A method for fine mapping quantitative trait loci in outbred animal stocks

The MexTAg collaborative cross: host genetics affects asbestos related disease latency, but has little influence once tumours develop

Objectives: This study combines two innovative mouse models in a major gene discovery project to assess the influence of host genetics on asbestos related disease (ARD). Conventional genetics studies provided evidence that some susceptibility to mesothelioma is genetic. However, the identification of host modifier genes, the roles they may play, and whether they contribute to disease susceptibility remain unknown. Here we report a study designed to rapidly identify genes associated with mesothelioma susceptibility by combining the Collaborative Cross (CC) resource with the well-characterised MexTAg mesothelioma mouse model. Methods: The CC is a powerful mouse resource that harnesses over 90% of common genetic variation in the mouse species, allowing rapid identification of genes mediating complex traits. MexTAg mice rapidly, uniformly, and predictably develop mesothelioma, but only after asbestos exposure. To assess the influence of host genetics on ARD, we crossed 72 genetically distinct CC mouse strains with MexTAg mice and exposed the resulting CC-MexTAg (CCMT) progeny to asbestos and monitored them for traits including overall survival, the time to ARD onset (latency), the time between ARD onset and euthanasia (disease progression) and ascites volume. We identified phenotype-specific modifier genes associated with these traits and we validated the role of human orthologues in asbestos-induced carcinogenesis using human mesothelioma datasets. Results: We generated 72 genetically distinct CCMT strains and exposed their progeny (2,562 in total) to asbestos. Reflecting the genetic diversity of the CC, there was considerable variation in overall survival and disease latency. Surprisingly, however, there was no variation in disease progression, demonstrating that host genetic factors do have a significant influence during disease latency but have a limited role once disease is established. Quantitative trait loci (QTL) affecting ARD survival/latency were identified on chromosomes 6, 12 and X. Of the 97-protein coding candidate modifier genes that spanned these QTL, eight genes (CPED1, ORS1, NDUFA1, HS1BP3, IL13RA1, LSM8, TES and TSPAN12) were found to significantly affect outcome in both CCMT and human mesothelioma datasets. Conclusion: Host genetic factors affect susceptibility to development of asbestos associated disease. However, following mesothelioma establishment, genetic variation in molecular or immunological mechanisms did not affect disease progression. Identification of multiple candidate modifier genes and their human homologues with known associations in other advanced stage or metastatic cancers highlights the complexity of ARD and may provide a pathway to identify novel therapeutic targets.

The Collaborative Cross-Mouse Population for Studying Genetic Determinants Underlying Alveolar Bone Loss Due to Polymicrobial Synergy and Dysbiosis

Dysbiosis of oral microbiota is associated with the initiation and progression of periodontitis. The cause-and-effect relationship between genetics, periodontitis, and oral microbiome dysbiosis is poorly understood. Here, we demonstrate the power of the collaborative cross (CC) mice model to assess the effect of the genetic background on microbiome diversity shifts during periodontal infection and host suitability status. We examined the bacterial composition in plaque samples from seven different CC lines using 16s rRNA sequencing before and during periodontal infection. The susceptibility/resistance of the CC lines to alveolar bone loss was determined using the micro-CT technique. A total of 53 samples (7 lines) were collected before and after oral infection using oral swaps followed by DNA extraction and 16 s rRNA sequencing analysis. CC lines showed a significant variation in response to the co-infection (p < 0.05). Microbiome compositions were significantly different before and after infection and between resistant and susceptible lines to periodontitis (p < 0.05). Gram-positive taxa were significantly higher at the resistant lines compared to susceptible lines (p < 0.05). Gram-positive bacteria were reduced after infection, and gram-negative bacteria, specifically anaerobic groups, increased after infection. Our results demonstrate the utility of the CC mice in exploring the interrelationship between genetic background, microbiome composition, and periodontitis.

Uncovering the genetic architecture of parallel evolution

Identifying the genetic architecture underlying adaptive traits is exceptionally challenging in natural populations. This is because associations between traits not only mask the targets of selection but also create correlated patterns of genomic divergence that hinder our ability to isolate causal genetic effects. Here, we examine the repeated evolution of components of the auxin pathway that have contributed to the replicated loss of gravitropism (i.e. the ability of a plant to bend in response to gravity) in multiple populations of the Senecio lautus species complex in Australia. We use a powerful approach which combines parallel population genomics with association mapping in a Multiparent Advanced Generation Inter-Cross (MAGIC) population to break down genetic and trait correlations to reveal how adaptive traits evolve during replicated evolution. We sequenced auxin and shoot gravitropism-related gene regions in 80 individuals from six natural populations (three parallel divergence events) and 133 individuals from a MAGIC population derived from two of the recently diverged natural populations. We show that artificial tail selection on gravitropism in the MAGIC population recreates patterns of parallel divergence in the auxin pathway in the natural populations. We reveal a set of 55 auxin gene regions that have evolved repeatedly during the evolution of the species, of which 50 are directly associated with gravitropism divergence in the MAGIC population. Our work creates a strong link between patterns of genomic divergence and trait variation contributing to replicated evolution by natural selection, paving the way to understand the origin and maintenance of adaptations in natural populations.

A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations

Although multi-parent populations (MPPs) integrate the advantages of linkage and association mapping populations in the genetic dissection of complex traits and especially combine genetic analysis with crop breeding, it is difficult to detect small-effect quantitative trait loci (QTL) for complex traits in multiparent advanced generation intercross (MAGIC), nested association mapping (NAM), and random-open-parent association mapping (ROAM) populations. To address this issue, here we proposed a multi-locus linear mixed model method, namely mppQTL, to detect QTLs, especially small-effect QTLs, in these MPPs. The new method includes two steps. The first is genome-wide scanning based on a single-locus linear mixed model; the P-values are obtained from likelihood-ratio test, the peaks of negative logarithm P-value curve are selected by group-lasso, and all the selected peaks are regarded as potential QTLs. In the second step, all the potential QTLs are placed on a multi-locus linear mixed model, all the effects are estimated using expectation-maximization empirical Bayes algorithm, and all the non-zero effect vectors are further evaluated via likelihood-ratio test for significant QTLs. In Monte Carlo simulation studies, the new method has higher power in QTL detection, lower false positive rate, lower mean absolute deviation for QTL position estimate, and lower mean squared error for the estimate of QTL size (r²) than existing methods because the new method increases the power of detecting small-effect QTLs. In real dataset analysis, the new method (19) identified five more known genes than the existing three methods (14). This study provides an effective method for detecting small-effect QTLs in any MPPs.

Impact of diet and host genetics on the murine intestinal mycobiome

The mammalian gut is home to a diverse microbial ecosystem, whose composition affects various physiological traits of the host. Next-generation sequencing-based metagenomic approaches demonstrated how the interplay of host genetics, bacteria, and environmental factors shape complex traits and clinical outcomes. However, the role of fungi in these complex interactions remains understudied. Here, using 228 males and 363 females from an advanced-intercross mouse line, we provide evidence that fungi are regulated by host genetics. In addition, we map quantitative trait loci associated with various fungal species to single genes in mice using whole genome sequencing and genotyping. Moreover, we show that diet and its' interaction with host genetics alter the composition of fungi in outbred mice, and identify fungal indicator species associated with different dietary regimes. Collectively, in this work, we uncover an association of the intestinal fungal community with host genetics and a regulatory role of diet in this ecological niche.

