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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

Optimized breeding strategies to harness Genetic Resources with different performance levels

The narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy. We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses.

genetics↗

bioRxiv2020

A whole-genome sequenced control population in northern Sweden reveals subregional genetic differences.

The number of national reference populations that are whole-genome sequenced are rapidly increasing. Partly driving this development is the fact that genetic disease studies benefit from knowing the genetic variation typical for the geographical area of interest. A whole-genome sequenced Swedish national reference population (n=1000) has been recently published but with few samples from northern Sweden. In the present study we have whole-genome sequenced a control population (n=300) (ACpop) from Vasterbotten County, a sparsely populated region in northern Sweden previously shown to be genetically different from southern Sweden. The aggregated variant frequencies within ACpop are publicly available (DOI 10.17044/NBIS/G000005) to function as a basic resource in clinical genetics and for genetic studies. Our analysis of ACpop, representing approximately 0.11% of the population in Vasterbotten, indicates the presence of a genetic substructure within the county. Furthermore, a demographic analysis showed that the population from which samples were drawn was to a large extent geographically stationary, a finding that was corroborated in the genetic analysis down to the level of municipalities. Including ACpop in the reference population when imputing unknown variants in a Vasterbotten cohort resulted in a strong increase in the number of high-confidence imputed variants (up to 81% for variants with minor allele frequency < 5%). ACpop was initially designed for cancer disease studies, but the genetic structure within the cohort will be of general interest for all genetic disease studies in northern Sweden.

genetics↗

bioRxiv2020

Genetic Diversity and Population Structure in the Yaeyama flying fox

The Ryukyu flying fox (Pteropus dasymallus) is distributed throughout the island chain spanning across southern Japan, Taiwan, and possibly the Philippines. Although P. dasymallus is listed as VU (vulnerable) in the IUCN Red List, only few genetic works have been conducted to support its conservation. In this study we analyzed 19 markers (mtDNA haplotypes and 18 microsatellite markers) to evaluate genetic diversity and investigate the genetic structure of this species. mtDNA analysis was conducted with 142 DNA remote samples, mostly from feces, and wing tissues collected on eight islands (Miyako, Ishigaki, Kohama, Kuroshima, Hateruma, Taketomi, Iriomote, Yonaguni). 39 haplotypes were identified in 526bp of the control region, and haplotype network showed no clear genetic structure. Microsatellite analysis was also conducted with 155 samples collected on six islands (Miyako, Ishigaki, Kohama, Taketomi, Iriomote, Yonaguni). It showed that the Yonaguni population exhibits low genetic diversity, high inbreeding, and clear genetic differentiation from other populations. Gene flow between Ishigaki and Miyako through small stepstone islands might be preventing inbreeding of the Miyako population. We provide for the first time indirect proof of long-distance inter-island dispersal in the Ryukyu flying fox and revealed genetic diversity, gene flow and genetic differentiation among populations of the archipelago. These results will be useful for delineating conservation units and designing specific conservation policies for each island based on metapopulation genetic structure.

genetics↗

bioRxiv2020

Divergent genetic effects for type 1 and type 2 diabetes at overlapping association signals

Aims/hypothesisGiven the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. MethodsUsing publicly available type 2 diabetes summary statistics from a genomewide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate<0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined colocalisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a colocalisation posterior probability of [≥]0.9 was considered a genuine shared association with both diseases. ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability [≥]0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (PGM1). In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect. Conclusions/interpretationThat four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases. Research in ContextWhat is already known about this subject? O_LIOther than insulin, there are currently no treatments for both type 1 and type 2 diabetes. C_LIO_LIFindings that genetic variants near the GLIS3 gene increase risk of both type 1 and type 2 diabetes have indicated shared genetic mechanisms at the level of the pancreatic {beta} cell. C_LI What is the key question? O_LIBy examining chromosome regions associated with both diseases, are there any more variants that affect risk of both diseases and could support common mechanisms and repositioning of therapeutics between the diseases? C_LI What are the new findings? O_LIAt current sample sizes, there is evidence that five genetic variants in different chromosome regions impact risk of developing both diseases. C_LIO_LIHowever, four of these variants have the opposite direction of effect in type 1 diabetes compared to type 2 diabetes, with only one, near GLIS3, having a concordant direction of effect. C_LI How might this impact on clinical practise in the foreseeable future? O_LIGenetic findings have furthered research in type 1 and type 2 diabetes independently, and suggest therapeutic strategies. However, our current investigation into their shared genetics suggests that repositioning of current type 2 diabetes treatments into type 1 diabetes may not be straightforward. C_LI

genetics↗

bioRxiv2020

Genetic factors contributing to extensive variability of sex-specific hepatic gene expression in Diversity Outbred mice

