SEARCH · bioRxiv Science

Results for “genetics”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43Linked to original sources

bioRxiv2018

Estimating Heritability and Genetic Correlation in Case Control Studies Directly and with Summary Statistics

Methods that estimate heritability and genetic correlations from genome-wide association studies have proven to be powerful tools for investigating the genetic architecture of common diseases and exposing unexpected relationships between disorders. Many relevant studies employ a case-control design, yet most methods are primarily geared towards analyzing quantitative traits. Here we investigate the validity of three common methods for estimating genetic heritability and genetic correlation. We find that the Phenotype-Correlation-Genotype-Correlation (PCGC) approach is the only method that can estimate both quantities accurately in the presence of important non-genetic risk factors, such as age and sex. We extend PCGC to work with summary statistics that take the case-control sampling into account, and demonstrate that our new method, PCGC-s, accurately estimates both heritability and genetic correlations and can be applied to large data sets without requiring individual-level genotypic or phenotypic information. Finally, we use PCGC-S to estimate the genetic correlation between schizophrenia and bipolar disorder, and demonstrate that previous estimates are biased due to incorrect handling of sex as a strong risk factor. PCGC-s is available at https://github.com/omerwe/PCGCs.

genetics↗

bioRxiv2018

Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications.

Introductory paragraphThe inclusion of genetic data in large studies has enabled the discovery of genetic contributions to complex traits and their application in applied analyses including those using genetic risk scores (GRS) for the prediction of phenotypic variance. If genotypes show structure by location and coincident structure exists for the trait of interest, analyses can be biased. Having illustrated structure in an apparently homogeneous collection, we aimed to a) test for geographical stratification of genotypes in UK Biobank and b) assess whether stratification might induce bias in genetic association analysis.\n\nWe found that single genetic variants are associated with birth location within UK Biobank and that geographic structure in genetic data could not be accounted for using routine adjustment for study centre and principal components (PCs) derived from genotype data. We found that GRS for complex traits do appear geographically structured and analysis using GRS can yield biased associations. We discuss the likely origins of these observations and potential implications for analysis within large-scale population based genetic studies.

genetics↗

bioRxiv2018

Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced \"atlases\" of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

genetics↗

bioRxiv2018

Effective population sizes and adaptive genetic variation in a captive bird population

Captive populations are considered a key component of ex situ conservation programs. Research on multiple taxa have shown the differential success of maintaining demographic versus genetic stability and viability in captive populations. In typical captive populations, usually founded by few or related individuals, genetic diversity can be lost and inbreeding can accumulate rapidly, calling into question their ultimate utility for release into the wild. Furthermore, domestication selection for survival in captive conditions is another concern. Therefore, it is crucial to understand the dynamics of population sizes, particularly the effective population size, and genetic diversity at non-neutral, at adaptive loci in captive populations.\n\nIn this study, we assessed effective population sizes and genetic variation at both neutral microsatellite markers, as well as SNP variants from the MHC-B locus of a captive Red Junglefowl population. This population is a rare instance of a well-documented history in captivity, and also following a realistic chain-of-custody, unlike captive lab populations. Our analysis of 27 individuals that comprise the entirety of one captive population. Our results show very low neutral and adaptive genetic variation, as well as low effective sizes, which are surprising in spite of the known demographic history. Finally, our study also shows the divergent impacts of small effective size and inbreeding in captive populations on microsatellite versus adaptive genetic variation in the MHC-B locus. Our study provides insights into the difficulties of maintaining adaptive genetic variation in small captive populations.

genetics↗

bioRxiv2018

Quantification of genetic components of population differentiation in UK Biobank traits reveals signals of polygenic selection

