Search

Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene co-expression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the African American Genetics of Metabolism and Expression and Metabolic Syndrome in Men. We used weighted gene co-expression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous “consensus genes” whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight co-expressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., Hcar1), inflammation (e.g., Rgs1), adipogenesis (e.g., Tmem120b) or no previously known role in obesity (e.g., St14, Msa4a6). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.

Terminal drought is one of the major constraints to crop production in chickpea (Cicer arietinum L.). In order to map drought tolerance related traits at high resolution, we sequenced multi-parent advanced generation intercross (MAGIC) population using whole genome resequencing approach and phenotyped it under drought stress environments for two consecutive years (2013–14 and 2014–15). A total of 52.02 billion clean reads containing 4.67 TB clean data were generated on the 1136 MAGIC lines and eight parental lines. Alignment of clean data on to the reference genome enabled identification of a total, 932,172 of SNPs, 35,973 insertions, and 35,726 deletions among the parental lines. A high-density genetic map was constructed using 57,180 SNPs spanning a map distance of 1606.69 cM. Using compressed mixed linear model, genome-wide association study (GWAS) enabled us to identify 737 markers significantly associated with days to 50% flowering, days to maturity, plant height, 100 seed weight, biomass, and harvest index. In addition to the GWAS approach, an identity-by-descent (IBD)-based mixed model approach was used to map quantitative trait loci (QTLs). The IBD-based mixed model approach detected major QTLs that were comparable to those from the GWAS analysis as well as some exclusive QTLs with smaller effects. The candidate genes like FRIGIDA and CaTIFY4b can be used for enhancing drought tolerance in chickpea. The genomic resources, genetic map, marker-trait associations, and QTLs identified in the study are valuable resources for the chickpea community for developing climate resilient chickpeas.

BackgroundDominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena played a major role in quantitative genetics. However, today most genome-wide association studies (GWAS) assume alleles act additively.MethodsWe systematically investigated both dominance – here representing any non-additive effect - and additivity across 574 physiological and gene expression traits in three mammalian models: a Pig F2 Intercross, a Rat Heterogeneous Stock and a Mouse Heterogeneous Stock.ResultsIn all species, and across all physiological traits, dominance accounts for about one quarter of the heritable variance. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable assuming additivity, we identified 154, 64 and 62 novel dominance QTLs in pigs, rats and mice respectively, that were undetectable as additive QTLs. Similarly, even though most cis-acting eQTLs are additive, we observed a large fraction of dominance variance in gene expression, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, in HS rat transcriptomes, thousands of eQTLs associate with alternate transcripts and exhibit complex additive and dominant architectures, suggesting a mechanism for dominance.ConclusionsAlthough heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement.

Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand selection within a breeding context, and how it might be optimised, we analysed genotypic and phenotypic data from a diverse, 16-founder wheat multi-parent advanced generation inter-cross (MAGIC) population.Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90% of traits, improving grain yield prediction accuracy by 3-52%. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10-36%. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimised long-term genetic gains.Simulations of selection on grain yield found indirect responses in related traits, which involved optimisation of antagonistic trait relationships. We found multi-trait selection indices could be used to optimise undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability.Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model, accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes and provide insights into mechanisms for continued genetic gain in a limited genepool and optimisation of multiple traits for crop improvement.

Despite the successes of human genome-wide association studies, the causal genes underlying most metabolic traits remain unclear. We used outbred heterogeneous stock (HS) rats, coupled with expression data and mediation analysis, to identify quantitative trait loci (QTLs) and candidate gene mediators for adiposity, glucose tolerance, serum lipids, and other metabolic traits. Physiological traits were measured in 1519 male HS rats, with liver and adipose transcriptomes measured in over 410 rats. Genotypes were imputed from low coverage whole genome sequence. Linear mixed models were used to detect physiological and expression QTLs (pQTLs and eQTLs, respectively), employing both SNP- and haplotype-based models for pQTL mapping. Genes with cis-eQTLs that overlapped pQTLs were assessed as causal candidates through mediation analysis. We identified 14 SNP-based pQTLs and 19 haplotype-based pQTLs, of which 10 were in common. Using mediation, we identified the following genes as candidate mediators of pQTLs: Grk5 for a fat pad weight pQTL on Chr1, Krtcap3 for fat pad weight and serum lipids pQTLs on Chr6, Ilrun for a fat pad weight pQTL on Chr20 and Rfx6 for a whole pancreatic insulin content pQTL on Chr20. Furthermore, we verified Grk5 and Ktrcap3 using gene knock-down/out models, thereby shedding light on novel regulators of obesity.

Introduction: Skeletal abnormalities and malocclusions have varied features that impact populations globally, impairing aesthetics and lowering life quality. The prevalence of the Skeletal Class III disease is the lowest among all angle malocclusions, with varied prevalence across nations. Environmental, genetic, and societal factors play a role in its numerous etiologies. In this study, we conducted a thorough search across the published data relating to quantitative trait loci (QTL) and the genes associated with Class III progression in humans, discussed these findings and their limitations, and proposed future directions and strategies for studying this phenotype. Methods: An inclusive search of published papers in the PubMed and Google Scholar search engines using the following terms: 1. Human skeletal Class III; 2. Genetics of Human skeletal Class III; 3. QTL mapping and gene associated with human skeletal Class III; 4. enriched skeletal Class-III-malocclusion-associated pathways. Results: Our search has found 53 genes linked with skeletal Class III malocclusion reported in humans, genes associated with epigenetics and phenomena, and the top 20 enriched pathways associated with skeletal Class III malocclusion. Conclusions: The human investigations yielded some contentious conclusions. We conducted a genome-wide association study (GWAS), an epigenetics-wide association study (EWAS), RNA-seq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro- and small-RNA, and long non-coding RNA analysis in tissues connected to skeletal Class III malocclusion phenotype in tissues connected with the skeletal phenotype. Finally, we invite regional, national, and international orthodontists and surgeons to join this effort by contributing human samples with skeletal Class III malocclusion following the accepted Helsinki ethical protocol to challenge these phenomena jointly.