Sex-specific transcription characterizes hundreds of genes in mouse liver, many implicated in sex-differential drug and lipid metabolism and disease susceptibility. While the regulation of liver sex differences by growth hormone-activated STAT5 is well established, little is known about autosomal genetic factors regulating the sex-specific liver transcriptome. Here we show, using genotyping and expression data from a large population of Diversity Outbred mice, that genetic factors work in tandem with growth hormone to control the individual variability of hundreds of sex-biased genes, including many lncRNA genes. Significant associations between single nucleotide polymorphisms and sex-specific gene expression were identified as expression quantitative trait loci (eQTLs), many of which showed strong sex-dependent associations. Remarkably, autosomal genetic modifiers of sex-specific genes were found to account for more than 200 instances of gain or loss of sex-specificity across eight Diversity Outbred mouse founder strains. Sex-biased STAT5 binding sites and open chromatin regions with strain-specific variants were significantly enriched at eQTL regions regulating correspondingly sex-specific genes, supporting the proposed functional regulatory nature of the eQTL regions identified. Binding of the male-biased, growth hormone-regulated repressor BCL6 was most highly enriched at trans-eQTL regions controlling female-specific genes. Co-regulated gene clusters defined by overlapping eQTLs included sets of highly correlated genes from different chromosomes, further supporting trans-eQTL action. These findings elucidate how an unexpectedly large number of autosomal factors work in tandem with growth hormone signaling pathways to regulate the individual variability associated with sex differences in liver metabolism and disease. Author summaryMale-female differences in liver gene expression confer sex differences in many biological processes relevant to health and disease, including lipid and drug metabolism and liver disease susceptibility. While the role of hormonal factors, most notably growth hormone, in regulating hepatic sex differences is well established, little is known about how autosomal genetic factors impact sex differences on an individual basis. Here, we harness the power of mouse genetics provided by the Diversity Outbred mouse model to discover significant genome-wide associations between genetic variants and sex-specific liver gene expression. Remarkably, we found that autosomal expression quantitative trait loci with a strong sex-bias account for the loss or gain of sex-specific expression of more than 200 autosomal genes seen across eight founder mice strains. Genetic associations with sex-specific genes were enriched for sex-biased and growth hormone-dependent regulatory regions harboring strain-specific genetic variants. Co-regulated gene clusters identified by overlapping regulatory regions included highly correlated genes from different chromosomes. These findings reveal the extensive regulatory role played by autosomal genetic variants, working in tandem with growth hormone signaling pathways, in the transcriptional control of sex-biased genes, many of which have been implicated in sex differential outcomes in liver metabolism and disease susceptibility.

genetics↗

bioRxiv2020

The Multivariate Genetic Architecture of Literacy-, Language- and Working Memory-related Abilities as Captured by Genome-wide Variation

There is genetic overlap between many measures of literacy, language and phonological working memory (PWM) though our knowledge of multivariate genetic architectures is incomplete. Here, we directly modeled genetic trait interrelationships in unrelated UK youth (8-13 years, N=6,453), as captured by genome-wide relationship matrices, using novel structural equation modeling techniques. We identified, besides shared genetic factors across different domains (explaining 91-97% genetic variance in literacy-related measures such as passage reading fluency, spelling, phonemic awareness, 44% in oral language and 53% in PWM), evidence for distinct cognitive abilities; trait-specific genetic influences ranged between 47% for PWM to 56% for oral language. Among reading fluency measures (non-word, word and passage reading), single-word reading was genetically most diverse. Multivariate genetic and residual covariance patterns showed concordant effect directionality, except for near-zero residual correlations between oral language and literacy-related abilities. These findings suggest differences in etiological mechanisms and trait modifiability even among genetically highly correlated skills.

