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bioRxiv2020

Estimating Genetic Similarity Matrices using Phylogenies

Genetic similarity is a measure of the genetic relatedness among individuals. The standard method for computing these matrices involves the inner product of observed genetic variants. Such an approach is inaccurate or impossible if genotypes are not available, or not densely sampled, or of poor quality (for example, genetic analysis of extinct species). We provide a new method for computing genetic similarities among individuals using phylogenetic trees. Our method can supplement (or stand in for) computations based on genotypes. We provide simulations suggesting that the genetic similarity matrices computed from trees are consistent with those computed from genotypes. With our methods, quantitative analysis on genetic traits and analysis of heritability and co-heritability can be conducted directly using genetic similarity matrices and so in the absence of genotype data, or under uncertainty in the phylogenetic tree. We use simulation studies to demonstrate the advantages of our method, and we provide applications to data.

genetics↗

bioRxiv2020

Pleiotropy and Epistasis Within and Between Signaling Pathways Defines the Genetic Architecture of Fungal Virulence

Cryptococcal disease is estimated to affect nearly a quarter of a million people annually. Environmental isolates of Cryptococcus deneoformans, which make up 15 to 30% of clinical infections in temperate climates such as Europe, vary in their pathogenicity, ranging from benign to hyper-virulent. Key traits that contribute to virulence, such as the production of the pigment melanin, an extracellular polysaccharide capsule, and the ability to grow at human body temperature have been identified, yet little is known about the genetic basis of variation in such traits. Here we investigate the genetic basis of melanization, capsule size, thermal tolerance, oxidative stress resistance, and antifungal drug sensitivity using quantitative trait locus (QTL) mapping in progeny derived from a cross between two divergent C. deneoformans strains. Using a "function-valued" QTL analysis framework that exploits both time-series information and growth differences across multiple environments, we identified QTL for each of these virulence traits and drug susceptibility. For three QTL we identified the underlying genes and nucleotide differences that govern variation in virulence traits. One of these genes, RIC8, which encodes a regulator of cAMP-PKA signaling, contributes to variation in four virulence traits: melanization, capsule size, thermal tolerance, and resistance to oxidative stress. Two major effect QTL for amphotericin B resistance map to the genes SSK1 and SSK2, which encode key components of the HOG pathway, a fungal-specific signal transduction network that orchestrates cellular responses to osmotic and other stresses. We also discovered complex epistatic interactions within and between genes in the HOG and cAMP-PKA pathways that regulate antifungal drug resistance and resistance to oxidative stress. Our findings advance the understanding of virulence traits among diverse lineages of Cryptococcus, and highlight the role of genetic variation in key stress-responsive signaling pathways as a major contributor to phenotypic variation. Author summaryDifferent environmental isolates (strains) of the same microbial species can vary greatly in their ability to cause disease, ranging from avirulent to hypervirulent. What makes some strains deadly pathogens, while others are relatively benign? This study describes the characterization of key genetic differences that underlie variation in traits thought to promote virulence in Cryptococcus deneoformans, a wide-spread opportunistic fungal pathogen. Using a combination of quantitative genetic and molecular genetic approaches we dissected the genetic architecture of virulence-related cellular traits (melanin production and the production of a polysaccharide capsule), physiological responses to stress (tolerance of thermal, oxidative, and osmotic stress), and sensitivity to multiple antifungal drugs. Strikingly we find that variation in most of these traits is governed by a small number of genetic differences that modify the function of two major cell signaling networks, cyclic AMP-Protein Kinase A (cAMP-PKA) signaling and a fungal specific MAP-kinase cascade called the high osmolarity glycerol (HOG) pathway. Similar to recent studies in a number of other fungal species, our findings point to an outsize role for a small number of highly pleiotropic signaling pathways in potentiating phenotypic variation both within and between fungal species.

genetics↗

bioRxiv2020

Shared and distinct genetic influences between cognitive domains and psychiatric disorder risk based on genome-wide data

