The long path from QTL to gene

Genetic characterization of outbred Sprague Dawley rats and utility for genome-wide association studies

Sprague Dawley (SD) rats are among the most widely used outbred laboratory rat populations. Despite this, the genetic characteristics of SD rats have not been clearly described, and SD rats are rarely used for experiments aimed at exploring genotype-phenotype relationships. In order to use SD rats to perform a genome-wide association study (GWAS), we collected behavioral data from 4,625 SD rats that were predominantly obtained from two commercial vendors, Charles River Laboratories and Harlan Sprague Dawley Inc. Using double-digest genotyping-by-sequencing (ddGBS), we obtained dense, high-quality genotypes at 291,438 SNPs across 4,061 rats. This genetic data allowed us to characterize the variation present in Charles River vs. Harlan SD rats. We found that the two populations are highly diverged (F_ST > 0.4). Furthermore, even for rats obtained from the same vendor, there was strong population structure across breeding facilities and even between rooms at the same facility. We performed multiple separate GWAS by fitting a linear mixed model that accounted for population structure and using meta-analysis to jointly analyze all cohorts. Our study examined Pavlovian conditioned approach (PavCA) behavior, which assesses the propensity for rats to attribute incentive salience to reward-associated cues. We identified 46 significant associations for the various metrics used to define PavCA. The surprising degree of population structure among SD rats from different sources has important implications for their use in both genetic and non-genetic studies.

Outbred Sprague Dawley rats are among the most commonly used rats for neuroscience, physiology and pharmacological research; in the year 2020, 4,188 publications contained the keyword “Sprague Dawley”. Rats identified as “Sprague Dawley” are sold by several commercial vendors, including Charles River Laboratories and Harlan Sprague Dawley Inc. (now Envigo). Despite their widespread use, little is known about the genetic diversity of SD. We genotyped more than 4,000 SD rats, which we used for a genome-wide association study (GWAS) and to characterize genetic differences between SD rats from Charles River Laboratories and Harlan. Our analysis revealed extensive population structure both between and within vendors. The GWAS for Pavlovian conditioned approach (PavCA) identified a number of genome-wide significant loci for that complex behavioral trait. Our results demonstrate that, despite sharing an identical name, SD rats that are obtained from different vendors are very different. Future studies should carefully define the exact source of SD rats being used and may exploit their genetic diversity for genetic studies of complex traits.

A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer's disease

To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer's disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.

Transcriptome-Based Prediction of Complex Traits in Maize

The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.

Gpr63 is a modifier of microcephaly in Ttc21b mouse mutants

The primary cilium is a signaling center critical for proper embryonic development. Previous studies have demonstrated that mice lacking Ttc21b have impaired retrograde trafficking within the cilium and multiple organogenesis phenotypes, including microcephaly. Interestingly, the severity of the microcephaly in Ttc21b^aln/aln homozygous null mutants is considerably affected by the genetic background and mutants on an FVB/NJ (FVB) background develop a forebrain significantly smaller than mutants on a C57BL/6J (B6) background. We performed a Quantitative Trait Locus (QTL) analysis to identify potential genetic modifiers and identified two regions linked to differential forebrain size: modifier of alien QTL1 (Moaq1) on chromosome 4 at 27.8 Mb and Moaq2 on chromosome 6 at 93.6 Mb. These QTLs were validated by constructing congenic strains. Further analysis of Moaq1 identified an orphan G-protein coupled receptor (GPCR), Gpr63, as a candidate gene. We identified a SNP that is polymorphic between the FVB and B6 strains in Gpr63 and creates a missense mutation predicted to be deleterious in the FVB protein. We used CRISPR-Cas9 genome editing to create two lines of FVB congenic mice: one with the B6 sequence of Gpr63 and the other with a deletion allele leading to a truncation of the GPR63 C-terminal tail. We then demonstrated that Gpr63 can localize to the cilium in vitro. These alleles affect ciliary localization of GPR63 in vitro and genetically interact with Ttc21b^aln/aln as Gpr63;Ttc21b double mutants show unique phenotypes including spina bifida aperta and earlier embryonic lethality. This validated Gpr63 as a modifier of multiple Ttc21b neural phenotypes and strongly supports Gpr63 as a causal gene (i.e., a quantitative trait gene, QTG) within the Moaq1 QTL.