genetics↗

bioRxiv2020

The Genetic Architecture of Robustness for Flight Performance in Drosophila

A central challenge of quantitative genetics is partitioning phenotypic variation into genetic and non-genetic components. These non-genetic components are usually interpreted as environmental effects; however, variation between genetically identical individuals in a common environment can still exhibit phenotypic variation. A traits resistance to variation is called robustness, though the genetics underlying it are poorly understood. Accordingly, we performed an association study on a previously studied, whole organism trait: flight performance. Using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, we surveyed variation at the level of single nucleotide polymorphisms and whole genes using additive, marginal, and epistatic analyses that associated with robustness for flight performance. Many genes had developmental and neurodevelopmental annotations, and many more were identified from associations that differed between sexes. Additionally, many genes were pleiotropic, with several annotated for fitness-associated traits (e.g. gametogenesis and courtship). Our results corroborate a previous study for genetic modifiers of micro-environmental variation, and have sizable overlap with studies for modifiers of wing morphology and courtship behavior. These results point to an important and shared role for genetic modifiers of robustness of flight performance affecting development, neurodevelopment, and behavior.

genetics↗

bioRxiv2020

A higher burden of multiple sclerosis genetic risk confers an earlier onset

Multiple sclerosis is a neurodegenerative, autoimmune disease characterized by inrreversible neurological disability. The age at onset of multiple sclerosis is an objective and influential predictor of the evolution of multiple sclerosis independent of disease duration. Little is known about the mechanisms contributing to variation in onset age of multiple sclerosis, though HLA-DRB1*15:01, the predominant risk variant, confers an earlier onset. Here we present an age at onset genome-wide association analysis for 9.2 million variants, including gene-based and pathway enrichment analyses, for 3,495 cases who were non-Latinx white with onset [≥]18 years. We investigated whether a higher burden of multiple sclerosis risk variants conferred an earlier age at onset for combinations of HLA-DRB1*15:01 alleles and quintiles of a genetic risk score for 200 risk variants that reside outside the major histocompatibility complex. The study population had a mean age at onset of 32 years, 29% was male, and 46% were HLA-DRB1*15:01 carriers. HLA-DRB1*15:01 carriers were on average one year younger at onset than non-carriers (p<0.001); a similar effect was observed for a 10-risk-allele increase in the genetic risk score (p<1x10-8). Those in the highest genetic risk score quintile (n=717) were on average 2.5 years younger at onset than those in the lowest quintile (n=698; p=1.2x10-7). For those with the greatest genetic risk burden (highest genetic risk score quintile with two HLA-DRB1*15:01 alleles) were on average five years younger at onset (p=0.002) than those with the lowest genetic risk burden (lowest genetic risk score quintile with no HLA-DRB1*15:01 alleles). There was an apparent inverse relationship between the genetic multiple sclerosis risk burden and age at onset of multiple sclerosis (p<5x10-8). We did not observe any individual variants reaching genome-wide significance in the genome-wide association analysis of age at onset. The most significantly associated independent genic loci (p<5x10-6) were located within HLA-DQB1, COL21A1, LINC01484, UBR3, and CSMD1. At the gene-level, the most significant associations (p<5x10-5) were for SSB, TRAFD1, HECTD2, MMP8, NAA25 and UBR3. There was an enrichment of genes involved in adaptive and innate immunity, specifically genes in the complement pathway, and genes involved in synapses and collagen biosynthesis. In summary, we demonstrated a significant gradient between elevated genetic risk burden and an earlier onset of multiple sclerosis.

genetics↗

bioRxiv2021

Genetic variants associated mRNA stability in lung

Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs). Specifically, we computationally inferred mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3,942 genes and 186,132 eQTLs for 4,751 genes from 15,122,700 genetic variants for 13,476 genes, respectively. Interesting, our results indicated that stQTLs were enriched in the CDS and 3UTR regions, while eQTLs are enriched in the CDS, 3UTR, 5UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels. Author SummaryIn the past decade, many studies have identified genetic variants associated with gene expression level (eQTLs) in different phenotypes, including tissues and diseases. Gene expression is the result of cooperation between transcriptional regulation, such as transcriptional activity, and post-transcriptional regulation, such as mRNA stability. Here, we present a computational framework that take advantage of recently developed methods to estimate mRNA stability from RNA-Seq, which is widely used to estimate gene expression, and then to identify genetic variants associated with mRNA stability (stQTLs) in lung tissue. Compared to eQTLs, we found that genetic variants that affects mRNA stability are more significantly located in the CDS and 3UTR regions, which are known to interact with RNA-binding proteins (RBPs) or microRNAs to regulate stability. In addition, stQTLs are significantly more likely to overlap the binding sites of RBPs. We show that the six RBPs that most significantly bind to stQTLs are all known to regulate mRNA stability. This pipeline of simultaneously identifying eQTLs and stQTLs using only RNA-Seq data can provide higher resolution than traditional eQTLs study to better understand the molecular mechanisms of genetic variants on the regulation of gene expression.

