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Power analyses are often used to determine the number of animals required for a genome-wide association study (GWAS). These analyses are typically intended to estimate the sample size needed for at least 1 locus to exceed a genome-wide significance threshold. A related question that is less commonly considered is the number of significant loci that will be discovered with a given sample size. We used simulations based on a real data set that consisted of 3,173 male and female adult N/NIH heterogeneous stock rats to explore the relationship between sample size and the number of significant loci discovered. Our simulations examined the number of loci identified in subsamples of the full data set. The subsampling analysis was conducted for 4 traits with low (0.15 ± 0.03), medium (0.31 ± 0.03 and 0.36 ± 0.03), and high (0.46 ± 0.03) SNP-based heritabilities. For each trait, we subsampled the data 100 times at different sample sizes (500, 1,000, 1,500, 2,000, and 2,500). We observed an exponential increase in the number of significant loci with larger sample sizes. Our results are consistent with similar observations in human GWAS and imply that future rodent GWAS should use sample sizes that are significantly larger than those needed to obtain a single significant result.

Power analyses are often used to determine the number of animals required for a genome wide association analysis (GWAS). These analyses are typically intended to estimate the sample size needed for at least one locus to exceed a genome-wide significance threshold. A related question that is less commonly considered is the number of significant loci that will be discovered with a given sample size. We used simulations based on a real dataset that consisted of 3,173 male and female adult N/NIH heterogeneous stock (HS) rats to explore the relationship between sample size and the number of significant loci discovered. Our simulations examined the number of loci identified in sub-samples of the full dataset. The sub-sampling analysis was conducted for four traits with low (0.15 ± 0.03), medium (0.31 ± 0.03 and 0.36 ± 0.03) and high (0.46 ± 0.03) SNP-based heritabilities. For each trait, we sub-sampled the data 100 times at different sample sizes (500, 1,000, 1,500, 2,000, and 2,500). We observed an exponential increase in the number of significant loci with larger sample sizes. Our results are consistent with similar observations in human GWAS and imply that future rodent GWAS should use sample sizes that are significantly larger than those needed to obtain a single significant result.

Elevated intraocular pressure (IOP) is influenced by environmental and genetic factors. Increased IOP is a major risk factor for most types of glaucoma, including primary open angle glaucoma (POAG). Investigating the genetic basis of IOP may lead to a better understanding of the molecular mechanisms of POAG. The goal of this study was to identify genetic loci involved in regulating IOP using outbred heterogeneous stock (HS) rats. HS rats are a multigenerational outbred population derived from eight inbred strains that have been fully sequenced. This population is ideal for genome-wide association studies (GWASs) owing to the accumulated recombinations among well-defined haplotypes, the relatively high allele frequencies, the accessibility to a large collection of tissue samples, and the large allelic effect size compared to human studies. Both male and female HS rats (N=1,812) were used in the study. Genotyping-by-sequencing was used to obtain ~3.5 million single nucleotide polymorphisms (SNP) from each individual. SNP heritability for IOP in HS rats was 0.32, which agrees with other studies. We performed a GWAS for the IOP phenotype using a linear mixed model and used permutation to determine a genome-wide significance threshold. We identified three genome-wide significant loci for IOP on chromosomes 1, 5, and 16. Next, we sequenced the mRNA of 51 whole eye samples to find cis-eQTLs to aid in identification of candidate genes. We report 5 candidate genes within those loci:Tyr,Ctsc,Plekhf2,Ndufaf6 and Angpt2.Tyr,Ndufaf6andAngpt2genes have been previously implicated by human GWAS of IOP-related conditions.CtscandPlekhf2genes represent novel findings that may provide new insight into the molecular basis of IOP. This study highlights the efficacy of HS rats for investigating the genetics of elevated IOP and identifying potential candidate genes for future functional testing.Contribution to the field statementGlaucoma is the leading cause of irreversible blindness worldwide. Intraocular pressure (IOP) is the only known modifiable risk factor. This study describes results of the genome-wide association study (GWAS) performed in outbred rats that identifies known and novel genes involved in IOP regulation. To our knowledge, this is the first GWAS performed for IOP in a rat model. Identifying novel candidate genes in the rat model provides insight into the risk factors for glaucoma in humans and potential pharmacological targets for regulating IOP. The rat model is advantageous for studying natural variations in IOP, controlling environmental exposures, and providing easier access to tissue that can be used in phenotyping and gene expression in future studies.

Common genetic factors likely contribute to multiple psychiatric diseases including mood and substance use disorders. Certain stable, heritable traits reflecting temperament, termed externalizing or internalizing, play a large role in modulating vulnerability to these disorders. To model these heritable tendencies, we selectively bred rats for high and low exploration in a novel environment (bred High Responders (bHR) vs. Low Responders (bLR)). To identify genes underlying the response to selection, we phenotyped and genotyped 558 rats from an F2cross between bHR and bLR. Several behavioral traits show high heritability, including the selection trait: exploratory locomotion (EL) in a novel environment. There were significant phenotypic and genetic correlations between tests that capture facets of EL and anxiety. There were also correlations with Pavlovian conditioned approach (PavCA) behavior despite the lower heritability of that trait.Ten significant and conditionally independent loci for six behavioral traits were identified. Five of the six traits reflect different facets of EL that were captured by three behavioral tests. Distance traveled measures from the open field and the elevated plus maze map onto different loci, thus may represent different aspects of novelty-induced locomotor activity. The sixth behavioral trait, number of fecal boli, is the only anxiety-related trait mapping to a significant locus on chromosome 18 within which thePik3c3gene is located. There were no significant loci for PavCA. We identified a missense variant in thePlekhf1gene on the chromosome 1:95 Mb QTL andFancfandGas2as potential candidate genes that may drive the chromosome 1:107 Mb QTL for EL traits. The identification of a locomotor activity-related QTL on chromosome 7 encompassing thePkhd1l1andTrhrgenes is consistent with our previous finding of these genes being differentially expressed in the hippocampus of bHR vs. bLR rats.The strong heritability coupled with identification of several loci associated with exploratory locomotion and emotionality provide compelling support for this selectively bred rat model in discovering relatively large effect causal variants tied to elements of internalizing and externalizing behaviors inherent to psychiatric and substance use disorders.