TTC21B in humans is a known ciliopathy gene and contributes to the pathophysiology of a number of ciliopathies. Mice homozygous for a null allele of Ttc21b also have a spectrum of ciliopathy phenotypes, including microcephaly (small brain). Further work has shown that the severity of the microcephaly significantly depends on the genetic background of the mouse model. The genetic mechanisms of the Ttc21b pathophysiology and the interacting gene network remain far from understood. As an initial attempt to understand the underlying mechanism(s) underlying the variable effects on brain size, we performed a quantitative trait locus (QTL) analysis and found two regions of genomic significance that correlated with smaller brain size. We confirmed both QTLs with congenic lines. One of the two regions was small enough that we considered candidate genes and hypothesized Gpr63 might be a contributing locus for a number of reasons. We evaluated this hypothesis directly with precise variant creation using genome editing and provide evidence that Ttc21b and Gpr63 do indeed genetically interact. Thus, we have been able to combine classical QTL analysis and genome editing to directly test the resulting hypothesis.

Mitochondrial polymorphisms contribute to aging phenotypes in MNX mouse models

Many inbred strains of mice develop spontaneous tumors as they age. Recent awareness of the impacts of mitochondrial DNA (mtDNA) on cancer and aging has inspired developing a mitochondrial-nuclear exchange (MNX) mouse model in which nuclear DNA is paired with mitochondrial genomes from other strains of mouse. MNX mice exhibit mtDNA influences on tumorigenicity and metastasis upon mating with transgenic mice. However, we also wanted to investigate spontaneous tumor phenotypes as MNX mice age. Utilizing FVB/NJ, C57BL/6J, C3H/HeN, and BALB/cJ wild-type inbred strains, previously documented phenotypes were observed as expected in MNX mice with the same nuclear background. However, aging nuclear matched MNX mice exhibited decreased occurrence of mammary tumors in C3H/HeN mice containing C57BL/6J mitochondria compared to wild-type C3H/HeN mice. Although aging tumor phenotypes appear to be driven by nuclear genes, evidence suggesting that some differences are modified by the mitochondrial genome is presented.

Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2

To fine map a mouse QTL for lean body mass (Burly1), we used information from intercross, backcross, consomic, and congenic mice derived from the C57BL/6ByJ (host) and 129P3/J (donor) strains. The results from these mapping populations were concordant and showed that Burly1 is located between 151.9 and 152.7 Mb (rs33197365 to rs3700604) on mouse chromosome 2. The congenic region harboring Burly1 contains 26 protein-coding genes, 11 noncoding RNA elements (e.g., lncRNA), and 4 pseudogenes, with 1949 predicted functional variants. Of the protein-coding genes, 7 have missense variants, including genes that may contribute to lean body weight, such as Angpt41, Slc52c3, and Rem1. Lean body mass was increased by the B6-derived variant relative to the 129-derived allele. Burly1 influenced lean body weight at all ages but not food intake or locomotor activity. However, congenic mice with the B6 allele produced more heat per kilogram of lean body weight than did controls, pointing to a genotype effect on lean mass metabolism. These results show the value of integrating information from several mapping populations to refine the map location of body composition QTLs and to identify a short list of candidate genes.

Adiposity QTL Adip20 decomposes into at least four loci when dissected using congenic strains

An average mouse in midlife weighs between 25 and 30 g, with about a gram of tissue in the largest adipose depot (gonadal), and the weight of this depot differs between inbred strains. Specifically, C57BL/6ByJ mice have heavier gonadal depots on average than do 129P3/J mice. To understand the genetic contributions to this trait, we mapped several quantitative trait loci (QTLs) for gonadal depot weight in an F2 intercross population. Our goal here was to fine-map one of these QTLs, Adip20 (formerly Adip5), on mouse chromosome 9. To that end, we analyzed the weight of the gonadal adipose depot from newly created congenic strains. Results from the sequential comparison method indicated at least four rather than one QTL; two of the QTLs were less than 0.5 Mb apart, with opposing directions of allelic effect. Different types of evidence (missense and regulatory genetic variation, human adiposity/body mass index orthologues, and differential gene expression) implicated numerous candidate genes from the four QTL regions. These results highlight the value of mouse congenic strains and the value of this sequential method to dissect challenging genetic architecture.