genetics↗

bioRxiv2021

A systematic review and meta-analysis of genetic parameters for complex quantitative traits in aquatic animal species

A systematic review and meta-analysis of genetic parameters underlying inheritance and complex biological relationships for quantitative traits are not available for aquatic animal species. I synthesised and conducted a comprehensive meta-analysis of the published information from 1985 to 2017 on heritability, common full-sib effects and genetic correlations for quantitative characters of biological importance (growth, carcass and flesh quality, disease resistance, deformity and reproduction) for aquaculture species. A majority of the studies (73.5%) focussed on growth related traits (body weight), followed by those on disease resistance (15.9%), whereas only a limited number of studies (10.6%) reported heritability estimates for carcass and flesh quality, deformity or reproduction characteristics. The weighted means of heritability for growth (weight, food utilisation efficiency, maturity) and carcass (fillet weight and yield) traits were moderate. Resistance against various bacteria, virus and parasites were moderately to highly heritable. Across aquatic animal species, the weighted heritability for a range of deformity measures and reproductive traits (fecundity, early survival) was low and not significantly different from zero. The common full-sibs (c2) accounted for a large proportion of total variance for body traits but it was of smaller magnitude in later phase of the growth development. The c2 effects however were not significant or in many cases they were not reported for carcass and flesh quality attributes as well as survival and deformity. The maternal genetic effects were not available for all traits studied especially for reproductive and early growth characters. Genetic correlations between body and carcass traits were high and positive, suggesting that selection for rapid growth can improve fillet weight, a carcass trait of paramount importance. Body weight, the most commonly used selection criterion in aquatic animals, showed non-significant genetic correlation with disease resistance, likely because both positive and negative genetic associations between the two types of traits. Interestingly the genetic associations between growth and reproductive performance (fecundity) and fry traits (fry weight, fry survival) were favourable. To date, there are still no published data on genetic relationships of carcass and flesh quality with disease resistance or reproductive performance in any aquaculture species. Additionally, the present study discussed new traits, including functional, immunological, behavioural and social interaction as well as uniformity that are emerging as potential selection criteria and which can be exploited in future genetic improvement programs for aquatic animals.

genetics↗

bioRxiv2021

The genetic etiology of longitudinal measures of predicted brain ageing in a population-based sample of mid to late-age males

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimers Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 52 to 72 years. Biometrical genetic analyses twin models revealed significant and highly correlated estimates of additive genetic heritability ranging from 59% to 75%. Multivariate longitudinal modelling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences. HighlightsWe explored the genetic and environmental etiology of MRI-based predicted brain age (PBA) in a longitudinal sample of males starting in midlife. Genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and largely explained by common genetic influences.

genetics↗

bioRxiv2021

The genetic diversity of Ethiopian barley genotypes in relation to their geographical origin

Ethiopia is recognized as a center of diversity for barley, and its landraces are known for the distinct genetic features compared to other barley collections. The genetic diversity of Ethiopian barley likely results from the highly diverse topography, altitude, climate conditions, soil types, and farming systems. To get detailed information on the genetic diversity a panel of 260 accessions, comprising 239 landraces and 21 barley breeding lines, obtained from the Ethiopian biodiversity institute (EBI) and the national barley improvement program, respectively were studied for their genetic diversity using the 50k iSelect single nucleotide polymorphism (SNP) array. A total of 983 highly informative SNP markers were used for structure and diversity analysis. Three genetically distinct clusters were obtained from the structure analysis comprising 80, 71, and 109 accessions, respectively. Analysis of molecular variance (AMOVA) revealed the presence of higher genetic variation (89%) within the clusters than between the clusters (11%), with moderate genetic differentiation (PhiPT=0.11) and adequate gene flow (Nm=2.02). The Mantel test revealed that the genetic distance between accessions is poorly associated with their geographical distance. Despite the observed weak correlation between geographic distance and genetic differentiation, for some regions like Gonder, Jimma, Gamo-Gofa, Shewa, and Welo, more than 50% of the landraces derived from these regions are assigned to one of the three clusters.