Hunting for Genes Underlying Emotionality in the Laboratory Rat: Maps, Tools and Traps

Scientists have systematically investigated the hereditary bases of behaviors since the 19th century, moved by either evolutionary questions or clinically-motivated purposes. The pioneer studies on the genetic selection of laboratory animals had already indicated, one hundred years ago, the immense complexity of analyzing behaviors that were influenced by a large number of small-effect genes and an incalculable amount of environmental factors. Merging Mendelian, quantitative and molecular approaches in the 1990s made it possible to map specific rodent behaviors to known chromosome regions. From that point on, Quantitative Trait Locus (QTL) analyses coupled with behavioral and molecular techniques, which involved in vivo isolation of relevant blocks of genes, opened new avenues for gene mapping and characterization. This review examines the QTL strategy applied to the behavioral study of emotionality, with a focus on the laboratory rat. We discuss the challenges, advances and limitations of the search for Quantitative Trait Genes (QTG) playing a role in regulating emotionality. For the past 25 years, we have marched the long journey from emotionality-related behaviors to genes. In this context, our experiences are used to illustrate why and how one should move forward in the molecular understanding of complex psychiatric illnesses. The promise of exploring genetic links between immunological and emotional responses are also discussed. New strategies based on humans, rodents and other animals (such as zebrafish) are also acknowledged, as they are likely to allow substantial progress to be made in the near future.

The utility of a closed breeding colony of Peromyscus leucopus for dissecting complex traits

Deermice of the genus Peromyscus are well suited for addressing several questions of biologist interest, including the genetic bases of longevity, behavior, physiology, adaptation, and their ability to serve as disease vectors. Here, we explore a diversity outbred approach for dissecting complex traits in Peromyscus leucopus, a nontraditional genetic model system. We take advantage of a closed colony of deer-mice founded from 38 individuals and subsequently maintained for ∼40-60 generations. From 405 low-pass short-read sequenced deermice we accurate impute genotypes at 16 million single nucleotide polymorphisms. Conditional on observed genotypes simulations were conducted in which three different sized quantitative trait loci contribute to a complex trait under three different genetic models. Using a stringent significance threshold power was modest, largely a function of the percent variation attributable to the simulated quantitative trait loci, with the underlying genetic model having only a subtle impact. We additionally simulated 2,000 pseudo-individuals, whose genotypes were consistent with those observed in the genotyped cohort and carried out additional power simulations. In experiments employing more than 1,000 mice power is high to detect quantitative trait loci contributing greater than 2.5% to a complex trait, with a localization ability of ∼100 kb. We finally carried out a Genome-Wide Association Study on two demonstration traits, bleeding time and body weight, and uncovered one significant region. Our work suggests that complex traits can be dissected in founders-unknown P. leucopus colony mice and similar colonies in other systems using easily obtained genotypes from low-pass sequencing.

Genetic architecture of variation in Arabidopsis thaliana rosettes

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

A generic hidden Markov model for multiparent populations

A common step in the analysis of multiparent populations (MPPs) is genotype reconstruction: identifying the founder origin of haplotypes from dense marker data. This process often makes use of a probability model for the pattern of founder alleles along chromosomes, including the relative frequency of founder alleles and the probability of exchanges among them, which depend on a model for meiotic recombination and on the mating design for the population. While the precise experimental design used to generate the population may be used to derive a precise characterization of the model for exchanges among founder alleles, this can be tedious, particularly given the great variety of experimental designs that have been proposed. We describe an approximate model that can be applied for a variety of MPPs. We have implemented the approach in the R/qtl2 software, and we illustrate its use in applications to publicly available data on Diversity Outbred and Collaborative Cross mice.

Quantitative Trait Loci Mapping of Adult Plant and Seedling Resistance to Stripe Rust (Puccinia striiformis Westend.) in a Multiparent Advanced Generation Intercross Wheat Population

Stripe rust caused by the biotrophic fungus Puccinia striiformis Westend. is one of the most important diseases of wheat worldwide, causing high yield and quality losses. Growing resistant cultivars is the most efficient way to control stripe rust, both economically and ecologically. Known resistance genes are already present in numerous cultivars worldwide. However, their effectiveness is limited to certain races within a rust population and the emergence of stripe rust races being virulent against common resistance genes forces the demand for new sources of resistance. Multiparent advanced generation intercross (MAGIC) populations have proven to be a powerful tool to carry out genetic studies on economically important traits. In this study, interval mapping was performed to map quantitative trait loci (QTL) for stripe rust resistance in the Bavarian MAGIC wheat population, comprising 394 F6 : 8 recombinant inbred lines (RILs). Phenotypic evaluation of the RILs was carried out for adult plant resistance in field trials at three locations across three years and for seedling resistance in a growth chamber. In total, 21 QTL for stripe rust resistance corresponding to 13 distinct chromosomal regions were detected, of which two may represent putatively new QTL located on wheat chromosomes 3D and 7D.

Genome optimization via virtual simulation to accelerate maize hybrid breeding

The employment of doubled-haploid (DH) technology in maize has vastly accelerated the efficiency of developing inbred lines. The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented 'genome optimization via virtual simulation (GOVS)' using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' or 'advantageous alleles' in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize. Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https://govs-pack.github.io/) to the public may ultimately facilitate genomically designed breeding in maize.

Crafting for a better MAGIC: systematic design and test for Multiparental Advanced Generation Inter-Cross population

Multiparental Advanced Generation Inter-Cross (MAGIC) populations are valuable crop resources with a wide array of research uses including genetic mapping of complex traits, management of genetic resources and breeding of new varieties. Multiple founders are crossed to create a rich mosaic of highly recombined founder genomes in the MAGIC recombinant inbred lines (RILs). Many variations of MAGIC population designs exist; however, a large proportion of the currently available populations have been created empirically and based on similar designs. In our evaluations of five MAGIC populations, we found that the choice of designs has a large impact on the recombination landscape in the RILs. The most popular design used in many MAGIC populations has been shown to have a bias in recombinant haplotypes and low level of unique recombinant haplotypes, and therefore is not recommended. To address this problem and provide a remedy for the future, we have developed the “magicdesign” R package for creating and testing any MAGIC population design via simulation. A Shiny app version of the package is available as well. Our “magicdesign” package provides a unifying tool and a framework for creativity and innovation in MAGIC population designs. For example, using this package, we demonstrate that MAGIC population designs can be found which are very effective in creating haplotype diversity without the requirement for very large crossing programs. Furthermore, we show that interspersing cycles of crossing with cycles of selfing is effective in increasing haplotype diversity. These approaches are applicable in species that are hard to cross or in which resources are limited.