Group-level cognitive performance differences are found in psychiatric disorders ranging from depression to autism to schizophrenia. To investigate the genetics of individual differences in fluid and crystallized cognitive abilities and their associations with psychiatric disorder risk, we conducted genome-wide association studies (GWAS) of a total of 335,227 consented 23andMe customers of European descent between the ages of 50 and 85, who completed at least one online test of crystallized cognitive ability (vocabulary knowledge, N=188,434) and/or fluid cognitive ability (visual change detection, N=158 888; digit-symbol substitution, N=132,807). All cognitive measures were significantly heritable (h2=0.10-0.16), and GWAS identified 25 novel genome-wide significant loci. Genetic correlation analyses highlight variable profiles of genetic relationships across tasks and disorders. While schizophrenia had moderate negative genetic correlations with tests of fluid cognition (visual change detection rg=-0.27, p<9.2e-24; digit-symbol substitution rg=-0.26, p<5.2e-27), it was only weakly negatively associated with crystalized cognition (vocabulary knowledge rg=-0.07, p<0.004). Autism, in contrast, showed a robust positive genetic correlation with vocabulary knowledge (rg=0.30, p<5.6e-13) and little to no genetic correlation with either fluid cognition task (rgs<0.08, ps>0.005). Crystalized and fluid cognitive abilities thus have correlated but distinct genetic architectures that relate to those of psychiatric disorders. Understanding the genetic underpinnings of specific cognitive abilities, and their relationships to psychiatric disorder risk, can inform the understanding of disease biology nosology and etiology.

genetics↗

bioRxiv2020

Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics

Genetic correlation is the correlation of additive genetic effects on two phenotypes. It is an informative metric to quantify the overall genetic similarity between complex traits, which provides insights into their polygenic genetic architecture. Several methods have been proposed to estimate genetic correlations based on data collected from genome-wide association studies (GWAS). Due to the easy access of GWAS summary statistics and computational efficiency, methods only requiring GWAS summary statistics as input have become more popular than methods utilizing individual-level genotype data. Here, we present a benchmark study for different summary-statistics-based genetic correlation estimation methods through simulation and real data applications. We focus on two major technical challenges in estimating genetic correlation: marker dependency caused by linkage disequilibrium (LD) and sample overlap between different studies. To assess the performance of different methods in the presence of these two challenges, we first conducted comprehensive simulations with diverse LD patterns and sample overlaps. Then we applied these methods to real GWAS summary statistics for a wide spectrum of complex traits. Based on these experiments, we conclude that methods relying on accurate LD estimation are less robust in real data applications compared to other methods due to the imprecision of LD obtained from reference panels. Our findings offer a guidance on how to appropriately choose the method for genetic correlation estimation in post-GWAS analysis in interpretation.

genetics↗

bioRxiv2020

Back to the future: Implications of genetic complexity for hybrid breeding strategies

Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate inbred use reduce effective population size and constrain the accessible genetic space. Under these conditions, long term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study we therefore aim to propose an alternative rational for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space and reducing effective population size, through program decentralization and disproportionate inbred use, is required to expose additive genetic variation and thus facilitate heritable genetic gains. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for "why things worked" in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.

genetics↗

bioRxiv2020

Genetic variation in aneuploidy prevalence and tolerance across the Saccharomyces cerevisiae phylogeny