The Mouse Tumor Biology Database: A Comprehensive Resource for Mouse Models of Human Cancer

Research using laboratory mice has led to fundamental insights into the molecular genetic processes that govern cancer initiation, progression, and treatment response. Although thousands of scientific articles have been published about mouse models of human cancer, collating information and data for a specific model is hampered by the fact that many authors do not adhere to existing annotation standards when describing models. The interpretation of experimental results in mouse models can also be confounded when researchers do not factor in the effect of genetic background on tumor biology. The Mouse Tumor Biology (MTB) database is an expertly curated, comprehensive compendium of mouse models of human cancer. Through the enforcement of nomenclature and related annotation standards, MTB supports aggregation of data about a cancer model from diverse sources and assessment of how genetic background of a mouse strain influences the biological properties of a specific tumor type and model utility. Cancer Res; 77(21); e67-70. ©2017 AACR.

Genotyping-by-sequencing reveals three QTL for clubroot resistance to six pathotypes of Plasmodiophora brassicae in Brassica rapa

Clubroot, caused by Plasmodiophora brassicae, is an important disease of Brassica crops worldwide. F1 progeny from the Brassica rapa lines T19 (resistant) × ACDC (susceptible) were backcrossed with ACDC, then self-pollinated to produce BC1S1 lines, From genotyping-by-sequencing (GBS) of the parental lines and BC1 plants, about 1.32 M sequences from T19 were aligned into the reference genome of B. rapa with 0.4-fold coverage, and 1.77 M sequences with 0.5-fold coverage in ACDC. The number of aligned short reads per plant in the BC1 ranged from 0.07 to 1.41 M sequences with 0.1-fold coverage. A total of 1584 high quality SNP loci were obtained, distributed on 10 chromosomes. A single co-localized QTL, designated as Rcr4 on chromosome A03, conferred resistance to pathotypes 2, 3, 5, 6 and 8. The peak was at SNP locus A03_23710236, where LOD values were 30.3 to 38.8, with phenotypic variation explained (PVE) of 85-95%. Two QTLs for resistance to a novel P. brassicae pathotype 5x, designated Rcr8 on chromosome A02 and Rcr9 on A08, were detected with 15.0 LOD and 15.8 LOD, and PVE of 36% and 39%, respectively. Bulked segregant analysis was performed to examine TIR-NBS-LRR proteins in the regions harboring the QTL.

Mapping Complex Traits in a Diversity Outbred F1 Mouse Population Identifies Germline Modifiers of Metastasis in Human Prostate Cancer

It is unclear how standing genetic variation affects the prognosis of prostate cancer patients. To provide one controlled answer to this problem, we crossed a dominant, penetrant mouse model of prostate cancer to Diversity Outbred mice, a collection of animals that carries over 40 million SNPs. Integration of disease phenotype and SNP variation data in 493 F1 males identified a metastasis modifier locus on Chromosome 8 (LOD = 8.42); further analysis identified the genes Rwdd4, Cenpu, and Casp3 as functional effectors of this locus. Accordingly, analysis of over 5,300 prostate cancer patient samples revealed correlations between the presence of genetic variants at these loci, their expression levels, cancer aggressiveness, and patient survival. We also observed that ectopic overexpression of RWDD4 and CENPU increased the aggressiveness of two human prostate cancer cell lines. In aggregate, our approach demonstrates how well-characterized genetic variation in mice can be harnessed in conjunction with systems genetics approaches to identify and characterize germline modifiers of human disease processes.

Conserved 33-kb haplotype in the MHC class III region regulates chronic arthritis

Genome-wide association studies have revealed many genetic loci associated with complex autoimmune diseases. In rheumatoid arthritis (RA), the MHC gene HLA-DRB1 is the strongest candidate predicting disease development. It has been suggested that other immune-regulating genes in the MHC contribute to the disease risk, but this contribution has been difficult to show because of the strong linkage disequilibrium within the MHC. We isolated genomic regions in the form of congenic fragments in rats to test whether there are additional susceptibility loci in the MHC. By both congenic mapping in inbred strains and SNP typing in wild rats, we identified a conserved, 33-kb large haplotype Ltab-Ncr3 in the MHC-III region, which regulates the onset, severity, and chronicity of arthritis. The Ltab-Ncr3 haplotype consists of five polymorphic immunoregulatory genes: Lta (lymphotoxin-α), Tnf, Ltb (lymphotoxin-β), Lst1 (leukocyte-specific transcript 1), and Ncr3 (natural cytotoxicity-triggering receptor 3). Significant correlation in the expression of the Ltab-Ncr3 genes suggests that interaction of these genes may be important in keeping these genes clustered together as a conserved haplotype. We studied the arthritis association and the spliceo-transcriptome of four different Ltab-Ncr3 haplotypes and showed that higher Ltb and Ncr3 expression, lower Lst1 expression, and the expression of a shorter splice variant of Lst1 correlate with reduced arthritis severity in rats. Interestingly, patients with mild RA also showed higher NCR3 expression and lower LST1 expression than patients with severe RA. These data demonstrate the importance of a conserved haplotype in the regulation of complex diseases such as arthritis.

Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model

Background

In recent years, with the development of high-throughput sequencing technology and the commercial availability of genotyping bead chips, more attention is being directed towards the utilization of abundant genetic markers in animal and plant breeding programs, human disease risk prediction and personal medicine. Several useful approaches to accomplish genomic prediction have been developed and used widely, but still have room for improvement to gain more accuracy. In this study, an improved Bayesian approach, termed BayesBπ, which differs from the original BayesB in priors assigning, is proposed. An effective method for calculating the locus-specific π by converting p-values from association between SNPs and traits’ phenotypes is given and systemically validated using a German Holstein dairy cattle population. Furthermore, the new method is applied to a loblolly pine (Pinus taeda) dataset.

Results

Compared with the original BayesB, BayesBπ can improve the accuracy of genomic prediction up to 7.62 % for milk fat percentage, a trait which shows a large effect of quantitative trait loci (QTL). For milk yield, which is controlled by small to moderate effect genes, the accuracy of genomic prediction can be improved up to 4.94 %. For somatic cell score, of which no large effect QTL has been reported, GBLUP performs better than Bayesian methods. BayesBπ outperforms BayesCπ in 10 out of 12 scenarios in the dairy cattle population, especially in small to moderate population sizes where accuracy of BayesCπ are dramatically low. Results of the loblolly pine dataset show that BayesBπ outperforms BayesB in 14 out of 17 traits and BayesCπ in 8 out of 17 traits, respectively.

Conclusions

For traits controlled by large effect genes, BayesBπ can improve the accuracy of genomic prediction and unbiasedness of BayesB in moderate size populations. Knowledge of traits’ genetic architectures can be integrated into practices of genomic prediction by assigning locus-specific priors to markers, which will help Bayesian approaches perform better in variable selection and marker effects shrinkage.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0278-9) contains supplementary material, which is available to authorized users.

The Cnes2 locus on mouse chromosome 17 regulates host defense against cryptococcal infection through pleiotropic effects on host immunity

The genetic basis of natural susceptibility to progressive Cryptococcus neoformans infection is not well understood. Using C57BL/6 and CBA/J inbred mice, we previously identified three chromosomal regions associated with C. neoformans susceptibility (Cnes1, Cnes2, and Cnes3). To validate and characterize the role of Cnes2 during the host response, we constructed a congenic strain on the C57BL/6 background (B6.CBA-Cnes2). Phenotypic analysis of B6.CBA-Cnes2 mice 35 days after C. neoformans infection showed a significant reduction of fungal burden in the lungs and spleen with higher pulmonary expression of gamma interferon (IFN-γ) and interleukin-12 (IL-12), lower expression of IL-4, IL-5, and IL-13, and an absence of airway epithelial mucus production compared to that in C57BL/6 mice. Multiparameter flow cytometry of infected lungs also showed a significantly higher number of neutrophils, exudate macrophages, CD11b(+) dendritic cells, and CD4(+) cells in B6.CBA-Cnes2 than in C57BL/6 mice. The activation state of recruited macrophages and dendritic cells was also significantly increased in B6.CBA-Cnes2 mice. Taken together, these findings demonstrate that the Cnes2 interval is a potent regulator of host defense, immune responsiveness, and differential Th1/Th2 polarization following C. neoformans infection.

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.

The crop QTLome comes of age

Recent progress in genomics and phenomics allows for a more accurate and comprehensive characterization of the Quantitative Trait Loci (QTLs)—hereafter defined 'QTLome' as a whole—that govern the variation targeted in breeding programs. High-density genotyping now provides unambiguous identification of QTL alleles, and for several traits beneficial alleles at major QTLs have already been deployed in marker-assisted breeding. However, the amount of QTLome information is enormous and approaches to distill and translate this information to breeders remain to be refined. Improved QTL meta-analyses, better estimation of QTL effects, improved crop modelling and full sharing of raw QTL data will enable a more effective exploitation of the QTLome.