On the Use of Heterogeneous Stock Mice to Map Transcriptomes Associated With Excessive Ethanol Consumption

We and many others have noted the advantages of using heterogeneous (HS) animals to map genes and gene networks associated with both behavioral and non-behavioral phenotypes. Importantly, genetically complex Mus musculus crosses provide substantially increased resolution to examine old and new relationships between gene expression and behavior. Here we report on data obtained from two HS populations: the HS/NPT derived from eight inbred laboratory mouse strains and the HS-CC derived from the eight collaborative cross inbred mouse strains that includes three wild-derived strains. Our work has focused on the genes and gene networks associated with risk for excessive ethanol consumption, individual variation in ethanol consumption and the consequences, including escalation, of long-term ethanol consumption. Background data on the development of HS mice is provided, including advantages for the detection of expression quantitative trait loci. Examples are also provided of using HS animals to probe the genes associated with ethanol preference and binge ethanol consumption.

Haplotype reconstruction in connected tetraploid F1 populations

In diploid species, many multiparental populations have been developed to increase genetic diversity and quantitative trait loci (QTL) mapping resolution. In these populations, haplotype reconstruction has been used as a standard practice to increase the power of QTL detection in comparison with the marker-based association analysis. However, such software tools for polyploid species are few and limited to a single biparental F1 population. In this study, a statistical framework for haplotype reconstruction has been developed and implemented in the software PolyOrigin for connected tetraploid F1 populations with shared parents, regardless of the number of parents or mating design. Given a genetic or physical map of markers, PolyOrigin first phases parental genotypes, then refines the input marker map, and finally reconstructs offspring haplotypes. PolyOrigin can utilize single nucleotide polymorphism (SNP) data coming from arrays or from sequence-based genotyping; in the latter case, bi-allelic read counts can be used (and are preferred) as input data to minimize the influence of genotype calling errors at low depth. With extensive simulation we show that PolyOrigin is robust to the errors in the input genotypic data and marker map. It works well for various population designs with ≥30 offspring per parent and for sequences with read depth as low as 10x. PolyOrigin was further evaluated using an autotetraploid potato dataset with a 3 × 3 half-diallel mating design. In conclusion, PolyOrigin opens up exciting new possibilities for haplotype analysis in tetraploid breeding populations.

Follistatin mediates learning and synaptic plasticity via regulation of Asic4 expression in the hippocampus

Adult neurogenesis, which is known to be a heritable trait, is thought to be involved in learning, stress-related anxiety, and antidepressant action. In this study, we map genes influencing adult neurogenesis and identify a candidate gene, follistatin (Fst) for further study. By utilizing a brain-specific knockout and viral vector-mediated gene transfer, we reveal the importance of hippocampal FST in neurogenesis, learning, and synaptic plasticity. From RNA sequencing and chromatin immunoprecipitation experiments, we identify Asic4 as a critical downstream target gene regulated by FST. Our work demonstrates that FST functions in the hippocampus at least in part through regulating Asic4 expression. Overall, we illustrate the role of hippocampal Fst in learning and synaptic plasticity.

The biological mechanisms underpinning learning are unclear. Mounting evidence has suggested that adult hippocampal neurogenesis is involved although a causal relationship has not been well defined. Here, using high-resolution genetic mapping of adult neurogenesis, combined with sequencing information, we identify follistatin (Fst) and demonstrate its involvement in learning and adult neurogenesis. We confirmed that brain-specific Fst knockout (KO) mice exhibited decreased hippocampal neurogenesis and demonstrated that FST is critical for learning. Fst KO mice exhibit deficits in spatial learning, working memory, and long-term potentiation (LTP). In contrast, hippocampal overexpression of Fst in KO mice reversed these impairments. By utilizing RNA sequencing and chromatin immunoprecipitation, we identified Asic4 as a target gene regulated by FST and show that Asic4 plays a critical role in learning deficits caused by Fst deletion. Long-term overexpression of hippocampal Fst in C57BL/6 wild-type mice alleviates age-related decline in cognition, neurogenesis, and LTP. Collectively, our study reveals the functions for FST in adult neurogenesis and learning behaviors.

Genetic mapping of novel modifiers for ApcMin induced intestinal polyps' development using the genetic architecture power of the collaborative cross mice

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.

Accurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations

Genetic association studies seek to uncover the link between genotype and phenotype, and often utilize inbred reference panels as a replicable source of genetic variation. However, inbred reference panels can differ substantially from wild populations in their genotypic distribution, patterns of linkage-disequilibrium, and nucleotide diversity. As a result, associations discovered using inbred reference panels may not reflect the genetic basis of phenotypic variation in natural populations. To address this problem, we evaluated a mapping population design where dozens to hundreds of inbred lines are outbred for few generations, which we call the Hybrid Swarm. The Hybrid Swarm approach has likely remained underutilized relative to pre-sequenced inbred lines due to the costs of genome-wide genotyping. To reduce sequencing costs and make the Hybrid Swarm approach feasible, we developed a computational pipeline that reconstructs accurate whole genomes from ultra-low-coverage (0.05X) sequence data in Hybrid Swarm populations derived from ancestors with phased haplotypes. We evaluate reconstructions using genetic variation from the Drosophila Genetic Reference Panel as well as variation from neutral simulations. We compared the power and precision of Genome-Wide Association Studies using the Hybrid Swarm, inbred lines, recombinant inbred lines (RILs), and highly outbred populations across a range of allele frequencies, effect sizes, and genetic architectures. Our simulations show that these different mapping panels vary in their power and precision, largely depending on the architecture of the trait. The Hybrid Swam and RILs outperform inbred lines for quantitative traits, but not for monogenic ones. Taken together, our results demonstrate the feasibility of the Hybrid Swarm as a cost-effective method of fine-scale genetic mapping.

SPEARS: Standard Performance Evaluation of Ancestral haplotype Reconstruction through Simulation

MOTIVATION

Ancestral haplotype maps provide useful information about genomic variation and insights into biological processes. Reconstructing the descendent haplotype structure of homologous chromosomes, particularly for large numbers of individuals, can help with characterizing the recombination landscape, elucidating genotype-to-phenotype relationships, improving genomic predictions and more. Inferring haplotype maps from sparse genotype data is an efficient approach to whole-genome haplotyping, but this is a non-trivial problem. A standardized approach is needed to validate whether haplotype reconstruction software, conceived population designs and existing data for a given population provides accurate haplotype information for further inference.

RESULTS

We introduce SPEARS, a pipeline for the simulation-based appraisal of genome-wide haplotype maps constructed from sparse genotype data. Using a specified pedigree, the pipeline generates virtual genotypes (known data) with genotyping errors and missing data structure. It then proceeds to mimic analysis in practice, capturing sources of error due to genotyping, imputation and haplotype inference. Standard metrics allow researchers to assess different population designs and which features of haplotype structure or regions of the genome are sufficiently accurate for analysis. Haplotype maps for 1000 outcross progeny from a multi-parent population of maize are used to demonstrate SPEARS.