The genetic architecture of most human complex traits is highly polygenic, motivating efforts to detect polygenic selection involving a large number of loci. In contrast to previous work relying on top GWAS loci, we developed a method that uses genome-wide association statistics and linkage disequilibrium patterns to estimate the genome-wide genetic component of population differentiation of a complex trait along a continuous gradient, enabling powerful inference of polygenic selection. We analyzed 43 UK Biobank traits and focused on PC1 and North-South and East-West birth coordinates across 337K unrelated British-ancestry samples, for which our method produced close to unbiased estimates of genetic components of population differentiation and high power to detect polygenic selection in simulations across different trait architectures. For PC1, we identified signals of polygenic selection for height (74.5{+/-}16.7% of 9.3% total correlation with PC1 attributable to genome-wide genetic effects; P = 8.4x10-6) and red hair pigmentation (95.9{+/-}24.7% of total correlation with PC1 attributable to genome-wide genetic effects; P = 1.1x10-4); the bulk of the signal remained when removing genome-wide significant loci, even though red hair pigmentation includes loci of large effect. We also detected polygenic selection for height, systolic blood pressure, BMI and basal metabolic rate along North-South birth coordinate, and height and systolic blood pressure along East-West birth coordinate. Our method detects polygenic selection in modern human populations with very subtle population structure and elucidates the relative contributions of genetic and non-genetic components of trait population differences.

genetics↗

bioRxiv2018

Genetic and phenotypic differentiation of lumpfish (Cyclopterus lumpus) across the North Atlantic: implications for conservation and aquaculture

Demand for lumpfish (Cyclopterus lumpus) has increased exponentially over the last decade, both for their roe, which is used as a caviar substitute, and increasingly also as cleaner fish to control sea lice in salmon farming. The species is classified as Near Threatened by the UICN and there are growing concerns that over-exploitation of wild stocks and translocation of hatchery-reared lumpfish may compromise the genetic diversity of native populations. We carried a comparative analysis of genetic and phenotypic variation across the species range to estimate the level of genetic and phenotypic differentiation, and determined patterns of gene flow at spatial scales relevant to management. We found five genetically distinct groups located in the West Atlantic (USA, and Canada), Mid Atlantic (Iceland), East Atlantic (Faroe Islands, Ireland, Scotland, Norway, and Denmark), English Channel (England) and Baltic Sea (Sweden). Significant phenotypic differences were also found, with Baltic lumpfish growing more slowly, attaining a higher condition factor and maturing at a smaller size than North Atlantic lumpfish. Estimates of effective population size were consistently low across the NE Atlantic (Iceland, Faroe Islands, Norway), the area where most wild lumpfish are fished for their roe, and also for the aquaculture industry. Our study suggests that some lumpfish populations are very small and have low genetic diversity, which makes them particularly vulnerable to over-exploitation and genetic introgression. To protect them we advocate curtailing fishing effort, closing the breeding cycle of the species in captivity to reduce dependence on wild stocks, restricting the translocation of genetically distinct populations, and limiting the risk of farm escapes.

genetics↗

bioRxiv2018

Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits

Understanding the genetic basis of dynamic plant phenotypes has largely been limited due to lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to non-destructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g. genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits, and provide a robust framework for modeling traits trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice using 33,674 SNPs. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over a conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent, as well as time-specific, transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements. O_LSTCore Ideas:C_LSTO_LIRandom regression models are an appealing framework for GWAS of longitudinal traits C_LIO_LIThis approach provides improvements over a conventional single time point analyses for GWAS C_LIO_LIWe identify QTL with transient and persistent effects on shoot growth in rice C_LI

genetics↗

bioRxiv2018

Reverse GWAS: Using Genetics to Identify and Model Phenotypic Subtypes

Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automatic statistical approaches to subtype definition particularly valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a bespoke decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show these features can be crucial for power and calibration. We validate RGWAS in practice by recovering known stress subtypes in major depressive disorder. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests genetic heterogeneity may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting potential have potential translational value.\n\nAuthor summaryComplex diseases depend on interactions between many known and unknown genetic and environmental factors. However, most studies aggregate these strata and test for associations on average across samples, though biological factors and medical interventions can have dramatically different effects on different people. Further, more-sophisticated models are often infeasible because relevant sources of heterogeneity are not generally known a priori. We introduce Reverse GWAS to simultaneously split samples into homogeneoues subtypes and to learn differences in genetic or treatment effects between subtypes. Unlike existing approaches to computational subtype identification using high-dimensional trait data, RGWAS accounts for covariates, binary disease traits and, especially, population structure; these features are each invaluable in extensive simulations. We validate RGWAS by recovering known genetic subtypes of major depression. We demonstrate RGWAS is practically useful in a metabolic study, finding three novel subtypes with both SNP- and polygenic-level heterogeneity. Importantly, RGWAS can uncover differential treatment response: for example, we show that statin, a common drug and potential type 2 diabetes risk factor, may have opposing subtype-specific effects on blood glucose.