Fine mapping and candidate gene search of quantitative trait loci for growth and obesity using mouse intersubspecific subcongenic intercrosses and exome sequencing

Although growth and body composition traits are quantitative traits of medical and agricultural importance, the genetic and molecular basis of those traits remains elusive. Our previous genome-wide quantitative trait locus (QTL) analyses in an intersubspecific backcross population between C57BL/6JJcl (B6) and wild Mus musculus castaneus mice revealed a major growth QTL (named Pbwg1) on a proximal region of mouse chromosome 2. Using the B6.Cg-Pbwg1 intersubspecific congenic strain created, we revealed 12 closely linked QTLs for body weight and body composition traits on an approximately 44.1-Mb wild-derived congenic region. In this study, we narrowed down genomic regions harboring three (Pbwg1.12, Pbwg1.3 and Pbwg1.5) of the 12 linked QTLs and searched for possible candidate genes for the QTLs. By phenotypic analyses of F2 intercross populations between B6 and each of four B6.Cg-Pbwg1 subcongenic strains with overlapping and non-overlapping introgressed regions, we physically defined Pbwg1.12 affecting body weight to a 3.8-Mb interval (61.5-65.3 Mb) on chromosome 2. We fine-mapped Pbwg1.3 for body length to an 8.0-Mb interval (57.3-65.3) and Pbwg1.5 for abdominal white fat weight to a 2.1-Mb interval (59.4-61.5). The wild-derived allele at Pbwg1.12 and Pbwg1.3 uniquely increased body weight and length despite the fact that the wild mouse has a smaller body size than that of B6, whereas it decreased fat weight at Pbwg1.5. Exome sequencing and candidate gene prioritization suggested that Gcg and Grb14 are putative candidate genes for Pbwg1.12 and that Ly75 and Itgb6 are putative candidate genes for Pbwg1.5. These genes had nonsynonymous SNPs, but the SNPs were predicted to be not harmful to protein functions. These results provide information helpful to identify wild-derived quantitative trait genes causing enhanced growth and resistance to obesity.

Mouse Tumor Biology (MTB): a database of mouse models for human cancer

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers.

Heritability and identification of QTLs and underlying candidate genes associated with the architecture of the grapevine cluster (Vitis vinifera L.)

We have identified 19 QTLs for rachis architecture, a key and complex trait for grapevine production. Fifty out of 1,173 genes underlying these QTLs are candidates to be further explored. In the table grape industry, the rachis architecture has economic and management implications. Therefore, understanding the genetics of this trait is key for its breeding. The aim of this work was to identify genetic determinants of traits associated with the cluster architecture. Characterisations of eight traits was performed on a 'Ruby Seedless' × 'Sultanina' crossing (F1: n = 137) during three seasons, with and without gibberellic acid (GA3) applications. The genotypic effects and the genotype × GA3 interactions were significant for several traits. Rachis length (rl), lateral shoulder length and node number along the central axis were the most prominent traits. On average, the heritability of these traits was ~71 %, with heritability of rl being 76 % as estimated under different seasons. Quantitative trait loci (QTLs) analyses showed that linkage group 5 (LG5) and LG18 harboured the largest number of QTLs for these traits. According to the variance explained, the main QTL (corresponding to rl) was found on LG9. These QTLs were supported mainly by a paternal additive effect and revealed possible pleiotropic effects. Based on the grapevine reference genome, we identified 1,173 genes located under these QTL confidence intervals. Fifty of the 891 annotated genes of this list were selected for their further characterisation because of their possible participation in the rachis architecture. In conclusion, the QTLs detected indicate that these traits and their GA3 responsiveness have a clear genetic basis. Due to the percentage of the total variance explained, they are good candidates to participate in the genetic determination of the cluster architecture.