Individuals carrying an aberrant number of chromosomes can vary widely in their expression of aneuploidy phenotypes. A major unanswered question is the degree to which an individuals genetic makeup influences its tolerance of karyotypic imbalance. Here we took a population genetics perspective to investigate the selective forces influencing aneuploidy prevalence in Saccharomyces cerevisiae populations as a model for eukaryotic biology. We analyzed genotypic and phenotypic variation recently published for over 1,000 S. cerevisiae strains spanning dozens of genetically defined clades and ecological associations. Our results show that the prevalence of chromosome gain and loss varies by clade and can be better explained by differences in genetic background than ecology. The phylogenetic context of lineages showing high aneuploidy rates suggests that increased aneuploidy frequency arose multiple times in S. cerevisiae evolution. Separate from aneuploidy frequency, analyzing growth phenotypes reveals that some backgrounds - such as European Wine strains - show fitness costs upon chromosome duplication, whereas other clades with high aneuploidy rates show little evidence of major deleterious effects. Our analysis confirms that chromosome amplification can produce phenotypic benefits that can influence evolutionary trajectories. These results have important implications for understanding genetic variation in aneuploidy prevalence in health, disease, and evolution. ARTICLE SUMMARYAneuploidy, an imbalance in the normal chromosome copy number, is detrimental during human development; yet individuals show substantial variability in their aneuploidy susceptibility, suggesting the influence of genetic background on aneuploidy tolerance. Scopel et al. employed a population genetic approach to address this question, analyzing over 1,000 published Saccharomyces cerevisiae genomes. The results demonstrate that genetic background has a substantial effect on aneuploidy frequency and cellular tolerance of aneuploidy stress, presenting important new information on the forces that contribute to aneuploidy prevalence.

genetics↗

bioRxiv2020

Genetic variation for plant growth traits in a common wheat population is dominated by known variants and novel QTL

Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL. We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models. In a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

genetics↗

bioRxiv2021

Genetic Basis of Variation in Cocaine and Methamphetamine Consumption in Outbred Populations of Drosophila melanogaster

We used Drosophila melanogaster to map the genetic basis of naturally occurring variation in voluntary consumption of cocaine and methamphetamine. We derived an outbred advanced intercross population (AIP) from 37 sequenced inbred wild-derived lines of the Drosophila melanogaster Genetic Reference Panel (DGRP), which are maximally genetically divergent, have minimal residual heterozygosity, are not segregating for common inversions, and are not infected with Wolbachia pipientis. We assessed consumption of sucrose, methamphetamine-supplemented sucrose and cocaine-supplemented sucrose, and found considerable phenotypic variation for consumption of both drugs, in both sexes. We performed whole genome sequencing and extreme QTL mapping on the top 10% of consumers for each replicate, sex and condition, and an equal number of randomly selected flies. We evaluated changes in allele frequencies among high consumers and control flies and identified 3,033 variants significantly (P < 1.9 x 10-8) associated with increased consumption, located in or near 1,962 genes. Many of these genes are associated with nervous system development and function, and 77 belong to a known gene-gene interaction subnetwork. We assessed the effects of RNA interference (RNAi) on drug consumption for 22 candidate genes; 17 had a significant effect in at least one sex. We constructed allele-specific AIPs which were homozygous for alternative candidate alleles for 10 SNPs and measured average consumption for each population; nine SNPs had significant effects in at least one sex. The genetic basis of voluntary drug consumption in Drosophila is polygenic and implicates genes with human orthologs and associated variants with sex- and drug-specific effects. Significance StatementThe use of cocaine and methamphetamine presents significant socioeconomic problems. However, identifying the genetic underpinnings that determine susceptibility to substance use is challenging in human populations. The fruit fly, Drosophila melanogaster, presents a powerful genetic model since we can control the genetic background and environment, 75% of disease-causing genes in humans have a fly counterpart, and flies - like humans - exhibit adverse effects upon cocaine and methamphetamine exposure. We showed that the genetic architecture underlying variation in voluntary cocaine and methamphetamine consumption differs between sexes and is dominated by variants in genes associated with connectivity and function of the nervous system. Results obtained from the Drosophila gene discovery model can guide studies on substance abuse susceptibility in human populations.

genetics↗

bioRxiv2021

Within-sibship GWAS improve estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

