of determination and goodness of fit. Genomic heritability estimates for scale-based and image-based BW were mostly identical across growth periods. Genomic heritability estimates for trait measurement error were consistently negligible, regardless of the choice of computer vision algorithm. In addition, genome-wide association analysis revealed no overlap between the top markers identified for scale-based BW and those associated with trait measurement error. Overall, the deep learning-based regressions outperformed the adaptive thresholding segmentation methods. ConclusionsThis study showed that manually measured scale-based and image-based BW phenotypes yielded the same quantitative genetic results. We found no evidence that BW trait measurement error could be influenced, at least in part, by genetic factors. This suggests that trait measurement error in pig BW does not contain systematic errors that could bias downstream genetic analysis.

genetics↗

bioRxiv2025

Unlocking the Forgotten Dimension of Biodiversity: A Scalable Genetic Diversity Index for Multi-Species Analysis

Geographic patterns of community-level genetic diversity provide key information about evolutionary processes and are increasingly relevant for conservation planning under rapid environmental change. Yet, continental-scale, multi-taxon assessments of genetic diversity remain rare, largely due to difficulties in integrating heterogeneous datasets across species and markers. Here, we develop a scalable framework to quantify and map nucleotide diversity ({pi}) across co-occurring taxa using georeferenced sequence data. We compiled and harmonized genetic metadata for European vascular plants and calculated site-level{pi} for individual species. By spatially aggregating values across taxa, we derived the Genetic Diversity Index (GDI), designed to capture shared geographic structure in intraspecific variation while minimizing species-specific noise. We evaluated robustness using simulations and rarefaction procedures to assess sensitivity to sampling intensity and taxonomic composition. Using [~]630,000 sequences from 1,860 species, GDI identified consistent hotspots of genetic diversity in the Anatolian Peninsula, Southern Iberia, and the Eastern Alps. These regions coincide with areas widely recognized as long-term refugia and biogeographic transition zones. GDI showed weak relationships with species richness and phylogenetic diversity, indicating that it captures a distinct dimension of biodiversity linked to demographic persistence and evolutionary history. In addition, GDI increased significantly with late-Quaternary climate stability, supporting the expectation that regions experiencing reduced climatic fluctuation tend to retain higher levels of genetic variation. Our results demonstrate that publicly available sequence data can be synthesized into reproducible, large-scale estimates of community-level diversity. The GDI provides a practical tool for integrating genetic information into biodiversity assessments and for identifying regions where evolutionary potential may be concentrated. This framework opens new opportunities for incorporating genomic resources into conservation prioritization and macroecological research.

genetics↗

bioRxiv2025

Structural variation at lhcb6 underlies genetic variation in photosystem II maximum quantum efficiency in maize

Targeted utilization of native genetic diversity can expand the genetic basis of traits that exhibit limited genetic variation in elite breeding material and can enhance our understanding of the genotype-phenotype relationships associated with complex traits in crops. In a genome-wide association study in a European maize landrace we identified quantitative trait loci (QTL) that affected the maximum quantum efficiency of photosystem II (Fv/Fm) in field experiments. In a forward genetic approach, we focused on a QTL on chromosome 10, explaining a large proportion of the genetic variance for Fv/Fm in growth stages V4 (35%) and V6 (47%), for genetic dissection and candidate gene discovery. Integrating molecular and physiological information we show that allelic variation at the gene encoding LIGHT HARVESTING CHLOROPHYLL A/B BINDING PROTEIN6 (LHCB6), a component of the photosystem II (PSII) light-harvesting complex (LHCII) antenna, underlies the variation in Fv/Fm. We demonstrate that the allelic variation results from a hAT transposon insertion at lhcb6 and is associated with differential accumulation of the LHCII antenna components LHCB6 and LHCB3, leading to differences in non-photochemical quenching (NPQ) and plant biomass accumulation. Based on proteomic analyses we propose candidate genes that compensate the unfavorable effects caused by impaired LHCII antenna assembly. Our work provides novel insights into the function of lhcb6 in the context of LHCII antenna assembly and demonstrates the value of natural variation in landraces for the understanding and genetic improvement of complex photosynthetic processes.

genetics↗

bioRxiv2025

Fine-scale structure of a whole regional population through genetics and genealogies

The Saguenay-Lac-Saint-Jean (SLSJ) region of Quebec, Canada, a population shaped by a prominent founder effect, has long been considered genetically homogeneous. This study comprehensively investigates the fine-scale population structure within SLSJ by integrating genotype data from the CARTaGENE cohort with extensive genealogical records from the BALSAC population register. A time-efficient algorithm was developed to compute billions of kinship coefficients from genealogies in order to analyse an entire generation. We demonstrate a striking concordance between realised (genetic) and expected (genealogical) kinship (r = 0.78). From both kinship measures, we reveal fine-scale population structure at the municipal level within SLSJ, challenging the notion of regional homogeneity. Our analysis highlights an east-west genetic gradient and uncovers migratory streams and differential founders genetic contributions that shaped the genetic landscape of this population. This research provides insights into the interplay of genetics, demography, and historical events, underscoring the importance of fine-scale population structure in genetic studies and reaffirming the power of large-scale genealogical data.

genetics↗

bioRxiv2025

Beyond years of schooling: Shifting genetic influences across educational milestones in two Norwegian cohorts