A strategy to identify dominant point mutant modifiers of a quantitative trait

A central goal in the analysis of complex traits is to identify genes that modify a phenotype. Modifiers of a cancer phenotype may act either intrinsically or extrinsically on the salient cell lineage. Germline point mutagenesis by ethylnitrosourea can provide alleles for a gene of interest that include loss-, gain-, or alteration-of-function. Unlike strain polymorphisms, point mutations with heterozygous quantitative phenotypes are detectable in both essential and nonessential genes and are unlinked from other variants that might confound their identification and analysis. This report analyzes strategies seeking quantitative mutational modifiers of Apc^Min in the mouse. To identify a quantitative modifier of a phenotype of interest, a cluster of test progeny is needed. The cluster size can be increased as necessary for statistical significance if the founder is a male whose sperm is cryopreserved. A second critical element in this identification is a mapping panel free of polymorphic modifiers of the phenotype, to enable low-resolution mapping followed by targeted resequencing to identify the causative mutation. Here, we describe the development of a panel of six “isogenic mapping partner lines” for C57BL/6J, carrying single-nucleotide markers introduced by mutagenesis. One such derivative, B6.SNVg, shown to be phenotypically neutral in combination with Apc^Min, is an appropriate mapping partner to locate induced mutant modifiers of the Apc^Min phenotype. The evolved strategy can complement four current major initiatives in the genetic analysis of complex systems: the Genome-wide Association Study; the Collaborative Cross; the Knockout Mouse Project; and The Cancer Genome Atlas.

Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies

Utilizing the whole genomic variation of complex traits to predict the yet-to-be observed phenotypes or unobserved genetic values via whole genome prediction (WGP) and to infer the underlying genetic architecture via genome wide association study (GWAS) is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding. Though thousands of significant loci for several species were detected via GWAS in the past decade, they were not used directly to improve WGP due to lack of proper models. Here, we propose a generalized way of building trait-specific genomic relationship matrices which can exploit GWAS results in WGP via a best linear unbiased prediction (BLUP) model for which we suggest the name BLUP|GA. Results from two illustrative examples show that using already existing GWAS results from public databases in BLUP|GA improved the accuracy of WGP for two out of the three model traits in a dairy cattle data set, and for nine out of the 11 traits in a rice diversity data set, compared to the reference methods GBLUP and BayesB. While BLUP|GA outperforms BayesB, its required computing time is comparable to GBLUP. Further simulation results suggest that accounting for publicly available GWAS results is potentially more useful for WGP utilizing smaller data sets and/or traits of low heritability, depending on the genetic architecture of the trait under consideration. To our knowledge, this is the first study incorporating public GWAS results formally into the standard GBLUP model and we think that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well as human genetics.

Wild mice as bountiful resources of novel genetic variants for quantitative traits

Most traits of biological importance, including traits for human complex diseases (e.g., obesity and diabetes), are continuously distributed. These complex or quantitative traits are controlled by multiple genetic loci called QTLs (quantitative trait loci), environments and their interactions. The laboratory mouse has long been used as a pilot animal model for understanding the genetic architecture of quantitative traits. Next-generation sequencing analyses and genome-wide SNP (single nucleotide polymorphism) analyses of mouse genomes have revealed that classical inbred strains commonly used throughout the world are derived from a few fancy mice with limited and non-randomly distributed genetic diversity that occurs in nature and also indicated that their genomes are predominantly Mus musculus domesticus in origin. Many QTLs for a huge variety of traits have so far been discovered from a very limited gene pool of classical inbred strains. However, wild M. musculus mice consisting of five subspecies widely inhabit areas all over the world, and hence a number of novel QTLs may still lie undiscovered in gene pools of the wild mice. Some of the QTLs are expected to improve our understanding of human complex diseases. Using wild M. musculus subspecies in Asia as examples, this review illustrates that wild mice are untapped natural resources for valuable QTL discovery.

Altered expression of Raet1e, a major histocompatibility complex class 1-like molecule, underlies the atherosclerosis modifier locus Ath11 10b

RATIONALE

Quantitative trait locus mapping of an intercross between C57.Apoe⁻/⁻ and FVB.Apoe⁻/⁻ mice revealed an atherosclerosis locus controlling aortic root lesion area on proximal chromosome 10, Ath11. In a previous work, subcongenic analysis showed Ath11 to be complex with proximal (10a) and distal (10b) regions.

OBJECTIVE

To identify the causative genetic variation underlying the atherosclerosis modifier locus Ath11 10b.