genetics↗

bioRxiv2021

Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome

Backgroundparent-of-origin effects (POE) play important roles in development and complex disease and thus understanding their regulation and associated molecular and phenotypic variation are warranted. Previous studies have mainly focused on the detection of genomic regions or phenotypes regulated by POE. Understanding whether POE may be modified by environmental or genetic exposures is important for understanding of the source of POE-associated variation, but only a few case studies addressing these modifiable POE exist. Methodsin order to understand this high order of POE regulation, we screened 101 genetic and environmental factors such as "predicted mRNA expression levels" of DNA methylation/imprinting machinery genes and early/late lifestyle/environmental exposures. POE-mQTL-modifier interaction models were proposed to test the potential of these factors to modify POE at DNA methylation using data from Generation Scotland: The Scottish Family Health Study(N=2315). Resultsa set of vulnerable/modifiable POE-CpGs were identified (modifiable-POE-regulated CpGs, N=3). Four factors, "lifetime smoking status" and "predicted mRNA expression levels" of TET2, SIRT1 and KDM1A, were found to significantly modify the POE on the three CpGs in both discovery and replication datasets. Importantly, the POE on one of the CpGs were modified by both genetic and environmental factors. We further identified plasma protein and health-related phenotypes associated with the methylation level of one of the identified CpGs. Conclusionsthe modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation, motivating future comprehensive evaluation of the role of these modifiers in complex diseases. Key MessagesO_LIPrevious population studies showed that parent-origin-effects(POE) on human methylome can be widespread and affect health-related traits and diseases. C_LIO_LIWhether the POE remained stable throughout the life or can be modified by genetic or environmental factors were largely unknown. C_LIO_LIBy systematically screening 101 genetic and environmental factors in a large cohort(GS:SFHS) we provided the first population-level replicated evidence that those measuring lifestyle (smoking) and predicted expression of DNA methylation- or imprinting-machinery genes are amongst the factors that can modulate the POE of mQTLs for a set of CpG sites. C_LIO_LIWe found those modifiable-POE-regulated CpGs are also phenotypically relevant - one is associated with the plasma levels of CLEC4C and health-related phenotypes such as HDL levels. C_LIO_LIThe modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation and their potential phenotypic consequences. This also provided a paradigm for further studies to explore how environmental and genetic effects can be integrated at methylation level. C_LI

genetics↗

bioRxiv2021

Using adopted individuals to partition maternal genetic effects into prenatal and postnatal effects on offspring phenotypes

Maternal genetic effects can be defined as the effect of a mothers genotype on the phenotype of her offspring, independent of the offsprings genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We assume that in biological families, offspring phenotypes are influenced prenatally by their mothers genotype and postnatally by both parents genotypes, whereas adopted individuals phenotypes are influenced prenatally by their biological mothers genotype and postnatally by their adoptive parents genotypes. Our SEM framework allows us to model the (potentially) unobserved genotypes of biological and adoptive parents as latent variables, permitting us in principle to leverage the thousands of adopted singleton individuals in the UK Biobank. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5178 adopted singletons, 983 trios, 3650 mother-offspring pairs, 1665 father-offspring pairs and 350330 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.

genetics↗

bioRxiv2021

Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

BackgroundHuman brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. MethodsThis study analyzes diffusion weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies (GWAS) of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. ResultsOur empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index (BMI) in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. DiscussionThese imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders. Impact StatementThe genetic architecture underlying brain connectivity, and whether this mechanism changes over time, remain largely unknown. To understand the inter-individual variability at different life stages, this study performed genome-wide association studies of brain network connectivity measures from two age-specific neuroimaging cohorts, and identified a common association between the minor allele (G) of rs7937515 and decreased network segregation measures of the left middle temporal gyrus. The mediation analysis further elucidated neurobiological pathway of brain connectivity mediators linking the genes FAM86C1/FOLR3 with body mass index. This study provided new insights into the genetic mechanism of inter-regional connectivity alteration in the brain.

genetics↗

bioRxiv2021

Genomic analysis reveals geography rather than culture as the predominant factor shaping genetic variation in northern Kenyan human populations