AVAILABILITYAND IMPLEMENTATION

SPEARS, the protocol and suite of scripts, are publicly available under an MIT license at GitHub (https://github.com/maizeatlas/spears).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics

Recent computational methods have enabled the inference of the cell-type-specificity of eQTLs based on bulk transcriptomes from highly heterogeneous tissues. However, these methods are limited in their scalability to highly heterogeneous tissues and limited in their broad applicability to any cell-type specificity of eQTLs. Here we present and demonstrate Cell Lineage Genetics (CeL-Gen), a novel computational approach that allows inference of eQTLs together with the subsets of cell types in which they have an effect, from bulk transcriptome data. To obtain improved scalability and broader applicability, CeL-Gen takes as input the known cell lineage tree and relies on the observation that dynamic changes in genetic effects occur relatively infrequently during cell differentiation. CeL-Gen can therefore be used not only to tease apart genetic effects derived from different cell types but also to infer the particular differentiation steps in which genetic effects are altered.

Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot

Much has been published on QTL detection for complex traits using bi-parental and multi-parental crosses (linkage analysis) or diversity panels (GWAS studies). While successful for detection, transferability of results to real applications has proven more difficult. Here, we combined a QTL detection approach using a pre-breeding populations which utilized intensive phenotypic selection for the target trait across multiple plant generations, combined with rapid generation turnover (i.e. "speed breeding") to allow cycling of multiple plant generations each year. The reasoning is that QTL mapping information would complement the selection process by identifying the genome regions under selection within the relevant germplasm. Questions to answer were the location of the genomic regions determining response to selection and the origin of the favourable alleles within the pedigree. We used data from a pre-breeding program that aimed at pyramiding different resistance sources to Fusarium crown rot into elite (but susceptible) wheat backgrounds. The population resulted from a complex backcrossing scheme involving multiple resistance donors and multiple elite backgrounds, akin to a MAGIC population (985 genotypes in total, with founders, and two major offspring layers within the pedigree). A significant increase in the resistance level was observed (i.e. a positive response to selection) after the selection process, and 17 regions significantly associated with that response were identified using a GWAS approach. Those regions included known QTL as well as potentially novel regions contributing resistance to Fusarium crown rot. In addition, we were able to trace back the sources of the favourable alleles for each QTL. We demonstrate that QTL detection using breeding populations under selection for the target trait can identify QTL controlling the target trait and that the frequency of the favourable alleles was increased as a response to selection, thereby validating the QTL detected. This is a valuable opportunistic approach that can provide QTL information that is more easily transferred to breeding applications.

Phenomics, genomics of oil palm (Elaeis guineensis Jacq.): way forward for making sustainable and high yielding quality oil palm

Oil palm (Elaeis guineensis Jacq.) is a heterogeneous, perennial crop having long breeding cycle with a genome size of 1.8 Gb. The demand for vegetable oil is steadily increasing, and expected that nearly 240-250 million tons of vegetable oil may be required by 2050. Genomics and next generation technologies plays crucial role in achieving the sustainable availability of oil palm with good yield and high quality. A successful breeding programme in oil palm depends on the availability of diverse gene pool, ex-situ conservation and their proper utilization for generating elite planting material. The major breeding methods adopted in oil palm are either modified recurrent selection or the modified reciprocal recurrent selection method. The QTLs of yield and related traits are chiefly located on chromosome 4, 10, 12 and 15 which is discussed in the current review. The probable chromosomal regions influencing the less height increment is observed to be on chromosomes 4, 10, 14 and 15. Advanced genomic approaches together with bioinformatics tools were discussed thoroughly for achieving sustainable oil palm where more efforts are needed. Major emphasis is given on oil palm crop improvement using holistic approaches of various genomic tools. Also a road map given on the milestones in the genomics and way forward for making oil palm to high yielding quality oil palm.

Ancestral haplotype reconstruction in endogamous populations using identity-by-descent

In this work we develop a novel algorithm for reconstructing the genomes of ancestral individuals, given genotype or sequence data from contemporary individuals and an extended pedigree of family relationships. A pedigree with complete genomes for every individual enables the study of allele frequency dynamics and haplotype diversity across generations, including deviations from neutrality such as transmission distortion. When studying heritable diseases, ancestral haplotypes can be used to augment genome-wide association studies and track disease inheritance patterns. The building blocks of our reconstruction algorithm are segments of Identity-By-Descent (IBD) shared between two or more genotyped individuals. The method alternates between identifying a source for each IBD segment and assembling IBD segments placed within each ancestral individual. Unlike previous approaches, our method is able to accommodate complex pedigree structures with hundreds of individuals genotyped at millions of SNPs.

We apply our method to an Old Order Amish pedigree from Lancaster, Pennsylvania, whose founders came to North America from Europe during the early 18th century. The pedigree includes 1338 individuals from the past 12 generations, 394 with genotype data. The motivation for reconstruction is to understand the genetic basis of diseases segregating in the family through tracking haplotype transmission over time. Using our algorithm thread, we are able to reconstruct an average of 224 ancestral individuals per chromosome. For these ancestral individuals, on average we reconstruct 79% of their haplotypes. We also identify a region on chromosome 16 that is difficult to reconstruct—we find that this region harbors a short Amish-specific copy number variation and the gene HYDIN. thread was developed for endogamous populations, but can be applied to any extensive pedigree with the recent generations genotyped. We anticipate that this type of practical ancestral reconstruction will become more common and necessary to understand rare and complex heritable diseases in extended families.

When analyzing complex heritable traits, genomic data from many generations of an extended family increases the amount of information available for statistical inference. However, typically only genomic data from the recent generations of a pedigree are available, as ancestral individuals are deceased. In this work we present an algorithm, called thread, for reconstructing the genomes of ancestral individuals, given a complex pedigree and genomic data from the recent generations. Previous approaches have not been able to accommodate large datasets (both in terms of sites and individuals), made simplifying assumptions about pedigree structure, or did not tie reconstructed sequences back to specific individuals. We apply thread to a complex Old Order Amish pedigree of 1338 individuals, 394 with genotype data.

Sensitivity to food and cocaine cues are independent traits in a large sample of heterogeneous stock rats

Sensitivity to cocaine and its associated stimuli ("cues") are important factors in the development and maintenance of addiction. Rodent studies suggest that this sensitivity is related, in part, to the propensity to attribute incentive salience to food cues, which, in turn, contributes to the maintenance of cocaine self-administration, and cue-induced relapse of drug-seeking. Whereas each of these traits has established links to drug use, the relatedness between the individual traits themselves has not been well characterized in preclinical models. To this end, the propensity to attribute incentive salience to a food cue was first assessed in two distinct cohorts of 2716 outbred heterogeneous stock rats (HS; formerly N:NIH). We then determined whether each cohort was associated with performance in one of two paradigms (cocaine conditioned cue preference and cocaine contextual conditioning). These measure the unconditioned locomotor effects of cocaine, as well as conditioned approach and the locomotor response to a cocaine-paired floor or context. There was large individual variability and sex differences among all traits, but they were largely independent of one another in both males and females. These findings suggest that these traits may contribute to drug-use via independent underlying neuropsychological processes.