METHODS AND RESULTS

We now report subcongenic J, which narrows the 10b region to 5 genes, Myb, Hbs1L, Aldh8a1, Sgk1, and Raet1e. Sequence analysis of these genes revealed no amino acid coding differences between the parental strains. However, comparing aortic expression of these genes between F1.Apoe⁻/⁻ Chr10SubJ((B/F)) and F1.Apoe⁻/⁻ Chr10SubJ((F/F)) uncovered a consistent difference only for Raet1e, with decreased, virtually background, expression associated with increased atherosclerosis in the latter. The key role of Raet1e was confirmed by showing that transgene-induced aortic overexpression of Raet1e in F1.Apoe⁻/⁻ Chr10SubJ((F/F)) mice decreased atherosclerosis. Promoter reporter constructs comparing C57 and FVB sequences identified an FVB mutation in the core of the major aortic transcription start site abrogating activity.

CONCLUSIONS

This nonbiased approach has revealed Raet1e, a major histocompatibility complex class 1-like molecule expressed in lesional aortic endothelial cells and macrophage-rich regions, as a novel atherosclerosis gene and represents one of the few successes of the quantitative trait locus strategy in complex diseases.

Rat Genome Database: a unique resource for rat, human, and mouse quantitative trait locus data

The rat has been widely used as a disease model in a laboratory setting, resulting in an abundance of genetic and phenotype data from a wide variety of studies. These data can be found at the Rat Genome Database (RGD, http://rgd.mcw.edu/), which provides a platform for researchers interested in linking genomic variations to phenotypes. Quantitative trait loci (QTLs) form one of the earliest and core datasets, allowing researchers to identify loci harboring genes associated with disease. These QTLs are not only important for those using the rat to identify genes and regions associated with disease, but also for cross-organism analyses of syntenic regions on the mouse and the human genomes to identify potential regions for study in these organisms. Currently, RGD has data on >1,900 rat QTLs that include details about the methods and animals used to determine the respective QTL along with the genomic positions and markers that define the region. RGD also curates human QTLs (>1,900) and houses>4,000 mouse QTLs (imported from Mouse Genome Informatics). Multiple ontologies are used to standardize traits, phenotypes, diseases, and experimental methods to facilitate queries, analyses, and cross-organism comparisons. QTLs are visualized in tools such as GBrowse and GViewer, with additional tools for analysis of gene sets within QTL regions. The QTL data at RGD provide valuable information for the study of mapped phenotypes and identification of candidate genes for disease associations.

The gene desert mammary carcinoma susceptibility locus Mcs1a regulates Nr2f1 modifying mammary epithelial cell differentiation and proliferation

Genome-wide association studies have revealed that many low-penetrance breast cancer susceptibility loci are located in non-protein coding genomic regions; however, few have been characterized. In a comparative genetics approach to model such loci in a rat breast cancer model, we previously identified the mammary carcinoma susceptibility locus Mcs1a. We now localize Mcs1a to a critical interval (277 Kb) within a gene desert. Mcs1a reduces mammary carcinoma multiplicity by 50% and acts in a mammary cell-autonomous manner. We developed a megadeletion mouse model, which lacks 535 Kb of sequence containing the Mcs1a ortholog. Global gene expression analysis by RNA-seq revealed that in the mouse mammary gland, the orphan nuclear receptor gene Nr2f1/Coup-tf1 is regulated by Mcs1a. In resistant Mcs1a congenic rats, as compared with susceptible congenic control rats, we found Nr2f1 transcript levels to be elevated in mammary gland, epithelial cells, and carcinoma samples. Chromatin looping over ∼820 Kb of sequence from the Nr2f1 promoter to a strongly conserved element within the Mcs1a critical interval was identified. This element contains a 14 bp indel polymorphism that affects a human-rat-mouse conserved COUP-TF binding motif and is a functional Mcs1a candidate. In both the rat and mouse models, higher Nr2f1 transcript levels are associated with higher abundance of luminal mammary epithelial cells. In both the mouse mammary gland and a human breast cancer global gene expression data set, we found Nr2f1 transcript levels to be strongly anti-correlated to a gene cluster enriched in cell cycle-related genes. We queried 12 large publicly available human breast cancer gene expression studies and found that the median NR2F1 transcript level is consistently lower in 'triple-negative' (ER-PR-HER2-) breast cancers as compared with 'receptor-positive' breast cancers. Our data suggest that the non-protein coding locus Mcs1a regulates Nr2f1, which is a candidate modifier of differentiation, proliferation, and mammary cancer risk.