Rice varieties whose quality is graded as excellent have a lower percent grain chalkiness (PGC) of two per cent and below with higher whole grain yields upon milling, leading to higher economic returns for farmers. We have conducted a genome‐wide association study (GWAS) using a combined population panel of indica and japonica rice varieties, and identified a total of 746 single nucleotide polymorphisms (SNPs) that were strongly associated with the chalk phenotype, covered 78 Quantitative Trait Loci (QTL) regions. Among them, 21 were high‐value QTLs, as they explained at least 10 % of the phenotypic variance for PGC. A combined epistasis and GWAS was applied to dissect the genetics of the complex chalkiness trait, and its regulatory cascades were validated using gene regulatory networks. Promising novel epistatic interactions were found between the loci of chromosomes 6 (PGC6.1) and 7 (PGC7.8) that contributed to lower PGC. Based on haplotype mining only a few modern rice varieties confounded with a lower chalkiness, and they possess several PGC QTLs. The importance of PGC6.1 was validated through multi‐parent advanced generation intercrosses and several low‐chalk lines possessing superior haplotypes were identified. The results of this investigation have deciphered the underlying genetic networks that can reduce PGC to 2%, and will thus support future breeding programs to improve the grain quality of elite genetic material with high‐yielding potentials.

Key Message Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Abstract Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.

Despite the successes of human genome-wide association studies, the causal genes underlying most metabolic traits remain unclear. We used outbred heterogeneous stock (HS) rats, coupled with expression data and mediation analysis, to identify quantitative trait loci (QTLs) and candidate gene mediators for adiposity, glucose tolerance, serum lipids and other metabolic traits. Physiological traits were measured in 1519 male HS rats, with liver and adipose transcriptomes measured in over 410 rats. Genotypes were imputed from low coverage whole genome sequence. Linear mixed models were used to detect physiological and expression QTLs (pQTLs and eQTLs, respectively), employing both SNP- and haplotype-based models for pQTL mapping. Genes with cis-eQTLs that overlapped pQTLs were assessed as causal candidates through mediation analysis. We identified 15 SNP-based pQTLs and 19 haplotype-based pQTLs, of which 11 were in common. Using mediation, we identified the following genes as candidate mediators of pQTLs: Grk5 for a fat pad weight pQTL on Chr1, Krtcap3 for fat pad weight and serum lipids pQTLs on Chr6, Ilrun for a fat pad weight pQTL on Chr20 and Rfx6 for a whole pancreatic insulin content pQTL on Chr20. Furthermore, we verified Grk5 and Ktrcap3 using gene knock-down/out models, thereby shedding light on novel regulators of obesity.

Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

Tetraploid emmer wheat (Triticum turgidum ssp. dicoccon) is a progenitor of the world’s most widely grown crop, hexaploid bread wheat (Triticum aestivum), as well as the direct ancestor of tetraploid durum wheat (T. turgidum subsp. turgidum). Emmer was one of the first cereals to be domesticated in the old world; it was cultivated from around 9700 BC in the Levant1,2 and subsequently in south-western Asia, northern Africa and Europe with the spread of Neolithic agriculture3,4. Here, we report a whole-genome sequence from a museum specimen of Egyptian emmer wheat chaff, 14C dated to the New Kingdom, 1130–1000 BC. Its genome shares haplotypes with modern domesticated emmer at loci that are associated with shattering, seed size and germination, as well as within other putative domestication loci, suggesting that these traits share a common origin before the introduction of emmer to Egypt. Its genome is otherwise unusual, carrying haplotypes that are absent from modern emmer. Genetic similarity with modern Arabian and Indian emmer landraces connects ancient Egyptian emmer with early south-eastern dispersals, whereas inferred gene flow with wild emmer from the Southern Levant signals a later connection. Our results show the importance of museum collections as sources of genetic data to uncover the history and diversity of ancient cereals., M.F.S. and R.M. are supported by RCUK BBSRC grant BB/M011585/1. R.M. is also supported by RCUK BBSRC grant BB/P024726/1; L.R.B. by the Spanish Ministry of Economy and Competitiveness Severo Ochoa Programme for Centres of Excellence in R&D 2016-2019 (SEV-2015-0533) and CERCA Programme, Generalitat de Catalunya; M.G.T. and S.B. by a Wellcome Trust Senior Research Fellowship, grant 100719/Z/12/Z. D.Q.F. and C.S. are supported by the ERC ComPag project, grant number 323842. V.E.M. and S.B. are partially supported by the RCUK NERC grant NE/P012574/1. UCL computing infrastructure was supported by BBSRC grant BB/R01356X/1.

Background Familial adenomatous polyposis is an inherited genetic disease, characterized by colorectal polyps. It is caused by inactivating mutations in the Adenomatous polyposis coli (Apc) gene. Mice carrying a nonsense mutation in the Apc gene at R850, which is designated ApcMin/+ (Multiple intestinal neoplasia), develop intestinal adenomas. Several genetic modifier loci of Min (Mom) were previously mapped, but so far, most of the underlying genes have not been identified. To identify novel modifier loci associated with ApcMin/+, we performed quantitative trait loci (QTL) analysis for polyp development using 49 F1 crosses between different Collaborative Cross (CC) lines and C57BL/6 J-ApcMin/+mice. The CC population is a genetic reference panel of recombinant inbred lines, each line independently descended from eight genetically diverse founder strains. C57BL/6 J-ApcMin/+ males were mated with females from 49 CC lines. F1 offspring were terminated at 23 weeks and polyp counts from three sub-regions (SB1–3) of small intestinal and colon were recorded. Results The number of polyps in all these sub-regions and colon varied significantly between the different CC lines. At 95% genome-wide significance, we mapped nine novel QTL for variation in polyp number, with distinct QTL associated with each intestinal sub-region. QTL confidence intervals varied in width between 2.63–17.79 Mb. We extracted all genes in the mapped QTL at 90 and 95% CI levels using the BioInfoMiner online platform to extract, significantly enriched pathways and key linker genes, that act as regulatory and orchestrators of the phenotypic landscape associated with the ApcMin/+ mutation. Conclusions Genomic structure of the CC lines has allowed us to identify novel modifiers and confirmed some of the previously mapped modifiers. Key genes involved mainly in metabolic and immunological processes were identified. Future steps in this analysis will be to identify regulatory elements – and possible epistatic effects – located in the mapped QTL. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07890-x.