Multiparental Population in Crops: Methods of Development and Dissection of Genetic Traits

Multiparental populations are located midway between association mapping that relies on germplasm collections and classic linkage analysis, based upon biparental populations. They provide several key advantages such as the possibility to include a higher number of alleles and increased level of recombination with respect to biparental populations, and more equilibrated allelic frequencies than association mapping panels. Moreover, in these populations new allele's combinations arise from recombination that may reveal transgressive phenotypes and make them a useful pre-breeding material. Here we describe the strategies for working with multiparental populations, focusing on nested association mapping populations (NAM) and multiparent advanced generation intercross populations (MAGIC). We provide details from the selection of founders, population development, and characterization to the statistical methods for genetic mapping and quantitative trait detection.

Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

Inferring the Allelic Series at QTL in Multiparental Populations

Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.

Container-aided integrative QTL and RNA-seq analysis of Collaborative Cross mice supports distinct sex-oriented molecular modes of response in obesity

BACKGROUND

The Collaborative Cross (CC) mouse population is a valuable resource to study the genetic basis of complex traits, such as obesity. Although the development of obesity is influenced by environmental factors, underlying genetic mechanisms play a crucial role in the response to these factors. The interplay between the genetic background and the gene expression pattern can provide further insight into this response, but we lack robust and easily reproducible workflows to integrate genomic and transcriptomic information in the CC mouse population.

RESULTS

We established an automated and reproducible integrative workflow to analyse complex traits in the CC mouse genetic reference panel at the genomic and transcriptomic levels. We implemented the analytical workflow to assess the underlying genetic mechanisms of host susceptibility to diet induced obesity and integrated these results with diet induced changes in the hepatic gene expression of susceptible and resistant mice. Hepatic gene expression differs significantly between obese and non-obese mice, with a significant sex effect, where male and female mice exhibit different responses and coping mechanisms.

CONCLUSION

Integration of the data showed that different genes but similar pathways are involved in the genetic susceptibility and disturbed in diet induced obesity. Genetic mechanisms underlying susceptibility to high-fat diet induced obesity are different in female and male mice. The clear distinction we observed in the systemic response to the high-fat diet challenge and to obesity between male and female mice points to the need for further research into distinct sex-related mechanisms in metabolic disease.

Ant Collective Behavior Is Heritable and Shaped by Selection

AbstractCollective behaviors are widespread in nature and usually assumed to be strongly shaped by natural selection. However, the degree to which variation in collective behavior is heritable and has fitness consequences-the two prerequisites for evolution by natural selection-is largely unknown. We used a new pharaoh ant (Monomorium pharaonis) mapping population to estimate the heritability, genetic correlations, and fitness consequences of three collective behaviors (foraging, aggression, and exploration), as well as of body size, sex ratio, and caste ratio. Heritability estimates for the collective behaviors were moderate, ranging from 0.17 to 0.32, but lower than our estimates for the heritability of caste ratio, sex ratio, and body size of new workers, queens, and males. Moreover, variation in collective behaviors among colonies was phenotypically correlated, suggesting that selection may shape multiple colony collective behaviors simultaneously. Finally, we found evidence for directional selection that was similar in strength to estimates of selection in natural populations. Altogether, our study begins to elucidate the genetic architecture of collective behavior and is one of the first studies to demonstrate that it is shaped by selection.

The Dawn of the Age of Multi-Parent MAGIC Populations in Plant Breeding: Novel Powerful Next-Generation Resources for Genetic Analysis and Selection of Recombinant Elite Material

The compelling need to increase global agricultural production requires new breeding approaches that facilitate exploiting the diversity available in the plant genetic resources. Multi-parent advanced generation inter-cross (MAGIC) populations are large sets of recombinant inbred lines (RILs) that are a genetic mosaic of multiple founder parents. MAGIC populations display emerging features over experimental bi-parental and germplasm populations in combining significant levels of genetic recombination, a lack of genetic structure, and high genetic and phenotypic diversity. The development of MAGIC populations can be performed using “funnel” or “diallel” cross-designs, which are of great relevance choosing appropriate parents and defining optimal population sizes. Significant advances in specific software development are facilitating the genetic analysis of the complex genetic constitutions of MAGIC populations. Despite the complexity and the resources required in their development, due to their potential and interest for breeding, the number of MAGIC populations available and under development is continuously growing, with 45 MAGIC populations in different crops being reported here. Though cereals are by far the crop group where more MAGIC populations have been developed, MAGIC populations have also started to become available in other crop groups. The results obtained so far demonstrate that MAGIC populations are a very powerful tool for the dissection of complex traits, as well as a resource for the selection of recombinant elite breeding material and cultivars. In addition, some new MAGIC approaches that can make significant contributions to breeding, such as the development of inter-specific MAGIC populations, the development of MAGIC-like populations in crops where pure lines are not available, and the establishment of strategies for the straightforward incorporation of MAGIC materials in breeding pipelines, have barely been explored. The evidence that is already available indicates that MAGIC populations will play a major role in the coming years in allowing for impressive gains in plant breeding for developing new generations of dramatically improved cultivars.

Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.

Impact of diet and genes on murine autoimmune pancreatitis

The impact of environmental factors, such as diet, and the genetic basis of autoimmune pancreatitis (AIP) are largely unknown. Here, we used an experimental murine AIP model to identify the contribution of diet to AIP development, as well as to fine-map AIP-associated genes in outbred mice prone to develop the disease. For this purpose, we fed mice of an autoimmune-prone intercross line (AIL) three different diets (control, calorie-reduced and western diet) for 6 months, at which point the mice were genotyped and phenotyped for AIP. Overall, 269 out of 734 mice (36.6%) developed AIP with signs of parenchymal destruction, equally affecting mice of both sexes. AIP prevalence and severity were reduced by approximately 50% in mice held under caloric restriction compared to those fed control or western diet. We identified a quantitative trait locus (QTL) on chromosome 4 to be associated with AIP, which is located within a previously reported QTL. This association does not change when considering diet or sex as an additional variable for the mapping. Using whole-genome sequences of the AIL founder strains, we resolved this QTL to a single candidate gene, namely Map3k7. Expression of Map3k7 was largely restricted to islet cells as well as lymphocytes found in the exocrine pancreas of mice with AIP. Our studies suggest a major impact of diet on AIP. Furthermore, we identify Map3k7 as a novel susceptibility gene for experimental AIP. Both findings warrant clinical translation.

E2f8 and Dlg2 genes have independent effects on impaired insulin secretion associated with hyperglycaemia

AIMS/HYPOTHESIS

Reduced insulin secretion results in hyperglycaemia and diabetes involving a complex aetiology that is yet to be fully elucidated. Genetic susceptibility is a key factor in beta cell dysfunction and hyperglycaemia but the responsible genes have not been defined. The Collaborative Cross (CC) is a recombinant inbred mouse panel with diverse genetic backgrounds allowing the identification of complex trait genes that are relevant to human diseases. The aim of this study was to identify and characterise genes associated with hyperglycaemia.