ObjectivesThe aim of this study was to characterize the genetic relationships within and among four neighboring populations in northern Kenya in light of cultural relationships to understand the extent to which geography and culture shape patterns of genetic variation. Materials and MethodsWe collected DNA and demographic information pertaining to aspects of social identity and heritage from 572 individuals across the Turkana, Samburu, Waso Borana, and Rendille of northern Kenya. We sampled individuals across a total of nine clans from these four groups and, additionally, three territorial sections within the Turkana and successfully genotyped 376 individuals. ResultsHere we report that geography predominately shapes genetic variation within and among human groups in northern Kenya. We observed a clinal pattern of genetic variation that mirrors the overall geographic distribution of the individuals we sampled. We also found relatively higher rates of intermarriage between the Rendille and Samburu and evidence of gene flow between them that reflect these higher rates of intermarriage. Among the Turkana, we observed strong recent genetic substructuring based on territorial section affiliation. Within ethnolinguistic groups, we found that Y chromosome haplotypes do not consistently cluster by natal clan affiliation. Finally, we found that sampled populations that are geographically closer have lower genetic differentiation, and that cultural similarity does not predict genetic similarity as a whole across these northern Kenyan populations. DiscussionOverall, the results from this study highlight the importance of geography, even on a local geographic scale, in shaping observed patterns of genetic variation in human populations.

genetics↗

bioRxiv2022

Imputation of 3D genome structure by genetic-epigenetic interaction modeling in mice

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions has previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of three- dimensional chromatin structure. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/479436v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@14d4221org.highwire.dtl.DTLVardef@130a812org.highwire.dtl.DTLVardef@16a6069org.highwire.dtl.DTLVardef@df9d9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2022

Practical application of indicators for genetic diversity in CBD post-2020 Global Biodiversity Framework implementation

Genetic diversity is a key aspect of biological variation for the adaptability and survival of populations of species, which must be monitored to assure maintenance. We used data from the Swedish Red-List 2020 and a recent government report to apply three indicators for genetic diversity proposed for the post-2020 Global Biodiversity Framework of the Convention on Biological Diversity (CBD). We made more detailed indicator assessments for mammals and herptiles. For indicator 1, the proportion of populations with effective population size Ne>500, 33% of 22557 investigated species had a population estimate. For herptiles and mammals, 70% and 49%, respectively likely had Ne>500. For indicator 2, the proportion of populations or range remaining within species, 20% of all species evaluated for the Red-List have data. Meanwhile, 32% of the herptile and 84% of the mammal populations are maintaining their populations/range. For indicator 3, the number of species/populations in which genetic diversity is monitored using DNA-based methods, there are studies on 3% of species, and 0.3% are monitored. In contrast 68% of mammals and 29% of herptiles are studied using DNA, and 8% of mammals and 24% of herptiles are genetically monitored. We conclude that the Red List provide data suitable for evaluating the genetic indicators, but the data quality can be improved. There is a synergy in estimating the genetic indicators in parallel with the Red-Listing process. We propose that indicator values could be included in national Red-Listing as a new category - "genetically threatened", based on the genetic indicators.

genetics↗

bioRxiv2022

Assessment of genetic diversity and SNP marker development within peanut germplasm in Taiwan by RAD-seq

The cultivated peanut (Arachis hypogaea L.) is an important oil crop but has a narrow genetic diversity. Molecular markers can be used to probe the genetic diversity of various germplasm. In this study, the restriction site associated DNA (RAD) approach was utilized to sequence 31 accessions of Taiwanese peanut germplasm, leading to the identification of a total of 17,610 single nucleotide polymorphisms (SNPs). The origin of these 31 accessions is contrasted so the global subset (n = 17) has greater genetic diversity than the local subset (n = 14). Concerning botanical varieties, the var. fastigiata subset has greater genetic diversity than the other two subsets of var. vulgaris and var. hypogaea, suggesting that novel genetic resources should be introduced into breeding programs to enhance genetic diversity. Principal component analysis (PCA) using genotyping data separated the 31 accessions into three clusters largely according to the botanical varieties, consistent with the PCA result for 282 accessions genotyped by 14 kompetitive allele-specific PCR (KASP) markers developed in this study. The SNP markers identified in this work not only revealed the genetic relationship and population structure of current germplasm in Taiwan, but also offer an efficient tool for breeding and further genetic applications.

genetics↗

bioRxiv2022

Genetic dissection of the pluripotent proteome through multi-omics data integration