BackgroundBreeding has helped improve bread wheat yield significantly over the last century. Understanding the potential for future crop improvement depends on relating segregating genetic variation to agronomic traits.ResultsWe bred NIAB Diverse MAGIC population, comprising over 500 recombinant inbred lines, descended from sixteen bread wheat varieties released between 1935-2004. We sequenced the founders’ exomes and promotors by capture. Despite being highly representative of North-West European wheat and capturing 73% of global polymorphism, we found 89% of genes contained no more than three haplotypes. We sequenced each line with 0.3x coverage whole-genome sequencing, and imputed 1.1M high-quality SNPs that were over 99% concordant with array genotypes. Imputation accuracy remained high at coverage as low as 0.076x, with or without the use of founder genomes as reference panels. We created a genotype-phenotype map for 47 traits over two years. We found 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Outside of these loci most traits are polygenic, as revealed by multi-locus shrinkage modelling.ConclusionsHistorically, wheat breeding has reshuffled a limited palette of haplotypes; continued improvement will require selection at dozens of loci of diminishing effect, as most of the major loci we mapped are known. Breeding to optimise one trait generates correlated trait changes, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break critical unfavourable trait-trait associations. Finally, low coverage whole genome sequencing of bread wheat populations is an economical and accurate genotyping strategy.

Respiratory infection caused by P. aeruginosa is one of the most critical health burdens worldwide. People affected by P. aeruginosa infection include patients with a weakened immune system, such as those with cystic fibrosis (CF) genetic disease or non-CF bronchiectasis. Disease outcomes range from fatal pneumonia to chronic life-threatening infection and inflammation leading to the progressive deterioration of pulmonary function. The development of these respiratory infections is mediated by multiple causes. However, the genetic factors underlying infection susceptibility are poorly known and difficult to predict. Our study employed novel approaches and improved mouse disease models to identify genetic modifiers that affect the severity of P. aeruginosa lung infection. We identified candidate genes to enhance our understanding of P. aeruginosa infection in humans and provide a proof of concept that could be exploited for other human pathologies mediated by bacterial infection., Human genetics influence a range of pathological and clinical phenotypes in respiratory infections; however, the contributions of disease modifiers remain underappreciated. We exploited the Collaborative Cross (CC) mouse genetic-reference population to map genetic modifiers that affect the severity of Pseudomonas aeruginosa lung infection. Screening for P. aeruginosa respiratory infection in a cohort of 39 CC lines exhibits distinct disease phenotypes ranging from complete resistance to lethal disease. Based on major changes in the survival times, a quantitative-trait locus (QTL) was mapped on murine chromosome 3 to the genomic interval of Mb 110.4 to 120.5. Within this locus, composed of 31 protein-coding genes, two candidate genes, namely, dihydropyrimidine dehydrogenase (Dpyd) and sphingosine-1-phosphate receptor 1 (S1pr1), were identified according to the level of genome-wide significance and disease gene prioritization. Functional validation of the S1pr1 gene by pharmacological targeting in C57BL/6NCrl mice confirmed its relevance in P. aeruginosa pathophysiology. However, in a cohort of Canadian patients with cystic fibrosis (CF) disease, regional genetic-association analysis of the syntenic human locus on chromosome 1 (Mb 97.0 to 105.0) identified two single-nucleotide polymorphisms (rs10875080 and rs11582736) annotated to the Dpyd gene that were significantly associated with age at first P. aeruginosa infection. Thus, there is evidence that both genes might be implicated in this disease. Our results demonstrate that the discovery of murine modifier loci may generate information that is relevant to human disease progression.

Background Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology. Methods Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high‐density single nucleotide polymorphic markers. Results Statistical analysis revealed a significant (P

A previous study of exome-sequenced schizophrenia cases and controls reported an excess of singleton, gene-disruptive variants among cases, concentrated in particular gene sets. The dataset included a number of subjects with a substantial Finnish contribution to ancestry. We have reanalysed the same dataset after removal of these subjects and we have also included non-singleton variants of all types using a weighted burden test which assigns higher weights to variants predicted to have a greater effect on protein function. We investigated the same 31 gene sets as previously and also 1454 GO gene sets. The reduced dataset consisted of 4225 cases and 5834 controls. No individual variants or genes were significantly enriched in cases but 13 out of the 31 gene sets were significant after Bonferroni correction and the “FMRP targets” set produced a signed log p value (SLP) of 7.1. The gene within this set with the highest SLP, equal to 3.4, was FYN, which codes for a tyrosine kinase which phosphorylates glutamate metabotropic receptors and ionotropic NMDA receptors, thus modulating their trafficking, subcellular distribution and function. In the most recent GWAS of schizophrenia it was identified as a “prioritized candidate gene”. Two of the subunits of the NMDA receptor which are substrates of FYN are coded for by GRIN1 (SLP = 1.7) and GRIN2B (SLP = 2.1). Of note, for some sets there was a substantial enrichment of non-singleton variants. Of 1454 GO gene sets, three were significant after Bonferroni correction. Identifying specific genes and variants will depend on genotyping them in larger samples and/or demonstrating that they cosegregate with illness within pedigrees. Electronic supplementary material The online version of this article (10.1007/s10519-018-9893-3) contains supplementary material, which is available to authorized users.

Heart rate variability (HRV) is a commonly used metric in animal science to quantify an aspect of the physiological response to stressors. HRV analysis relies upon accurate detection of the electrical activity of the heart to define inter-beat interval (IBI). In horses, this can be problematic due to specific features of the equine ECG waveform and a high incidence of 2nd-degree atrioventricular (AV) block in clinically normal horses at rest. The current study validates the collection and interpretation of IBI data from two heart rate monitors (HRMs) principally designed for human personal monitoring (Polar V800 and Actiheart 5). 30 min of synchronous IBI data was collected using the two HRMs and a 4-lead telemetric ECG that is widely used in equine veterinary clinical practice (Televet 100). The study was conducted using a mixed sample (n = 17) of general riding horses, with (n = 4) and without (n = 13) cardiac dysrhythmias. HRV data was derived from each IBI data set and compared following application of a series of correction factors. There was a very strong inter-class correlation of HRV parameters from both the Actiheart 5 and Polar V800 with the Televet 100 data. Both HRMs providing an acceptable level of agreement with both time and frequency domain HRV parameters. The data from the Actiheart 5 were found to be interchangeable with the Televet 100 in respect of beat detection whereas the Polar V800 data required a 0.4 s artefact correction to give best agreement. Both HRMs have utility in equine research for demonstrating the direction and magnitude of change in HRV parameters, although users must apply appropriate artefact correction to maximise data validity.