genetics↗

bioRxiv2018

Genetic differences in host infectivity affect disease spread and survival in epidemics

Survival during an epidemic is partly determined by host genetics. While quantitative genetic studies typically consider survival as an indicator for disease resistance, mortality rates of populations undergoing an epidemic are also affected by tolerance and infectivity (i.e. the propensity of an infected individual to transmit disease). Few studies have demonstrated genetic variation in disease tolerance, and no study has demonstrated genetic variation in host infectivity, despite strong evidence for considerable phenotypic variation in this trait. Here we propose an experimental design and statistical models for estimating genetic diversity in all three host traits. Using an infection model in fish we provide, for the first time, direct evidence for genetic variation in host infectivity, in addition to variation in resistance and tolerance. We also demonstrate how genetic differences in these three traits contribute to survival. Our results imply that animals can evolve different disease response types affecting epidemic survival rates, with important implications for understanding and controlling epidemics.

genetics↗

bioRxiv2018

A global view of pleiotropy and genetic architecture in complex traits

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http://atlas.ctglab.nl).

genetics↗

bioRxiv2019

Analysis of genetic networks regulating refractive eye development in collaborative cross progenitor strain mice reveals new genes and pathways underlying human myopia

Population studies suggest that genetic factors play an important role in refractive error development; however, the precise role of genetic background and the composition of the signaling pathways underlying refractive eye development remain poorly understood. Here, we analyzed normal refractive development and susceptibility to form-deprivation myopia in the eight progenitor mouse strains of the Collaborative Cross (CC). Genetic background strongly influenced both baseline refractive development and susceptibility to environmentally-induced myopia. Baseline refractive errors ranged from -21.2 diopters (D) in 129S1/svlmj mice to +22.0 D in CAST/EiJ mice and represented a continuous distribution typical of a quantitative genetic trait. The extent of induced form-deprivation myopia ranged from -5.6 D in NZO/HILtJ mice to -20.0 D in CAST/EiJ mice and also followed a continuous distribution. Whole-genome (RNA-seq) gene expression profiling in retinae from CC progenitor strains identified genes whose expression level correlated with either baseline refractive error or susceptibility to myopia. Expression levels of 2,302 genes correlated with the baseline refractive state of the eye, whereas 1,917 genes correlated with susceptibility to induced myopia. Genome-wide gene-based association analysis in the CREAM and UK Biobank human cohorts revealed that 985 of the above genes were associated with refractive error development in humans, including 847 genes which were implicated in the development of human myopia for the first time. Although the gene sets controlling baseline refractive development and those regulating susceptibility to myopia overlapped, these two processes appeared to be controlled by largely distinct sets of genes. Comparison with data for other animal models of myopia revealed that the genes identified in this study comprise a well-defined set of retinal signaling pathways, which are highly conserved across different species. These results provide attractive targets for the development of anti-myopia drugs. Author SummarySeveral lines of evidence suggest that variations in genetic background have a strong impact on a default (baseline) trajectory of eye growth and refractive development. Many studies also highlighted differences in susceptibility of different individuals to environmentally induced changes in refractive eye development, suggesting that genetic background plays an important role in visual regulation of eye growth. However, genes and signaling pathways that control the baseline trajectory of refractive eye development and those that regulate the impact of visual environment on refractive eye development are still poorly understood. Our data suggest that both processes are regulated by elaborate retinal genetic networks. Surprisingly, we found that although genes that control baseline refractive eye development and genes regulating the impact of visual environment on refractive development overlap, there is a large number of genes and pathways which exclusively control either the baseline trajectory of refractive eye development or the impact of visual environment on refractive development. Moreover, we found that many of the genes and pathways, which we found to be associated with either baseline refractive development or susceptibility to environmentally induced myopia in mice, are also associated with refractive error development in the human population and are highly conserved across different species. Identification of genes and pathways that underlie visual regulation of eye growth versus genes and pathways that control default trajectory of refractive eye development sheds light on the basic mechanisms of eye emmetropization and provides previously unexplored possibilities for the development of new treatment options for myopia.