Genetic background is a major driver of phenotypic variability in pluripotent stem cells (PSCs). Most studies of variation in PSCs have relied on transcript abundance as the primary molecular readout of cell state. However, little is known about how proteins, the primary functional units in the cell, vary across genetically diverse PSCs, how protein abundance relates to variation in other cell characteristics, and how genetic background confers these effects. Here we present a comprehensive genetic study characterizing the pluripotent proteome of 190 unique mouse embryonic stem cell lines (mESCs) derived from genetically heterogeneous Diversity Outbred (DO) mice. The quantitative proteome is highly variable across DO mESCs, and we identified differentially activated pluripotency-associated pathways in the proteomics data that were not evident in transcriptome data from the same cell lines. Comparisons of protein abundance to transcript levels and chromatin accessibility show broad co-variation across molecular layers and variable correlation across samples, with some lines showing high and others low correlation between these multi-omics datasets. Integration of these three molecular data types using multi-omics factor analysis revealed shared and unique drivers of quantitative variation in pluripotency-associated pathways. QTL mapping localized the genetic drivers of this quantitative variation to a number of genomic hotspots, and demonstrated that multi-omics data integration consolidates the influence of genetic signals shared across molecular traits to increase QTL detection power and overcome the limitations inherent in mapping individual molecular features. This study reveals transcriptional and post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome, and in so doing provides a regulatory map for mouse ESCs that can provide a rational basis for future mechanistic studies, including studies of human PSCs.

genetics↗

bioRxiv2022

Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli

Collisions between DNA replication complexes (replisomes) and impediments such as damaged DNA or proteins tightly bound to the chromosome lead to premature dissociation of replisomes at least once per cell cycle in Escherichia coli. Left unrepaired, these events produce incompletely replicated chromosomes that cannot be properly partitioned into daughter cells. DNA replication restart, the process that reloads replisomes at prematurely terminated sites, is therefore essential in E. coli and other bacteria. Three replication restart pathways have been identified in E. coli: PriA/PriB, PriA/PriC, and PriC/Rep. A limited number of genetic interactions between replication restart and other genome maintenance pathways have been defined, but a systematic study placing replication restart reactions in a broader cellular context has not been performed. We have utilized transposon insertion sequencing to identify new genetic interactions between DNA replication restart pathways and other cellular systems. Known genetic interactors with the priB replication restart gene (uniquely involved in the PriA/PriB pathway) were confirmed and several novel priB interactions were discovered. Far fewer connections were found with the PriA/PriC or PriC/Rep pathways, suggesting a primacy role for the PriA/PriB pathway in E. coli. Targeted genetic and imaging-based experiments with priB and its genetic partners revealed significant double-strand DNA break (DSB) accumulation in strains with mutations in dam, rep, rdgC, lexA, or polA. Modulating the activity of the RecA recombinase partially suppressed the detrimental effects of rdgC or lexA mutations in {Delta}priB cells. Taken together, our results highlight roles for several genes in DSB homeostasis and define a genetic network that facilitates DNA repair/processing upstream of PriA/PriB-mediated DNA replication restart in E. coli. Author SummaryAll organisms rely on DNA replication to grow, develop, and reproduce. In bacteria, the cellular machinery that carries out DNA replication is estimated to fail and prematurely dissociate from the genome at least once per cell cycle. As a result, bacteria have evolved "DNA replication restart" mechanisms that resuscitate failed replication reactions. To probe the function and context of DNA replication restart in the bacterium Escherichia coli, we employed a genetic screen to identify genes that were conditionally important in mutant E. coli strains compromised in their ability to perform DNA replication restart. Identification of genes with previously known relationships with DNA replication restart confirmed the robustness of our screen, while additional findings implicated novel genetic relationships. Targeted experiments validated the importance of these genes and provided an explanation for their significance in preventing double-strand DNA breaks in cells, a severe form of DNA damage. Our results help to define specific roles for the genes identified by our screen and elucidate the contextual environment of DNA repair upstream of DNA replication restart in E. coli.

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