OBJECTIVE Obesity is a major risk factor for multiple diseases and is in part heritable, yet the majority of causative genetic variants that drive excessive adiposity remain unknown. Here, outbred heterogeneous stock (HS) rats were used in controlled environmental conditions to fine-map novel genetic modifiers of adiposity. METHODS Body weight and visceral fat pad weights were measured in male HS rats that were also genotyped genome-wide. Quantitative trait loci (QTL) were identified by genome-wide association of imputed single-nucleotide polymorphism (SNP) genotypes using a linear mixed effect model that accounts for unequal relatedness between the HS rats. Candidate genes were assessed by protein modeling and mediation analysis of expression for coding and noncoding variants, respectively. RESULTS HS rats exhibited large variation in adiposity traits, which were highly heritable and correlated with metabolic health. Fine-mapping of fat pad weight and body weight revealed three QTL and prioritized five candidate genes. Fat pad weight was associated with missense SNPs in Adcy3 and Prlhr and altered expression of Krtcap3 and Slc30a3, whereas Grid2 was identified as a candidate within the body weight locus. CONCLUSIONS These data demonstrate the power of HS rats for identification of known and novel heritable mediators of obesity traits.

Meiotic recombination ensures the faithful segregation of chromosomes and influences patterns of genetic diversity. Morgan et al. used genotype data.. Meiotic recombination is an essential feature of sexual reproduction that ensures faithful segregation of chromosomes and redistributes genetic variants in populations. Multiparent populations such as the Diversity Outbred (DO) mouse stock accumulate large numbers of crossover (CO) events between founder haplotypes, and thus present a unique opportunity to study the role of genetic variation in shaping the recombination landscape. We obtained high-density genotype data from 6886 DO mice, and localized 2.2 million CO events to intervals with a median size of 28 kb. The resulting sex-averaged genetic map of the DO population is highly concordant with large-scale (order 10 Mb) features of previously reported genetic maps for mouse. To examine fine-scale (order 10 kb) patterns of recombination in the DO, we overlaid putative recombination hotspots onto our CO intervals. We found that CO intervals are enriched in hotspots compared to the genomic background. However, as many as 26% of CO intervals do not overlap any putative hotspots, suggesting that our understanding of hotspots is incomplete. We also identified coldspots encompassing 329 Mb, or 12% of observable genome, in which there is little or no recombination. In contrast to hotspots, which are a few kilobases in size, and widely scattered throughout the genome, coldspots have a median size of 2.1 Mb and are spatially clustered. Coldspots are strongly associated with copy-number variant (CNV) regions, especially multi-allelic clusters, identified from whole-genome sequencing of 228 DO mice. Genes in these regions have reduced expression, and epigenetic features of closed chromatin in male germ cells, which suggests that CNVs may repress recombination by altering chromatin structure in meiosis. Our findings demonstrate how multiparent populations, by bridging the gap between large-scale and fine-scale genetic mapping, can reveal new features of the recombination landscape.

To define renal molecular mechanisms that are affected by permanent hyperglycaemia and might promote phenotypes relevant to diabetic nephropathy, we carried out linkage analysis of genome-wide gene transcription in the kidneys of F2 offspring from the Goto-Kakizaki (GK) rat model of type 2 diabetes and normoglycaemic Brown Norway (BN) rats. We mapped 2526 statistically significant expression quantitative trait loci (eQTLs) in the cross. More than 40% of eQTLs mapped in the close vicinity of the linked transcripts, underlying possible cis-regulatory mechanisms of gene expression. We identified eQTL hotspots on chromosomes 5 and 9 regulating the expression of 80-165 genes, sex or cross direction effects, and enriched metabolic and immunological processes by segregating GK alleles. Comparative analysis with adipose tissue eQTLs in the same cross showed that 496 eQTLs, in addition to the top enriched biological pathways, are conserved in the two tissues. Extensive similarities in eQTLs mapped in the GK rat and in the spontaneously hypertensive rat (SHR) suggest a common aetiology of disease phenotypes common to the two strains, including insulin resistance, which is a prominent pathophysiological feature in both GK rats and SHRs. Our data shed light on shared and tissue-specific molecular mechanisms that might underlie aetiological aspects of insulin resistance in the context of spontaneously occurring hyperglycaemia and hypertension., Summary: Kidney and fat expression QTL mapping in rat models of spontaneously occurring insulin resistance associated with either diabetes or hypertension reveals conserved gene expression regulation, suggesting shared aetiology of disease phenotypes.

Type-2 diabetes (T2D) is a complex metabolic disease characterized by impaired glucose tolerance. Despite environmental high risk factors, host genetic background is a strong component of T2D development. Herein, novel highly genetically diverse strains of collaborative cross (CC) lines from mice were assessed to map quantitative trait loci (QTL) associated with variations of glucose-tolerance response. In total, 501 mice of 58 CC lines were maintained on high-fat (42 % fat) diet for 12 weeks. Thereafter, an intraperitoneal glucose tolerance test (IPGTT) was performed for 180 min. Subsequently, the values of Area under curve for the glucose at zero and 180 min (AUC0-180), were measured, and used for QTL mapping. Heritability and coefficient of variations in glucose tolerance (CVg) were calculated. One-way analysis of variation was significant (P

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the western world, with spectrum from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis. NAFLD developments are known to be affected by host genetic background. Herein we emphasize the power of collaborative cross (CC) mouse for dissecting this complex trait and revealing quantitative trait loci (QTL) controlling hepatic fat accumulation in mice. 168 female and 338 male mice from 24 and 37 CC lines, respectively, of 18-20 weeks old, maintained on standard rodent diet, since weaning. Hepatic fat content was assessed, using dual DEXA scan in the liver. Using the available high-density genotype markers of the CC line, QTL mapping associated with percentage liver fat accumulation was performed. Our results revealed significant fatty liver accumulation QTL that were specifically, mapped in females. Two significant QTLs on chromosomes 17 and 18, with genomic intervals 3 and 2 Mb, respectively, were mapped. A third QTL, with a less significant P value, was mapped to chromosome 4, with genomic interval of 2 Mb. These QTLs were named Flal1-Flal3, referring to Fatty Liver Accumulation Locus 1-3, for the QTLs on chromosomes 17, 18, and 4, respectively. Unfortunately, no QTL was mapped with males. Searching the mouse genome database suggested several candidate genes involved in hepatic fat accumulation. Our results show that susceptibility to hepatic fat accumulations is a complex trait, controlled by multiple genetic factors in female mice, but not in male.