METHODS

Using an unbiased genome-wide association study, we examined random blood glucose and insulin sensitivity in 53 genetically unique mouse strains from the CC population. The influences of hyperglycaemia susceptibility quantitative trait loci (QTLs) were investigated by examining glucose tolerance, insulin secretion, pancreatic histology and gene expression in the susceptible mice. Expression of candidate genes and their association with insulin secretion were examined in human islets. Mechanisms underlying reduced insulin secretion were studied in MIN6 cells using RNA interference.

RESULTS

Wide variations in blood glucose levels and the related metabolic traits (insulin sensitivity and body weight) were observed in the CC population. We showed that elevated blood glucose in the CC strains was not due to insulin resistance nor obesity but resulted from reduced insulin secretion. This insulin secretory defect was demonstrated to be independent of abnormalities in islet morphology, beta cell mass and pancreatic insulin content. Gene mapping identified the E2f8 (p = 2.19 × 10^-15) and Dlg2 loci (p = 3.83 × 10^-8) on chromosome 7 to be significantly associated with hyperglycaemia susceptibility. Fine mapping the implicated regions using congenic mice demonstrated that these two loci have independent effects on insulin secretion in vivo. Significantly, our results revealed that increased E2F8 and DLG2 gene expression are correlated with enhanced insulin secretory function in human islets. Furthermore, loss-of-function studies in MIN6 cells demonstrated that E2f8 is involved in insulin secretion through an ATP-sensitive K⁺ channel-dependent pathway, which leads to a 30% reduction in Abcc8 expression. Similarly, knockdown of Dlg2 gene expression resulted in impaired insulin secretion in response to glucose and non-glucose stimuli.

CONCLUSIONS/INTERPRETATION

Collectively, these findings suggest that E2F transcription factor 8 (E2F8) and discs large homologue 2 (DLG2) regulate insulin secretion. The CC resource enables the identification of E2f8 and Dlg2 as novel genes associated with hyperglycaemia due to reduced insulin secretion in pancreatic beta cells. Taken together, our results provide better understanding of the molecular control of insulin secretion and further support the use of the CC resource to identify novel genes relevant to human diseases.

Ten Years of the Maize Nested Association Mapping Population: Impact, Limitations, and Future Directions

It has been just over a decade since the release of the maize (Zea mays) Nested Association Mapping (NAM) population. The NAM population has been and continues to be an invaluable resource for the maize genetics community and has yielded insights into the genetic architecture of complex traits. The parental lines have become some of the most well-characterized maize germplasm, and their de novo assemblies were recently made publicly available. As we enter an exciting new stage in maize genomics, this retrospective will summarize the design and intentions behind the NAM population; its application, the discoveries it has enabled, and its influence in other systems; and use the past decade of hindsight to consider whether and how it will remain useful in a new age of genomics.

Assessing the host genetic background effects on type 2 diabetes and obesity development in response to mixed-oral bacteria and high-fat diet using the collaborative cross mouse model

BACKGROUND

Host genetic background and sex, play central roles in defining the pathogenesis of type 2 diabetes (T2D), obesity and infectious diseases. Our previous studies demonstrated the utilization of genetically highly diverse inbred mouse lines, namely collaborative cross (CC), for dissecting host susceptibility for the development of T2D and obesity, showing significant variations following high-fat (42% fat) diet (HFD). Here, we aimed to assessing the host genetic background and sex effects on T2D and obesity development in response to oral-mixed bacterial infection and HFD using the CC lines.

MATERIALS AND METHODS

Study cohort consists of 97 mice from 2 CC lines (both sexes), maintained on either HFD or Standard diet (CHD) for 12 weeks. At week 5 a group of mice from each diet were infected with Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) bacteria (control groups without infection). Body weight (BW) and glucose tolerance ability were assessed at the end time point of the experiment.

RESULTS

The CC lines varied (P < .05) at their BW gain and glucose tolerance ability (with sex effect) in response to diets and/or infection, showing opposite responses despite sharing the same environmental conditions. The combination of diet and infection enhances BW accumulation for IL1912, while restraints it for IL72. As for glucose tolerance ability, only females (both lines) were deteriorated in response to infection.

CONCLUSIONS

This study emphasizes the power of the CC mouse population for the characterization of host genetic makeup for defining the susceptibility of the individual to development of obesity and/or impaired glucose tolerance.

Two Synthetic 18-Way Outcrossed Populations of Diploid Budding Yeast with Utility for Complex Trait Dissection

Advanced-generation multiparent populations (MPPs) are a valuable tool for dissecting complex traits, having more power than genome-wide association studies to detect rare variants and higher resolution than F2 linkage mapping. To extend the advantages of MPPs in budding yeast, we describe the creation and characterization of two outbred MPPs derived from 18 genetically diverse founding strains. We carried out de novo assemblies of the genomes of the 18 founder strains, such that virtually all variation segregating between these strains is known, and represented those assemblies as Santa Cruz Genome Browser tracks. We discovered complex patterns of structural variation segregating among the founders, including a large deletion within the vacuolar ATPase VMA1, several different deletions within the osmosensor MSB2, a series of deletions and insertions at PRM7 and the adjacent BSC1, as well as copy number variation at the dehydrogenase ALD2 Resequenced haploid recombinant clones from the two MPPs have a median unrecombined block size of 66 kb, demonstrating that the population is highly recombined. We pool-sequenced the two MPPs to 3270× and 2226× coverage and demonstrated that we can accurately estimate local haplotype frequencies using pooled data. We further downsampled the pool-sequenced data to ∼20-40× and showed that local haplotype frequency estimates remained accurate, with median error rates 0.8 and 0.6% at 20× and 40×, respectively. Haplotypes frequencies are estimated much more accurately than SNP frequencies obtained directly from the same data. Deep sequencing of the two populations revealed that 10 or more founders are present at a detectable frequency for > 98% of the genome, validating the utility of this resource for the exploration of the role of standing variation in the architecture of complex traits.

Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population

The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions. These traits are observed at multiple levels of the animals' systems, including metabolism, body weight, immune profile, and susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present general guidelines for design of QTL mapping experiments using the CC resource-along with full step-by-step protocols and methods that were implemented in our lab for the phenotypic and genotypic characterization of the different CC lines-for mapping the genes underlying host response to infectious and chronic diseases. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: CC lines for whole body mass index (BMI) Basic Protocol 2: A detailed assessment of the power to detect effect sizes based on the number of lines used, and the number of replicates per line Basic Protocol 3: Obtaining power for QTL with given target effect by interpolating in Table 1 of Keele et al. (2019).

Collaborative Cross Mice Yield Genetic Modifiers for Pseudomonas aeruginosa Infection in Human Lung Disease

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.