genetics↗

bioRxiv2019

Asthma and affective traits in adults: a genetically informative study

Depression, anxiety and high neuroticism (affective traits) are often comorbid with asthma. A causal direction between the affective traits and asthma is difficult to determine, however, it may be that there is a common underlying pathway attributable to shared genetic factors. Our aim was to determine whether a common genetic susceptibility exists for asthma and each of the affective traits. An adult twin cohort from the Swedish Twin Register underwent questionnaire-based health assessments (n=23 693) and genotyping (n=15 908). Firstly, questionnaire-based associations between asthma and affective traits were explored. This was followed by genetic analyses: a) polygenic risk scores (PRS) for affective traits were used as predictors of asthma, and b) linkage-disequilibrium score regression based on genome-wide association results from UK Biobank was used to quantify genetic correlations. Analyses found that the questionnaire-based associations between asthma and each affective trait were associated (OR 1.7, 95%CI 1.5-1.9 major depression, OR 1.5, 95%CI 1.3-1.6 anxiety, and OR 1.6, 95% 1.4-1.8 high neuroticism). Genetic susceptibility for neuroticism explained the variance in asthma with a dose response effect; that is, those in the highest neuroticism PRS quartile were more likely to have asthma than those in the lowest quartile (OR 1.4, 95%CI 1.2-1.6). Genetic correlations were found between depression and asthma (rg= 0.17), but not for anxiety or neuroticism score. We conclude that the observed comorbidity between asthma and the affective traits may in part be due to shared genetic influences between asthma and depression and neuroticism, but not anxiety.

genetics↗

bioRxiv2019

Information and genetic counselling for psychiatric risks in children with rare disorders

BackgroundThe diagnosis of developmental disorders is being transformed by advances in whole genome technologies. However, continuing uncertainties about the individual risks and potential severity of psychiatric impacts attributed to causal genomic variants limits the availability of comprehensive family-oriented information. In addition, there is insufficient evidence about how the parents of children with developmental disorders comprehend the facts and implications of their diagnosis through genetic counselling, nor how they gather developmental and mental health information to guide their understanding. MethodsParents of children (aged 0-17 years) referred to paediatric genetics services completed an anonymous online 46-item survey about: (i) the experience of attending services to receive their childs genetic diagnosis, and (ii) the availability, quality and helpfulness of information about psychiatric and neurodevelopmental conditions associated with genomic disorders. FindingsTwo-hundred and eighty-six families (199 UK and 87 USA) completed the survey. One-in-three UK and one-in-five US respondents were dissatisfied with how their childs genetic diagnosis was communicated. Satisfaction was predicted by face-to-face communication (odds ratio 2{middle dot}91 [95% CI 1{middle dot}43-5{middle dot}94]; p=0{middle dot}003); results being presented by genetics specialists (2{middle dot}97 [1{middle dot}41-6{middle dot}26]; p=0{middle dot}004); receiving clear explanations (5{middle dot}14 [2{middle dot}58- 10{middle dot}26]; p<0{middle dot}001); receiving support (2{middle dot}99 [1{middle dot}21-7{middle dot}36], p=0{middle dot}017); and male gender of the tested child (2{middle dot}56 [1{middle dot}28-5{middle dot}14]; p=0{middle dot}008). Compared to health-related information on developmental delay or intellectual disability, parents were more likely to obtain information about psychiatric manifestations from non-professional lay sources than from clinical specialists (p<0{middle dot}001). This was particularly evident for families in the UK compared to the USA (p<0{middle dot}001). Parents considered information from rare disorder support groups to be more helpful than from genetics specialists (odds ratio 11{middle dot}0 [95% CI 5{middle dot}08-86{middle dot}75]; p<0{middle dot}001), or paediatricians (11{middle dot}0 [1{middle dot}42-85{middle dot}20]; p=0{middle dot}006), or internet sites (15{middle dot}5 [3{middle dot}71-64{middle dot}77]; p<0{middle dot}001), which in turn proved more helpful than information provided by geneticists (2{middle dot}5 [1{middle dot}44-4{middle dot}31]; p=0{middle dot}001). InterpretationPsychiatric comorbidity is a common feature of rare genomic disorders, but the paucity of suitable information available from clinical specialists suggests families are not optimally informed about these challenges. Wider implementation of genomic testing in general medicine should include adequate training in genetic counselling to ensure best practice in communicating and explaining complex test results supported by comprehensive, family-oriented information. FundingThe Waterloo Foundation: Changing Minds Programme (506296); The Medical Research Council (MRC) Research Grant: Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment (MR/N022572/1).