Two bottlenecks impeding the genetic analysis of complex traits in rodents are access to mapping populations able to deliver gene-level mapping resolution and the need for population-specific genotyping arrays and haplotype reference panels. Here we combine low-coverage (0.15×) sequencing with a new method to impute the ancestral haplotype space in 1,887 commercially available outbred mice. We mapped 156 unique quantitative trait loci for 92 phenotypes at a 5% false discovery rate. Gene-level mapping resolution was achieved at about one-fifth of the loci, implicating Unc13c and Pgc1a at loci for the quality of sleep, Adarb2 for home cage activity, Rtkn2 for intensity of reaction to startle, Bmp2 for wound healing, Il15 and Id2 for several T cell measures and Prkca for bone mineral content. These findings have implications for diverse areas of mammalian biology and demonstrate how genome-wide association studies can be extended via low-coverage sequencing to species with highly recombinant outbred populations.

Inexpensive genotyping methods are essential to modern genomics. Here we present QUILT, which performs diploid genotype imputation using low-coverage whole-genome sequence data. QUILT employs Gibbs sampling to partition reads into maternal and paternal sets, facilitating rapid haploid imputation using large reference panels. We show this partitioning to be accurate over many megabases, enabling highly accurate imputation close to theoretical limits and outperforming existing methods. Moreover, QUILT can impute accurately using diverse technologies, including long reads from Oxford Nanopore Technologies, and a new form of low-cost barcoded Illumina sequencing called haplotagging, with the latter showing improved accuracy at low coverages. Relative to DNA genotyping microarrays, QUILT offers improved accuracy at reduced cost, particularly for diverse populations that are traditionally underserved in modern genomic analyses, with accuracy nearly doubling at rare SNPs. Finally, QUILT can accurately impute (four-digit) human leukocyte antigen types, the first such method from low-coverage sequence data.

Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.

The most commonly employed mammalian model organism is the laboratory mouse. A wide variety of genetically diverse inbred mouse strains, representing distinct physiological states, disease susceptibilities, and biological mechanisms have been developed over the last century. We report full length draft de novo genome assemblies for 16 of the most widely used inbred strains and reveal for the first time extensive strain-specific haplotype variation. We identify and characterise 2,567 regions on the current Genome Reference Consortium mouse reference genome exhibiting the greatest sequence diversity between strains. These regions are enriched for genes involved in defence and immunity, and exhibit enrichment of transposable elements and signatures of recent retrotransposition events. Combinations of alleles and genes unique to an individual strain are commonly observed at these loci, reflecting distinct strain phenotypes. Several immune related loci, some in previously identified QTLs for disease response have novel haplotypes not present in the reference that may explain the phenotype. We used these genomes to improve the mouse reference genome resulting in the completion of 10 new gene structures, and 62 new coding loci were added to the reference genome annotation. Notably this high quality collection of genomes revealed a previously unannotated gene (Efcab3-like) encoding 5,874 amino acids, one of the largest known in the rodent lineage. Interestingly, Efcab3-like−/− mice exhibit severe size anomalies in four regions of the brain suggesting a mechanism of Efcab3-like regulating brain development.

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.

Altres ajuts: This work was partially supported by the Wellcome Trust core grant 090532/Z/09/Z (to C.M, P.H.P, J.F, R.M.), and by grants MINECO, DGR and "ICREA-Academia2013" (to A.F-T). In this study we investigate the effects of parent of origin on complex traits in the laboratory rat, with a focus on coping style behavior in stressful situations. We develop theory, based on earlier work, to partition heritability into a component due to a combination of parent of origin, maternal, paternal and shared environment, and another component that estimates classical additive genetic variance. We use this theory to investigate the effects on heritability of the parental origin of alleles in 798 outbred heterogeneous stock rats across 199 complex traits. Parent-of-origin-like heritability was on average 2.7fold larger than classical additive heritability. Among the phenotypes with the most enhanced parent-of-origin heritability were 10 coping style behaviors, with average 3.2 fold heritability enrichment. To confirm these findings on coping behavior, and to eliminate the possibility that the parent of origin effects are due to confounding with shared environment, we performed a reciprocal F1 cross between the behaviorally divergent RHA and RLA rat strains. We observed parent-of-origin effects on F1 rat anxiety/coping-related behavior in the Elevated Zero Maze test. Our study is the first to assess genetic parent-of-origin effects in rats, and confirm earlier findings in mice that such effects influence coping and impulsive behavior, and suggest these effects might be significant in other mammals, including humans.

Major depressive disorder (MDD), one of the most frequently encountered forms of mental illness and a leading cause of disability worldwide, poses a major challenge to genetic analysis. To date, no robustly replicated genetic loci have been identified, despite analysis of more than 9,000 cases. Here, using low-coverage whole-genome sequencing of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, we identified, and subsequently replicated in an independent sample, two loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene (P = 2.53 × 10(-10)), the other in an intron of the LHPP gene (P = 6.45 × 10(-12)). Analysis of 4,509 cases with a severe subtype of MDD, melancholia, yielded an increased genetic signal at the SIRT1 locus. We attribute our success to the recruitment of relatively homogeneous cases with severe illness.