A genome-wide association study in mice reveals a role for Rhbdf2 in skeletal homeostasis

Low bone mass and an increased risk of fracture are predictors of osteoporosis. Individuals who share the same bone-mineral density (BMD) vary in their fracture risk, suggesting that microstructural architecture is an important determinant of skeletal strength. Here, we utilized the rich diversity of the Collaborative Cross mice to identify putative causal genes that contribute to the risk of fractures. Using microcomputed tomography, we examined key structural features that pertain to bone quality in the femoral cortical and trabecular compartments of male and female mice. We estimated the broad-sense heritability to be 50–60% for all examined traits, and we identified five quantitative trait loci (QTL) significantly associated with six traits. We refined each QTL by combining information inferred from the ancestry of the mice, ranging from RNA-Seq data and published literature to shortlist candidate genes. We found strong evidence for new candidate genes, particularly Rhbdf2, whose close association with the trabecular bone volume fraction and number was strongly suggested by our analyses. We confirmed our findings with mRNA expression assays of Rhbdf2 in extreme-phenotype mice, and by phenotyping bones of Rhbdf2 knockout mice. Our results indicate that Rhbdf2 plays a decisive role in bone mass accrual and microarchitecture.

Construction and integration of genetic linkage maps from three multi-parent advanced generation inter-cross populations in rice

BACKGROUND

The construction of genetic maps based on molecular markers is a crucial step in rice genetic and genomic studies. Pure lines derived from multiple parents provide more abundant genetic variation than those from bi-parent populations. Two four-parent pure-line populations (4PL1 and 4PL2) and one eight-parent pure-line population (8PL) were developed from eight homozygous indica varieties of rice by the International Rice Research Institute (IRRI). To the best of our knowledge, there have been no reports on linkage map construction and their integration in multi-parent populations of rice.

RESULTS

We constructed linkage maps for the three multi-parent populations and conducted quantitative trait locus (QTL) mapping for heading date (HD) and plant height (PH) based on the three maps by inclusive composite interval mapping (ICIM). An integrated map was built from the three individual maps and used for QTL projection and meta-analysis. QTL mapping of the three populations was also conducted based on the integrated map, and the mapping results were compared with those from meta-analysis. The three linkage maps developed for 8PL, 4PL1 and 4PL2 had 5905, 4354 and 5464 bins and were 1290.16, 1720.01 and 1560.30 cM in length, respectively. The integrated map was 3022.08 cM in length and contained 10,033 bins. Based on the three linkage maps, 3, 7 and 9 QTLs were detected for HD while 6, 9 and 10 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. In contrast, 19 and 25 QTLs were identified for HD and PH by meta-analysis using the integrated map, respectively. Based on the integrated map, 5, 9, and 10 QTLs were detected for HD while 3, 10, and 12 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. Eleven of these 49 QTLs coincided with those from the meta-analysis.

CONCLUSIONS

In this study, we reported the first rice linkage map constructed from one eight-parent recombinant inbred line (RIL) population and the first integrated map from three multi-parent populations, which provide essential information for QTL linkage mapping, meta-analysis, and map-based cloning in rice genetics and breeding.

CUBIC: an atlas of genetic architecture promises directed maize improvement

BACKGROUND

Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders.

RESULTS

Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width.

CONCLUSIONS

Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation

Gene transcription profiles across tissues are largely defined by the activity of regulatory elements, most of which correspond to regions of accessible chromatin. Regulatory element activity is in turn modulated by genetic variation, resulting in variable transcription rates across individuals. The interplay of these factors, however, is poorly understood. Here we characterize expression and chromatin state dynamics across three tissues-liver, lung, and kidney-in 47 strains of the Collaborative Cross (CC) mouse population, examining the regulation of these dynamics by expression quantitative trait loci (eQTL) and chromatin QTL (cQTL). QTL whose allelic effects were consistent across tissues were detected for 1,101 genes and 133 chromatin regions. Also detected were eQTL and cQTL whose allelic effects differed across tissues, including local-eQTL for Pik3c2g detected in all three tissues but with distinct allelic effects. Leveraging overlapping measurements of gene expression and chromatin accessibility on the same mice from multiple tissues, we used mediation analysis to identify chromatin and gene expression intermediates of eQTL effects. Based on QTL and mediation analyses over multiple tissues, we propose a causal model for the distal genetic regulation of Akr1e1, a gene involved in glycogen metabolism, through the zinc finger transcription factor Zfp985 and chromatin intermediates. This analysis demonstrates the complexity of transcriptional and chromatin dynamics and their regulation over multiple tissues, as well as the value of the CC and related genetic resource populations for identifying specific regulatory mechanisms within cells and tissues.

Whole genome sequencing and novel candidate genes for CAKUT and altered nephrogenesis in the HSRA rat

The HSRA rat is a model of congenital abnormalities of the kidney and urogenital tract (CAKUT). Our laboratory has used this model to investigate the role of nephron number (functional unit of the kidney) in susceptibility to develop kidney disease as 50-75% offspring are born with a single kidney (HSRA-S), while 25-50% are born with two kidneys (HSRA-C). HSRA-S rats develop increased kidney injury and hypertension with age compared with nephrectomized two-kidney animals (HSRA-UNX), suggesting that even slight differences in nephron number can be an important driver in decline in kidney function. The HSRA rat was selected and inbred from a family of outbred heterogeneous stock (NIH-HS) rats that exhibited a high incidence of CAKUT. The HS model was originally developed from eight inbred strains (ACI, BN, BUF, F344, M520, MR, WKY, and WN). The genetic make-up of the HSRA is therefore a mosaic of these eight inbred strains. Interestingly, the ACI progenitor of the HS model exhibits CAKUT in 10-15% of offspring with the genetic cause being attributed to the presence of a long-term repeat (LTR) within exon 1 of the c-Kit gene. Our hypothesis is that the HSRA and ACI share this common genetic cause, but other alleles in the HSRA genome contribute to the increased penetrance of CAKUT (75% HSRA vs. 15% in ACI). To facilitate genetic studies and better characterize the model, we sequenced the whole genome of the HSRA to a depth of ~50×. A genome-wide variant analysis of high-impact variants identified a number of novel genes that could be linked to CAKUT in the HSRA model. In summary, the identification of new genes/modifiers that lead to CAKUT/loss of one kidney in the HSRA model will provide greater insight into association between kidney development and susceptibility to develop cardiovascular disease later in life.

Bin-based genome-wide association analyses improve power and resolution in QTL mapping and identify favorable alleles from multiple parents in a four-way MAGIC rice population

A whole genome bin map was developed for a MAGIC population. Association studies for heading date at bin level exhibited powerful QTL mapping and identified favorable alleles. The presumed advantages of multiparent advanced generation intercross (MAGIC) population in quantitative trait locus (QTL) mapping were not fully utilized in the previous studies in which genome-wide association studies (GWAS) were conducted at only single nucleotide polymorphism level. In this study, we genotyped a rice four-way MAGIC population of 247 F₇ lines and their parents by sequencing. A total of 5934 bins with an average length of 65 kb were constructed and covered 97% of the genome. The MAGIC population showed low population structure and balanced parental contributions. A bin-based GWAS for heading date identified 4 QTLs in three environments. Three major QTLs were mapped exactly to the bins where the major heading date genes DTH3, Ghd7.1 and Ghd8 were located. Multiple comparisons showed that different parental alleles had varied genetic effects. Like DTH3, the alleles of the Guichao 2/YJSM, IR34 and Cypress had larger, intermediate and no effects, respectively. Based on comparative sequencing of 8 known heading date genes undetected in this MAGIC population, only Ghd7 exhibited diverse function among parents. The failure in Ghd7 mapping was well explained by its interaction with Hd1 because Ghd7 had no effects on heading date when combined with the nonfunctional hd1 carried by all four parents. Overall, bin-based GWAS have more mapping power and higher resolution with a MAGIC population and provide favorable alleles to breeders. The use of more diversified parents is encouraged to develop a MAGIC population for detecting more QTLs for important agronomical traits.