Although educational attainment is heritable, its conventional measurement in genetic research as years of education (EduYears) is not designed to reveal potential stage-specific genetic influences across discrete milestones. In two Norwegian cohorts (Norwegian Mother, Father and Child Cohort Study, N = 120,527; Norwegian Twin Registry, N = 8,910), we quantified the genetic contributions to completing high school, bachelors, masters and PhD using genome-wide association studies (GWAS), polygenic indices (PGIs) and twin models. Transition-specific analyses, conditioning on prior success, revealed that observed-scale common-variant heritability (h2SNP) and PGI predictability followed an inverse-U pattern, peaking at the transition into higher education (h2SNP {approx} 0.14; R2Tjur {approx} 0.05) before declining for postgraduate degrees. Genetic correlations (rg) with large-scale GWAS of EduYears (EA4) and intelligence (IQ3) were high for early transitions but declined markedly for later ones (e.g., rg with EA4 from {approx} 0.92 to {approx} 0.38). In cumulative analyses, aggregating liability across prior milestones, the gap between twin- and SNP-based heritability narrowed at higher levels of attainment (h2twin {approx} 0.6[->]0.3; h2SNP {approx} 0.22[->]0.19), while the genetic overlap between distant milestones diminished (rg {approx} 0.92[->]0.71). These patterns, obscured by EduYears metrics, highlight a dynamic genetic architecture across educational milestones, refining polygenic prediction and addressing misconceptions about uniform genetic influences on educational progression.

genetics↗

bioRxiv2025

A unified genetic perturbation language for human cellular programming

Evolution simultaneously and combinatorially explores complex genetic changes across perturbation classes, including gene knockouts, knockdowns, overexpression, and the creation of new genes from existing domains. Separate technologies are capable of genetic perturbations at scale in human cells, but these methods are largely mutually incompatible. Here we present CRISPR-All, a unified genetic perturbation language for programming of any major type of genetic perturbation simultaneously, in any combination, at genome scale, in primary human cells. This is enabled by a standardized molecular architecture for each major perturbation class, development of a functional syntax for combining arbitrary numbers of elements across classes, and linkage to unique single cell compatible barcodes. To facilitate use, CRISPR-All converts high level descriptions of desired complex genetic changes into a single DNA sequence that can rewire genomic programs within a cell. Using the CRISPR-All language allowed for head-to-head functional comparisons across perturbation types in a comprehensive analysis of all previously identified genetic enhancements of human CAR-T cells. Combining CRISPR-All programs with single cell RNA sequencing revealed a greater diversity of phenotypic states, including improved functional performance, only accessible through distinct perturbation classes. Finally, CRISPR-All combinatorial genome scale screening of up to four distinct perturbations simultaneously revealed additive functional improvements in human T cells accessible only through iterative multiplexing of modifications across perturbation classes. CRISPR-All enables exploration of a combinatorial genetic perturbation space, which may be impactful for biological and clinical applications.

genetics↗

bioRxiv2017

Explorations of the polygenic genetic architecture of flowering time in the worldwide Arabidopsis thaliana population

As a locally adapted complex trait, flowering time in Arabidopsis thaliana has attracted much attention in genetics. Most studies have, however, focused on contributions by individual loci rather than the joint contributions by the large number of loci in the genetic architecture of flowering time to local and global adaptation. In an earlier study, we reported 46 loci associated with flowering time variation during growth at 10{degrees}C, 16{degrees}C or both in the 1,001-genomes collection of Arabidopsis thaliana accessions. Here, we explore how these loci together contribute to differences among genetically defined, and geographically divided, subpopulations across the native range of this species. Our approach was to define flowering time as a trait, and the measurements at 10 and 16 {degrees}C as two independent measurements of it. This facilitated explorations of the dynamics in the genetic architecture -which loci contribute and their effects- of flowering time across growth temperatures and their potential roles in local and global adaptation. The overall flowering time differences between populations could be explained by subtle changes in allele-frequencies and gradual changes in phenotype due to globally present alleles. More extreme local adaptations were on several occasions due to contributions by regional alleles with relatively large effects. About 2/3 of the 48 evaluated flowering time loci had similar effects on flowering time at 10{degrees}C and 16{degrees}C, while the remaining 1/3 had different effects in the two temperatures, suggesting an important contribution of gene by temperature interactions to this trait. There are also indications that co-evolution of functionally connected alleles in local populations has been important for local adaptation. Overall, this study provides deeper insights to the polygenic genetic basis of flowering time variation in Arabidopsis thaliana across a wide range of ecological habitats.\n\nAuthor SummaryMany genes can affect flowering time in Arabidopsis thaliana, but their contribution to natural flowering time variation in the worldwide population is largely unknown. We explored how 48 loci associated with flowering time, measured at 10{degrees}C and 16{degrees}C, or their difference, for the same wild collected 1,001-genomes Arabidopsis thaliana accessions together contribute to differences among the genetically defined and geographically divided subpopulations from the native species range. The overall flowering time differences among these subpopulations could be explained by the joint small effects of globally present alleles, suggesting an important contribution by polygenic adaptation for this trait. Most alleles with large effects on flowering were present only in some populations, facilitating more extreme local adaptations. Long-range LD was observed between genes in several biological pathways, indicating possible local adaptation via co-evolution of functionally connected polymorphisms. The genetic architecture of flowering time was also found to depend on the growth temperature. Most flowering time loci had similar effects on flowering time measured at 10{degrees}C and 16{degrees}C, but the effects of about 1/3 of them had effects that varied with temperature. Overall, new insights are provided to how the polygenic architecture of flowering time has facilitated its colonisation of a wide range of ecological habitats.

genetics↗