Tameness is a major behavioral factor for domestication, and can be divided into two potential components: motivation to approach humans (active tameness) and reluctance to avoid humans (passive tameness). We identified genetic loci for active tameness through selective breeding, selection mapping, and association analysis. In previous work using laboratory and wild mouse strains, we found that laboratory strains were predominantly selected for passive tameness but not active tameness during their domestication. To identify genetic regions associated with active tameness, we applied selective breeding over 9 generations for contacting, a behavioural parameter strongly associated with active tameness. The prerequisite for successful selective breeding is high genetic variation in the target population, so we established and used a novel resource, wild-derived heterogeneous stock (WHS) mice from eight wild strains. The mice had genetic variations not present in other outbred mouse populations. Selective breeding of the WHS mice increased the contacting level through the generations. Selection mapping was applied to the selected population using a simulation based on a non-selection model and inferred haplotype data derived from single-nucleotide polymorphisms. We found a genomic signature for selection on chromosome 11 containing two closely linked loci.

Background Ribosomal RNA (rRNA) accounts for the majority of the RNA in eukaryotic cells, and is encoded by hundreds to thousands of nearly identical gene copies, only a subset of which are active at any given time. In Arabidopsis thaliana, 45S rRNA genes are found in two large ribosomal DNA (rDNA) clusters and little is known about the contribution of each to the overall transcription pattern in the species. Results By taking advantage of genome sequencing data from the 1001 Genomes Consortium, we characterize rRNA gene sequence variation within and among accessions. Notably, variation is not restricted to the pre-rRNA sequences removed during processing, but it is also present within the highly conserved ribosomal subunits. Through linkage mapping we assign these variants to a particular rDNA cluster unambiguously and use them as reporters of rDNA cluster-specific expression. We demonstrate that rDNA cluster-usage varies greatly among accessions and that rDNA cluster-specific expression and silencing is controlled via genetic interactions between entire rDNA cluster haplotypes (alleles). Conclusions We show that rRNA gene cluster expression is controlled via complex epistatic and allelic interactions between rDNA haplotypes that apparently regulate the entire rRNA gene cluster. Furthermore, the sequence polymorphism we discovered implies that the pool of rRNA in a cell may be heterogeneous, which could have functional consequences. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1209-z) contains supplementary material, which is available to authorized users.

The China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE) project on Major Depressive Disorder (MDD) sequenced 11,670 female Han Chinese at low-coverage (1.7X), providing the first large-scale whole genome sequencing resource representative of the largest ethnic group in the world. Samples are collected from 58 hospitals from 23 provinces around China. We are able to call 22 million high quality single nucleotide polymorphisms (SNP) from the nuclear genome, representing the largest SNP call set from an East Asian population to date. We use these variants for imputation of genotypes across all samples, and this has allowed us to perform a successful genome wide association study (GWAS) on MDD. The utility of these data can be extended to studies of genetic ancestry in the Han Chinese and evolutionary genetics when integrated with data from other populations. Molecular phenotypes, such as copy number variations and structural variations can be detected, quantified and analysed in similar ways.

Most biological traits of human importance are complex in nature; their manifestation controlled by the cumulative effect of many genetic factors interacting with one another and with the individual's life history. Because of this, mouse genetic reference populations (GRPs) consisting of collections of inbred lines or recombinant inbred lines (RIL) derived from crosses between inbred lines are of particular value in analysis of complex traits, since massive amounts of data can be accumulated on the individual lines. However, existing mouse GRPs are derived from inbred lines that share a common history, resulting in limited genetic diversity, and reduced mapping precision due to long-range gametic disequilibrium. To overcome these limitations, the Collaborative Cross (CC) a genetically highly diverse collection of mouse RIL was established. The CC, now in advanced stages of development, will eventually consist of about 500 RIL derived from reciprocal crosses of eight divergent founder strains of mice, including three wild subspecies. Previous studies have shown that the CC indeed contains enormous diversity at the DNA level, that founder haplotypes are inherited in expected frequency, and that long-range gametic disequilibrium is not present. We here present data, primarily from our own laboratory, documenting extensive genetic variation among CC lines as expressed in broad-sense heritability (H2) and by the well-known "coefficient of genetic variation," demonstrating the ability of the CC resource to provide unprecedented mapping precision leading to identification of strong candidate genes. © 2014 Springer Science+Business Media.

Osteoporosis, the most common bone disease, is characterized by a low bone mass and increased risk of fractures. Importantly, individuals with the same bone mineral density (BMD), as measured on two dimensional (2D) radiographs, have different risks for fracture, suggesting that microstructural architecture is an important determinant of skeletal strength. Here we took advantage of the rich phenotypic and genetic diversity of the Collaborative Cross (CC) mice. Using microcomputed tomography, we examined key structural parameters in the femoral cortical and trabecular compartments of male and female mice from 34 CC lines. These traits included the trabecular bone volume fraction, number, thickness, connectivity, and spacing, as well as structural morphometric index. In the mid-diaphyseal cortex, we recorded cortical thickness and volumetric BMD.The broad-sense heritability of these traits ranged between 50 to 60%. We conducted a genome-wide association study to unravel 5 quantitative trait loci (QTL) significantly associated with 6 of the traits. We refined each locus by combining information obtained from the known ancestry of the mice and RNA-Seq data from publicly available sources, to shortlist potential candidate genes. We found strong evidence for new candidate genes, includingRhbdf2, which association to trabecular bone volume fraction and number was strongly suggested by our analyses. We then examined knockout mice, and validated the causal action ofRhbdf2on bone mass accrual and microarchitecture.Our approach revealed new genome-wide QTLs and a series of genes that have never been associated with bone microarchitecture. This study demonstrates for the first time the skeletal role ofRhbdf2on the physiological remodeling of both the cortical and trabecular bone. This newly assigned function forRhbdf2can prove useful in deciphering the predisposing factors of osteoporosis and propose new investigative avenues toward targeted therapeutic solutions.Author summaryIn this study, we used the novel mouse reference population, the Collaborative Cross (CC), to identify new causal genes in the regulation of bone microarchitecture, a critical determinant of bone strength. This approach provides a clear advantage in terms of resolution and dimensionality of the morphometric features (versus humans) and rich allelic diversity (versus classical mouse populations), over current practices of bone-related genome-wide association studies.Our genome-wide study revealed 5 loci significantly associated with microstructural traits in the cortical and trabecular bone. We found strong evidence for new candidate genes, in particular,Rhbdf2.We then validated the specific role ofRhbdf2on bone mass accrual and microarchitecture using knockout mice. Importantly, this study is the first demonstration of a physiological role forRhbdf2.