Exploring genetic architecture of grain yield and quality traits in a 16-way indica by japonica rice MAGIC global population

Identification of Quantitative Trait Loci (QTL) has been a challenge for complex traits due to the use of populations with narrow genetic base. Most of QTL mapping studies were carried out from crosses made within the subspecies, either indica × indica or japonica × japonica. In this study we report advantages of using Multi-parent Advanced Generation Inter-Crosses global population, derived from a combination of eight indica and eight japonica elite parents, in QTL discovery for yield and grain quality traits. Genome-wide association study and interval mapping identified 38 and 34 QTLs whereas Bayesian networking detected 60 QTLs with 22 marker-marker associations, 32 trait-trait associations and 65 marker-trait associations. Notably, nine known QTLs/genes qPH₁/OsGA20ox2, qDF₃/OsMADS50, PL, QDg1, qGW-5b, grb7-2, qGL₃/GS3, Amy6/Wx gene and OsNAS3 were consistently identified by all approaches for nine traits whereas qDF₃/OsMADS50 was co-located for both yield and days-to-flowering traits on chromosome 3. Moreover, we identified a number of candidate QTLs in either one or two analyses but further validations will be needed. The results indicate that this new population has enabled identifications of significant QTLs and interactions for 16 traits through multiple approaches. Pyramided recombinant inbred lines provide a valuable source for integration into future breeding programs.

QTL.gCIMapping.GUI v2.0: An R software for detecting small-effect and linked QTLs for quantitative traits in bi-parental segregation populations

The methodologies and software packages for mapping quantitative trait loci (QTLs) in bi-parental segregation populations are well established. However, it is still difficult to detect small-effect and linked QTLs. To address this issue, we proposed a genome-wide composite interval mapping (GCIM) in bi-parental segregation populations. To popularize this method, we developed an R package. This program with two versions (Graphical User Interface: QTL.gCIMapping.GUI v2.0 and code: QTL.gCIMapping v3.2) can be used to identify QTLs for quantitative traits in recombinant inbred lines, doubled haploid lines, backcross and F2 populations. To save running time, fread function was used to read the dataset, parallel operation was used in parameter estimation, and conditional probability calculation was implemented by C++. Once one input file with *.csv or *.txt formats is uploaded into the package, one or two output files and one figure can be obtained. The input file with the ICIM and win QTL cartographer formats is available as well. Real data analysis for 1000-grain weight in rice showed that the GCIM detects the maximum previously reported QTLs and genes, and has the minimum AIC value in the stepwise regression of all the identified QTLs for this trait; using stepwise regression and empirical Bayesian analyses, there are some false QTLs around the previously reported QTLs and genes from the CIM method. The above software packages on Windows, Mac and Linux can be downloaded from https://cran.r-project.org/web/packages/ or https://bigd.big.ac.cn/biocode/tools/7078/releases/27 in order to identify all kinds of omics QTLs.

Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits

In this chapter we will review both the rationale and experimental design for using Heterogeneous Stock (HS) populations for fine-mapping of complex traits in mice and rats. We define an HS as an outbred population derived from an intercross between two or more inbred strains. HS have been used to perform genome-wide association studies (GWAS) for multiple behavioral, physiological, and gene expression traits. GWAS using HS require four key steps, which we review: selection of an appropriate HS population, phenotyping, genotyping, and statistical analysis. We provide advice on the selection of an HS, comment on key issues related to phenotyping, discuss genotyping methods relevant to these populations, and describe statistical genetic analyses that are applicable to genetic analyses that use HS.

Natural variation in Arabidopsis shoot branching plasticity in response to nitrate supply affects fitness

The capacity of organisms to tune their development in response to environmental cues is pervasive in nature. This phenotypic plasticity is particularly striking in plants, enabled by their modular and continuous development. A good example is the activation of lateral shoot branches in Arabidopsis, which develop from axillary meristems at the base of leaves. The activity and elongation of lateral shoots depends on the integration of many signals both external (e.g. light, nutrient supply) and internal (e.g. the phytohormones auxin, strigolactone and cytokinin). Here, we characterise natural variation in plasticity of shoot branching in response to nitrate supply using two diverse panels of Arabidopsis lines. We find extensive variation in nitrate sensitivity across these lines, suggesting a genetic basis for variation in branching plasticity. High plasticity is associated with extreme branching phenotypes such that lines with the most branches on high nitrate have the fewest under nitrate deficient conditions. Conversely, low plasticity is associated with a constitutively moderate level of branching. Furthermore, variation in plasticity is associated with alternative life histories with the low plasticity lines flowering significantly earlier than high plasticity lines. In Arabidopsis, branching is highly correlated with fruit yield, and thus low plasticity lines produce more fruit than high plasticity lines under nitrate deficient conditions, whereas highly plastic lines produce more fruit under high nitrate conditions. Low and high plasticity, associated with early and late flowering respectively, can therefore be interpreted alternative escape vs mitigate strategies to low N environments. The genetic architecture of these traits appears to be highly complex, with only a small proportion of the estimated genetic variance detected in association mapping.

Dissecting Epistatic QTL for Blood Pressure in Rats: Congenic Strains versus Heterogeneous Stocks, a Reality Check

Advances in molecular genetics have provided well-defined physical genetic maps and large numbers of genetic markers for both model organisms and humans. It is now possible to gain a fundamental understanding of the genetic architecture underlying quantitative traits, of which blood pressure (BP) is an important example. This review emphasizes analytical techniques and results obtained using the Dahl salt-sensitive (S) rat as a model of hypertension by presenting results in detail for three specific chromosomal regions harboring genetic elements of increasing complexity controlling BP. These results highlight the critical importance of genetic interactions (epistasis) on BP at all levels of structure, intragenic, intergenic, intrachromosomal, interchromosomal, and across whole genomes. In two of the three examples presented, specific DNA structural variations leading to biochemical, physiological, and pathological mechanisms are well defined. This proves the usefulness of the techniques involving interval mapping followed by substitution mapping using congenic strains. These classic techniques are compared to newer approaches using sophisticated statistical analysis on various segregating or outbred model-organism populations, which in some cases are uniquely useful in demonstrating the existence of higher-order interactions. It is speculated that hypertension as an outlier quantitative phenotype is dependent on higher-order genetic interactions. The obstacle to the identification of genetic elements and the biochemical/physiological mechanisms involved in higher-order interactions is not theoretical or technical but the lack of future resources to finish the job of identifying the individual genetic elements underlying the quantitative trait loci for BP and ascertaining their molecular functions. © 2019 American Physiological Society. Compr Physiol 9:1305-1337, 2019.