genetics↗

bioRxiv2021

Substrate-Specific Effects of Natural Genetic Variation on Proteasome Activity

Protein degradation is an essential biological process that regulates protein abundance and removes misfolded and damaged proteins from cells. In eukaryotes, most protein degradation occurs through the stepwise actions of two functionally distinct entities, the ubiquitin system and the proteasome. Ubiquitin system enzymes attach ubiquitin to cellular proteins, targeting them for degradation. The proteasome then selectively binds and degrades ubiquitinated substrate proteins. Genetic variation in ubiquitin system genes creates heritable differences in the degradation of their substrates. However, the challenges of measuring the degradative activity of the proteasome independently of the ubiquitin system in large samples have limited our understanding of genetic influences on the proteasome. Here, using the yeast Saccharomyces cerevisiae, we built and characterized reporters that provide high-throughput, ubiquitin system-independent measurements of proteasome activity. Using single-cell measurements of proteasome activity from millions of genetically diverse yeast cells, we mapped 15 loci across the genome that influence proteasomal protein degradation. Twelve of these 15 loci exerted specific effects on the degradation of two distinct proteasome substrates, revealing a high degree of substrate-specificity in the genetics of proteasome activity. Using CRISPR-Cas9-based allelic engineering, we resolved a locus to a causal variant in the promoter of RPT6, a gene that encodes a subunit of the proteasomes 19S regulatory particle. Our results reveal the complex genetic architecture of proteasome activity and suggest that genetic influences on the proteasome may be an important source of variation in the many cellular and organismal traits shaped by protein degradation. Author SummaryProtein degradation controls the abundance of cellular proteins and serves an essential role in protein quality control by eliminating misfolded and damaged proteins. In eukaryotes, most protein degradation occurs in two steps. The ubiquitin system first targets proteins for degradation by attaching ubiquitin to them. The proteasome then selectively binds and degrades ubiquitinated proteins. Understanding how individual genetic differences affect the activity of the proteasome could improve our understanding of the many traits influenced by protein degradation. However, most assays that measure proteasomal protein degradation are not suitable for use in large samples or are affected by changes in the activity of the ubiquitin system. Using yeast, we built reporters that provide high-throughput measurements of proteasome activity independently of the ubiquitin system. We used measurements of proteasome activity from millions of live, single cells to identify regions of the genome with DNA variants that affect proteasomal protein degradation. We identified 15 such regions, showing that proteasome activity is a genetically complex trait. Using genome engineering, we found that one locus contained a variant in the promoter of a proteasome subunit gene that affected the activity of the proteasome towards multiple substrates. Our results demonstrate that individual genetic differences shape proteasome activity and suggest that these differences may contribute to variation in the many traits regulated by protein degradation.

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bioRxiv2021

Genetic insights into the management and conservation of Arctic charr in North Wales

Aquatic species throughout the world are threatened by extinction in many parts of their range, particularly in their most southerly distributions. Arctic charr (Salvelinus alpinus) is a Holarctic species with a distribution that includes the glacial lakes of North Wales, towards it southern limit. To date, no genetic studies have been conducted to determine the genetic health of the three remaining native Arctic charr populations in North Wales, despite exposure to stocking and adverse environmental and ecological conditions. We used seven microsatellite loci to determine whether: 1) genetic differentiation existed between native populations; 2) translocated populations from Llyn Peris were genetically similar to the historically connected Llyn Padarn population; and 3) hatchery supplementation negatively impacted genetic diversity in Llyn Padarn. All three native populations retained their genetic integrity, with Llyn Bodlyn showing high levels of divergence (FST = 0.26 {+/-} 0.02SD) as well as low genetic diversity (HO 0.30) compared to remaining populations (HO 0.64 {+/-} 0.14SD). Although evidence suggests that stocking increased the effective population size of Llyn Padarn in the short term without impacting genetic diversity, the long term effects of such practices are yet to be seen. Results provide baseline data for conservation management, and highlight the need for protection of small isolated populations that are being negatively impacted by the processes of genetic drift due to escalating anthropogenic pressures. Continual monitoring of both Arctic charr and their habitats using a combination of methods will increase the likelihood that these threatened and iconic populations will persist in the future.