Heterogeneous Stock (HS) populations allow for fine-resolution genetic mapping of a variety of complex traits. HS mice and rats were created from breeding together eight inbred strains, followed by maintaining the colony in a manner that minimizes inbreeding. After 50 or more generations of breeding, the resulting animals' chromosomes represent a genetic mosaic of the founders' haplotypes, with the average distance between recombination events in the centiMorgan range. This allows for genetic mapping to only a few Mb, a much smaller region than what can be identified using traditional F2 intercross or backcross mapping strategies. HS animals have been used to fine-map a variety of complex traits including anxiety and fear behaviors, diabetes, asthma, and heart disease, among others. Once a quantitative trait locus (QTL) has been identified, founder sequence and expression analysis can be used to identify underlying causal genes. In the following review, we provide an overview of how HS rats and mice have been used to identify genetic loci, and in some cases the causal genes, underlying complex traits. We discuss the creation and breeding strategies for both HS rats and mice. We then discuss the statistical analyses used to identify genetic loci, as well as strategies to identify causal genes underlying these loci. We end the chapter by discussing limitations faced when using HS populations, including several statistical challenges that have not been fully resolved.

Following publication of this article, it came to the author’s attention that the there was some funding information missing from the acknowledgements section. The updated funding information has been included in the original article and can be seen below This study was also supported by a Project Grant from the Israel Cancer Research Fund (ICRF) and a grant from Israeli Science Foundations (ISF) number (961/15).

Structural Rearrangements can have unexpected effects on quantitative phenotypes. Surprisingly, these rearrangements can also be considered as..., To understand the population genetics of structural variants and their effects on phenotypes, we developed an approach to mapping structural variants that segregate in a population sequenced at low coverage. We avoid calling structural variants directly. Instead, the evidence for a potential structural variant at a locus is indicated by variation in the counts of short-reads that map anomalously to that locus. These structural variant traits are treated as quantitative traits and mapped genetically, analogously to a gene expression study. Association between a structural variant trait at one locus, and genotypes at a distant locus indicate the origin and target of a transposition. Using ultra-low-coverage (0.3×) population sequence data from 488 recombinant inbred Arabidopsis thaliana genomes, we identified 6502 segregating structural variants. Remarkably, 25% of these were transpositions. While many structural variants cannot be delineated precisely, we validated 83% of 44 predicted transposition breakpoints by polymerase chain reaction. We show that specific structural variants may be causative for quantitative trait loci for germination and resistance to infection by the fungus Albugo laibachii, isolate Nc14. Further we show that the phenotypic heritability attributable to read-mapping anomalies differs from, and, in the case of time to germination and bolting, exceeds that due to standard genetic variation. Genes within structural variants are also more likely to be silenced or dysregulated. This approach complements the prevalent strategy of structural variant discovery in fewer individuals sequenced at high coverage. It is generally applicable to large populations sequenced at low-coverage, and is particularly suited to mapping transpositions.

Summary Arabidopsis thaliana serves as a model organism for the study of fundamental physiological, cellular, and molecular processes. It has also greatly advanced our understanding of intraspecific genome variation. We present a detailed map of variation in 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and recently colonized North America. We identify relict populations that continue to inhabit ancestral habitats, primarily in the Iberian Peninsula. They have mixed with a lineage that has spread to northern latitudes from an unknown glacial refugium and is now found in a much broader spectrum of habitats. Insights into the history of the species and the fine-scale distribution of genetic diversity provide the basis for full exploitation of A. thaliana natural variation through integration of genomes and epigenomes with molecular and non-molecular phenotypes., Graphical Abstract, Highlights • The genomes of 1,135 naturally inbred lines of Arabidopsis thaliana are presented • Relict populations that continue to inhabit ancestral habitats were discovered • The last glacial maximum was important in structuring the distribution of relicts • This collection will connect genotypes and phenotypes on a species-wide level, Genomic sequencing analysis of over 1,000 natural inbred lines of Arabidopsis thaliana reveals its global population structure, migration patterns, and evolutionary history and provides a rich genetic resource for studying phenotypic variation and adaptation.

Sodium (Na) is ubiquitous in soils, and is transported to plant shoots via transpiration through xylem elements in the vascular tissue. However, excess Na is damaging. Accordingly, control of xylem-sap Na concentration is important for maintenance of shoot Na homeostasis, especially under Na stress conditions. Here we report that shoot Na homeostasis of Arabidopsis thaliana plants grown in saline soils is conferred by reactive oxygen species (ROS) regulation of xylem-sap Na concentrations. We show that lack of A. thaliana respiratory burst oxidase protein F (AtrbohF; an NADPH oxidase catalysing ROS production) causes hypersensitivity of shoots to soil salinity. Lack of AtrbohF-dependent salinity-induced vascular ROS accumulation leads to increased Na concentrations in root vasculature cells and in xylem sap, thus causing delivery of damaging amounts of Na to the shoot. We also show that the excess shoot Na delivery caused by lack of AtrbohF is dependent upon transpiration. We conclude that AtrbohF increases ROS levels in wild-type root vasculature in response to raised soil salinity, thereby limiting Na concentrations in xylem sap, and in turn protecting shoot cells from transpiration-dependent delivery of excess Na.

Ionizing radiation has long been known to induce heritable mutagenic change in DNA sequence. However, the genome-wide effect of radiation is not well understood. Here we report the molecular properties and frequency of mutations in phenotypically selected mutant lines isolated following exposure of the genetic model flowering plant Arabidopsis thaliana to fast neutrons (FNs). Previous studies suggested that FNs predominantly induce deletions longer than a kilobase in A. thaliana. However, we found a higher frequency of single base substitution than deletion mutations. While the overall frequency and molecular spectrum of fast-neutron (FN)–induced single base substitutions differed substantially from those of “background” mutations arising spontaneously in laboratory-grown plants, G:C>A:T transitions were favored in both. We found that FN-induced G:C>A:T transitions were concentrated at pyrimidine dinucleotide sites, suggesting that FNs promote the formation of mutational covalent linkages between adjacent pyrimidine residues. In addition, we found that FNs induced more single base than large deletions, and that these single base deletions were possibly caused by replication slippage. Our observations provide an initial picture of the genome-wide molecular profile of mutations induced in A. thaliana by FN irradiation and are particularly informative of the nature and extent of genome-wide mutation in lines selected on the basis of mutant phenotypes from FN-mutagenized A. thaliana populations.