genetics↗

bioRxiv2019

Population genetic data of the 21 autosomal STRs included in GlobalFilerTM kit of a population sample from the Kingdom of Bahrain

IntroductionBahrains population consists mainly of Arabs, Baharna and Persians leading Bahrain to become ethnically diverse. The exploration of the ethnic origin and genetic structure within the Bahraini population is fundamental mainly in the field of population genetics and forensic science. AimThe purpose of the study was to investigate and conduct genetic studies in the population of Bahrain to assist in the interpretation of DNA-based forensic evidence and in the construction of appropriate databases. Materials and Methods24 short-tandem repeats in the GlobalFiler PCR Amplification kit including 21 autosomal STR loci and three gender determination loci were amplified to characterize different genetic and forensic population parameters in a cohort of 543 Bahraini unrelated healthy men. Samples were collected during the year 2017. ResultsThe genotyping of the 21 autosomal STRs showed that most loci were in Hardy-Weinberg Equilibrium (HWE) except for three markers; D3S1358, D19S433 and D5S818 which showed deviation from HWE. We also found out no significant deviations from LD between pairwise STR loci in Bahraini population except when plotting for D3S1358-CSF1PO, CSF1PO-SE33, D19S433-D12S391, FGA-D2S1338, FGA-SE33, FGA-D7S820 and D7S820-SE33. The SE33 locus was the most polymorphic for the studied population and THO1 locus was the less polymorphic. The Allele 8 in TPOX scored the highest allele frequency of 0.496. The SE33 locus showed the highest power of discrimination (PD) in Bahraini population, whereas TPOX showed the lowest PD value. The 21 autosomal STRs showed a value of combined match probability (CMP) equal to 4.5633E-27, and a combined power of discrimination (CPD) of 99.99999999%. Off-ladders and tri-allelic variants were observed in various samples at D12S391, SE33 and D22S1045 loci. ConclusionOur study indicated that the twenty-one autosomal STRs are highly polymorphic in the Bahraini population and can be used as a powerful tool in forensics and population genetic analyses including paternity testing and familial DNA searching. Author SummaryKingdom of Bahrain is a country of 33 islands located in the Arabian Peninsula. The location of Bahrain had affected the diversity of its population, which is mainly divided into four main ethnic groups: Arabs, Baharna and Persians. Genetic studies on Bahraini population are very limited and little has been done to characterize population structure within Kingdom of Bahrain. Here, we used 21 autosomal STRs included in the GlobalFiler Amplification Kit to amplify DNA from 543 non-related males from Bahraini population. We conducted statistical analysis using two main different software such as STRAF and GenAlEx. Different forensic and population parameters were obtained to characterize Bahraini population. Some of the significant results obtained were the following: most of the loci were in Hardy-Weinberg Equilibrium, the most polymorphic and informative marker was SE33. Allele 8 in TPOX presented the highest allele frequency for the studied population. We also found out some of the rare variants which were recorded in STRbase website. Bahraini population was correspondingly compared to the genetic structure of the region. Our study indicated the usefulness of the 21 autosomal STRs in the GlobalFiler in establishing databases, analyzing paternity and reviewing DNA-based evidences.

genetics↗

bioRxiv2019

Multi-Trait Improvement by Predicting Genetic Correlations in Breeding Crosses

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e. pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.

genetics↗

bioRxiv2019

Genetic overlap between obsessive-compulsive disorder, related symptoms in the population and insulin signaling: etiological implications