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bioRxiv2022

Extremely low level of genetic diversity in Gentiana yakushimensis, an endangered species in Yakushima Island, Japan

Rare or endangered species largely contributing to global biodiversity are essential components of ecosystems. Because genetic diversity depends not only on apparent population size but also demographic processes, the conservation priority of endangered species based on population genetics needs to be assessed by comparison with common congeners and demographic events. In this study, we performed population genetic analysis for Gentiana yakushimensis Makino, a rare and endangered plant species with an isolated distribution on Yakushima Island, Japan. We performed comparison of genetic diversity with the congener (Gentiana triflora Pall. var. japonica (Kusn.) H.Hara), demographic inference, and bottleneck test based on genome-wide single nucleotide polymorphisms. The result was that G. yakushimensis had an extremely low genetic diversity level and a high inbreeding coefficient level compared to those of the congener. The population of G. yakushimensis was estimated to experience an expansion during the Last Glacial Maximum and a recent bottleneck by the demographic inference and the bottleneck test. Therefore, the low level of genetic diversity of this species might have resulted from the impact of the recent bottleneck and the long-term maintenance of a small population size. Due to the long lifespan of the genus Gentiana, the current level of genetic diversity probably does not reflect a large part of recent demographic events, which requires long-term monitoring of changes in genetic diversity to identify the time lag between the reduction in apparent population size and genetic diversity as a future outlook.

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bioRxiv2022

Insight on Selective Breeding the Robustness Based on Field Survival Records: New Genetic Evaluation of Survival Traits in White-leg Shrimp (Penaeus vannamei) Breeding Line

Survival is an old trait in animal breeding, yet commonly neglected nowadays for its simple binary records and low levels of heritability in aquaculture species. These traits however, can provide valuable field data when selecting for robustness in genetic improvement programs. In the current study, linear multivariate animal model (LMA) was used for the genetic analysis of survival records from two-year classes (BL2019 and BL2020) of white-leg shrimp (Penaeus vannamei) breeding lines with a total number of 52, 248 individuals from 481 fullsib families recorded for data collection. During grow-out test period, 10 day intervals of survival data were considered as independent traits. Two survival definitions, binary survivability (S) and continuous survival in days (SL), were used for the genetic analysis of survival records to investigate; i) whether adding more survival time information could improve estimation of genetic parameters; ii) the trajectory of survival heritability across time, and iii) patterns of genetic correlations of survival traits across time. Levels of heritability estimates for both S and SL were low (0.005 to 0.076), while heritability for survival day number was found to be similar with that of binary records at each observation time and were highly genetically correlated (rg >0.8). Heritability estimates of body weight (BW) for BL2019 and BL2020 were 0.486 and 0.373, respectively. Trajectories of survival heritability across time showed a gradual increase across the grow-out test period but slowed or reached a plateau during the later grow-out test period. Genetic correlations among survival traits in the grow-out tests were moderate to high, and the closer the times were between estimates, the higher were their genetic correlations. In contrast, genetic correlations between both survival traits and body weight were low but positive. Here we provide the first report of the trajectory of heritability estimates for survival traits across time in aquaculture. Results will be useful for developing robust improved white-leg shrimp culture strains in selective breeding programs based on field survival data.

genetics↗

bioRxiv2022

Polygenic scoring accuracy varies across the genetic ancestry continuum in all human populations

Polygenic scores (PGS) have limited portability across different groupings of individuals (e.g., by genetic ancestries and/or social determinants of health), preventing their equitable use. PGS portability has typically been assessed using a single aggregate population-level statistic (e.g., R2), ignoring inter-individual variation within the population. Here we evaluate PGS accuracy at individual-level resolution, independent of its annotated genetic ancestries. We show that PGS accuracy varies between individuals across the genetic ancestry continuum in all ancestries, even within traditionally "homogeneous" genetic ancestry clusters. Using a large and diverse Los Angeles biobank (ATLAS, N= 36,778) along with the UK Biobank (UKBB, N= 487,409), we show that PGS accuracy decreases along a continuum of genetic ancestries in all considered populations and the trend is well-captured by a continuous measure of genetic distance (GD) from the PGS training data; Pearson correlation of -0.95 between GD and PGS accuracy averaged across 84 traits. When applying PGS models trained in UKBB "white British" individuals to European-ancestry individuals of ATLAS, individuals in the highest GD decile have 14% lower accuracy relative to the lowest decile; notably the lowest GD decile of Hispanic/Latino American ancestry individuals showed similar PGS performance as the highest GD decile of European ancestry ATLAS individuals. GD is significantly correlated with PGS estimates themselves for 82 out of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestry in PGS interpretation. Our results highlight the need for moving away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGS and their applications.

genetics↗

bioRxiv2022

Retrospective varying coefficient association analysis of longitudinal binary traits: application to the identification of genetic loci associated with hypertension

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations, we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

genetics↗