International audience; We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism.

Summary Multicellular organisms can be regenerated from totipotent differentiated somatic cell or nuclear founders [1–3]. Organisms regenerated from clonally related isogenic founders might a priori have been expected to be phenotypically invariant. However, clonal regenerant animals display variant phenotypes caused by defective epigenetic reprogramming of gene expression [2], and clonal regenerant plants exhibit poorly understood heritable phenotypic (“somaclonal”) variation [4–7]. Here we show that somaclonal variation in regenerant Arabidopsis lineages is associated with genome-wide elevation in DNA sequence mutation rate. We also show that regenerant mutations comprise a distinctive molecular spectrum of base substitutions, insertions, and deletions that probably results from decreased DNA repair fidelity. Finally, we show that while regenerant base substitutions are a likely major genetic cause of the somaclonal variation of regenerant Arabidopsis lineages, transposon movement is unlikely to contribute substantially to that variation. We conclude that the phenotypic variation of regenerant plants, unlike that of regenerant animals, is substantially due to DNA sequence mutation., Highlights ► Regenerant Arabidopsis lineages display heritable phenotypic variation ► Regenerant Arabidopsis lineages display elevated genome-wide DNA sequence mutation ► Regenerant DNA sequence mutations comprise a distinct molecular spectrum ► Regenerant base substitution mutations confer heritable phenotypic variation

USP7 (HAUSP) is a deubiquitinating enzyme, which plays a crucial role in regulating the levels of the p53 tumour suppressor protein, through its ability to prevent the proteasomal degradation of the Ubiquitin ligase for p53, Hdm2. Supporting evidence suggests that an inhibitor of USP7 would act to abrogate the action of Hdm2, and thereby elevate levels of the p53 protein, with associated therapeutic benefits in cancer and potentially other diseases. In this article, we describe the characterisation of differential enzyme activity of both the full length and putative catalytic domain of human USP7 expressed in both bacterial and insect cell expression systems. We also demonstrate the way in which variations in the reducing environment surrounding the enzyme can dramatically affect both the stability of the enzyme and the range of small molecules able to inhibit the catalytic activity of the enzyme. Furthermore, we describe the validation and use of this assay for a high-throughput screening approach, again highlighting the critical nature of the enzyme's environment. Taken together, these findings not only increase our understanding of the enzymatic activity of deubiquitinating enzymes, but also highlight several key considerations of importance in the development of therapeutic agents against this novel class of therapeutic targets.

Short insertions, deletions (INDELs) and larger structural variants have been increasingly employed in genetic association studies, but few improvements over SNP-based association have been reported. In order to understand why this might be the case, we analysed two publicly available datasets and observed that 63% of INDELs called in A. thaliana and 64% in D. melanogaster populations are misrepresented as multiple alleles with different functional annotations, i.e. where the same underlying variant is represented by inconsistent alignments leading to different variant calls. To address this issue, we have developed the software Irisas to reclassify and re-annotate these variants, which we then used for single-locus tests of association. We also integrated them to predict the functional impact of SNPs, INDELs, and structural variants for burden testing. Using both approaches, we re-analysed the genetic architecture of complex traits in A. thaliana and D. melanogaster. Heritability analysis using SNPs alone explained on average 27% and 19% of phenotypic variance for A. thaliana and D. melanogaster respectively. Our method explained an additional 11% and 3%, respectively. We also identified novel trait loci that previous SNP-based association studies failed to map, and which contain established candidate genes. Our study shows the value of the association test with INDELs and integrating multiple types of variants in association studies in plants and animals., Author summary In this study, we develop a method for testing multiple types of variants in genome-wide association studies. We show that a multi-allelic artefact caused by inconsistent alignments was a key obstacle for testing association of insertion and deletion polymorphisms (INDELs) and for integrated association methods, such as burden testing. To address this problem, we developed the software Irisas that synchronizes variants and integrates the impact of SNPs, INDELs and structural variants for burden testing. We re-analysed two publicly available datasets with multiple traits in A. thaliana and D. melanogaster. We identified novel loci that contain well-established candidate genes that SNP-based GWAS failed to detect; INDEL-specific QTLs generally have weak local linkage disequilibrium (LD) with nearby SNPs. We showed by simulation that multiple independent loss-of-function common-allele SNPs/INDELs were challenging for direct association tests but demonstrated that they could be recovered by integrated burden testing.

The design and analysis of a bridge system is a complicated task. In order to simplify the design process, it is necessary to break the system into smaller, less complex, and more manageable subsystems. The AASHTO load-and-resistance factor design (LRFD) bridge design specifications provide the design methods and procedures to design the bridge components explicitly and on an individual basis. In particular, the AASHTO LRFD bridge design specifications employs the beam-line method or distribution factor method to reduce the spatial dimensionality of bridge structures into line elements. The distribution factor is used to modify the load effects determined from the beam-line analysis to account for the effects at other locations in the structure. The published vehicular design loads adopted by the AASHTO LRFD bridge design specifications are considered to be an “average design truck.” The average design truck loads exclude vehicles that are above the legal weight limits for the United States, but are regularly allowed to operate on the U.S. highway systems. These loads are short-haul vehicles such as solid waste trucks, aerial rescue fire trucks and concrete mixers. The most common type of bridge in the United States is the slab-girder bridge system. The principal function of the slab-girder bridge is to provide a roadway surface and transmit the applied loads of the roadway to the girder system supporting the roadway. This research examines the loading effects of the short-haul vehicular loads on a typical prestressed concrete open box beam (U-beam) bridge. This study consists of analytically determining the distribution factors of special vehicles such as, a concrete mixer, a solid waste truck, and an aerial fire rescue truck, and comparing the results to distribution factors recommended in the AASHTO LRFD bridge design specifications.