ObjectiveObsessive-compulsive symptoms (OCS) in the population have been linked to obsessive-compulsive disorder (OCD) in genetic and epidemiological studies. Insulin signaling has been implicated in OCD. We extend previous work by assessing genetic overlap between OCD, population-based OCS, and central nervous system (CNS) and peripheral insulin signaling.\n\nMethodsWe conducted genome-wide association studies (GWASs) in the population-based Philadelphia Neurodevelopmental Cohort (PNC, 650 children and adolescents) of the total OCS score and six OCS factors from an exploratory factor analysis of 22 questions. Subsequently, we performed polygenic risk score (PRS) analysis to assess shared genetic etiologies between clinical OCD (using GWAS data from the Psychiatric Genomics Consortium), the total OCS score and OCS factors. We then performed gene-set analyses with a set of OCD-linked genes centered around CNS insulin-regulated synaptic function and PRS analyses for five peripheral insulin signaling-related traits. For validation purposes, we explored data from the independent Spit for Science population cohort (5047 children and adolescents).\n\nResultsIn the PNC, we found a shared genetic etiology between OCD and impairment, contamination/cleaning and guilty taboo thoughts. In the Spit for Science cohort, we were able to validate the finding for contamination/cleaning, and additionally observed genetic sharing between OCD and symmetry/counting/ordering. The CNS insulin-linked gene-set associated with symmetry/counting/ordering. We also identified genetic sharing between peripheral insulin signaling-related traits (type 2 diabetes and the blood levels of HbA1C, fasting insulin and 2 hour glucose) and OCD as well as certain OCS.\n\nConclusionsOCD, OCS in the population and insulin-related traits share genetic risk factors, indicating a common etiological mechanism underlying somatic and psychiatric disorders.

genetics↗

bioRxiv2019

GENETIC RESILIENCE OF A ONCE ENDANGERED SPECIES, TIBETAN ANTELOPE (Pantholops hodgsonii)

Population reduction is generally assumed to reduce the populations genetic diversity and hence its ability to adapt to environmental change. However, if life history traits that promote gene flow buffer populations from such impacts, conservation efforts should aim to maintain those traits in vulnerable species. Tibetan antelope (Pantholops hodgsonii) has experienced population reduction by 95% due to poaching during the 20th century. We hypothesize that opportunities for gene flow provided by their sex-specific migration buffered their genetic diversity from the poaching impacts. We measured the mtDNA (control region, CR) and nuDNA (microsatellites or STRs) diversity, population differentiation, along with the change in effective population size (pre-poaching era vs. post-poaching era) and tested for a genetic bottleneck. Our results showed that Tibetan antelope maintained considerable genetic diversity in both mtDNA CR and STR markers (Hd = 0.9970 and Hobs = 0.8446, respectively), despite a marked reduction in post-poaching effective population size 368.9 (95% CI of 249.3 - 660.6) compared to the pre-poaching average (4.93x103 - 4.17x104). Post-poached populations also had low population structure and showed no evidence of a genetic bottleneck. Pairwise Fst values using CR haplotype frequencies were higher than those using STR allele frequencies, suggesting different degrees of gene flow mediated by females and males. This study suggests that the Tibetan antelopes sex-specific migration buffered their loss of genetic diversity in the face of severe demographic decline. These findings highlight the importance of recognizing the traits likely to maintain genetic diversity and promoting conservation efforts that allow them to be exercised. For Tibetan antelope, this requires assuring that their migration routes remain unobstructed by growing human disturbances while continuing to enforce anti-poaching law enforcement efforts.

genetics↗

bioRxiv2019

Low additive genetic variation in a trait under selection in domesticated rice

Quantitative traits are important targets of both natural and artificial selection. The genetic architecture of these traits and its change during the adaptive process is thus of fundamental interest. The fate of the additive effects of variants underlying a trait receives particular attention because they constitute the genetic variation component that is transferred from parents to offspring and thus governs the response to selection. While estimation of this component of phenotypic variation is challenging, the increasing availability of dense molecular markers puts it within reach. Inbred plant species offer an additional advantage because phenotypes of genetically identical individuals can be measured in replicate. This makes it possible to estimate marker effects separately from the contribution of the genetic background. We focused on root growth in domesticated rice, Oryza sativa, under normal and aluminum (Al) stress conditions, a trait under recent selection because it correlates with survival under drought. A dense single nucleotide polymorphism (SNP) map is available for all accessions studied. Taking advantage of this map and a set of Bayesian models, we assessed additive marker effects. While total genetic variation accounted for a large proportion of phenotypic variance, marker effects contributed little information, particularly in the Al-tolerant tropical japonica population of rice. We were unable to identify any loci associated with root growth in this population. Models estimating the aggregate effects of all measured genotypes like-wise produced low estimates of marker heritability and were unable to predict total genetic values accurately. Our results support the long-standing conjecture that additive genetic variation is depleted in traits under selection. We further provide evidence that this depletion is due to the prevalence of low-frequency alleles that underlie the trait.

genetics↗