We developed a framework for identifying trait-associated genes in rats and facilitating the transfer of polygenic evidence across species by expanding the transcriptome-wide association (TWAS) approach to rats. Our analysis successfully trained transcript predictors for over 8000 genes in each of the five brain regions of rats, revealing several shared properties of gene regulation with humans. Moreover, mirroring trends observed in humans, our findings showed that sparse predictors using variants in cis are more effective than polygenic predictors and that gene expression prediction in rats is highly correlated across brain regions. Importantly, our analysis also identified a significant overlap between genes associated with rat and human body length and BMI, indicating rat models may be useful for studying the genetic basis of complex traits in humans. RatXcan represents a valuable tool for uncovering shared biological mechanisms of complex traits across species, with potential applications in a wide range of research fields.

Author(s): Chitre, Apurva S; Polesskaya, Oksana; Holl, Katie; Gao, Jianjun; Cheng, Riyan; Bimschleger, Hannah; Garcia Martinez, Angel; George, Tony; Gileta, Alexander F; Han, Wenyan; Horvath, Aidan; Hughson, Alesa; Ishiwari, Keita; King, Christopher P; Lamparelli, Alexander; Versaggi, Cassandra L; Martin, Connor; St Pierre, Celine L; Tripi, Jordan A; Wang, Tengfei; Chen, Hao; Flagel, Shelly B; Meyer, Paul; Richards, Jerry; Robinson, Terry E; Palmer, Abraham A; Solberg Woods, Leah C | Abstract: ObjectiveObesity is influenced by genetic and environmental factors. Despite the success of human genome-wide association studies, the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify the genetic loci underlying obesity and related morphometric and metabolic traits.MethodsThis study measured obesity-relevant traits, including body weight, body length, BMI, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat genome-wide association study to date. Genetic loci were identified using a linear mixed model to account for the complex family relationships of the HS and using covariates to account for differences among the three phenotyping centers.ResultsThis study identified 32 independent loci, several of which contained only a single gene (e.g., Epha5, Nrg1, Klhl14) or obvious candidate genes (e.g., Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity-related traits, and there was extensive pleiotropy at individual loci.ConclusionsThis study demonstrates the utility of HS rats for investigating the genetics of obesity-related traits across institutions and identify several candidate genes for future functional testing.

Many personality traits are influenced by genetic factors. Rodents models provide an efficient system for analyzing genetic contribution to these traits. Using 1,246 adolescent heterogeneous stock (HS) male and female rats, we conducted a genome-wide association study (GWAS) of behaviors measured in an open field, including locomotion, novel object interaction, and social interaction. We identified 30 genome-wide significant quantitative trait loci (QTL). Using multiple criteria, including the presence of high impact genomic variants and co-localization of cis-eQTL, we identified 13 candidate genes (Adarb2, Ankrd26, Cacna1c, Clock, Crhr1, Ctu2, Cyp26b1, Eva1a, Fam114a1, Kcnj9, Mlf2, Rab27b, Sec11a) for these traits. Most of these genes have been implicated by human GWAS of various psychiatric traits. For example, Cacna1c, a gene known to be critical for social behavior in rodents and implicated in human schizophrenia and bipolar disorder, is a candidate gene for distance to the social zone. In addition, the QTL region for total distance to the novel object zone, on Chr1 at 144 Mb, is syntenic to a hotspot on human Chr15 (82.5-90.8 Mb) that contains 14 genes associated with psychiatric or substance abuse traits. Although some of the genes identified by this study appear to replicate findings from prior human GWAS, others likely represent novel findings that can be the catalyst for future molecular and genetic insights into human psychiatric diseases. Together, these findings provide strong support for the use of the HS population to study psychiatric disorders.

Many personality traits are influenced by genetic factors. Rodents models provide an efficient system for analyzing genetic contribution to these traits. Using 1,246 adolescent heterogeneous stock (HS) male and female rats, we conducted a genome-wide association study (GWAS) of behaviors measured in an open field, including locomotion, novel object interaction, and social interaction. We identified 30 genome-wide significant quantitative trait loci (QTL). Using multiple criteria, including the presence of high impact genomic variants and co-localization of cis-eQTL, we identified 17 candidate genes (Adarb2, Ankrd26, Cacna1c, Cacng4, Clock, Ctu2, Cyp26b1, Dnah9, Gda, Grxcr1, Eva1a, Fam114a1, Kcnj9, Mlf2, Rab27b, Sec11a, and Ube2h) for these traits. Many of these genes have been implicated by human GWAS of various psychiatric or drug abuse related traits. In addition, there are other candidate genes that likely represent novel findings that can be the catalyst for future molecular and genetic insights into human psychiatric diseases. Together, these findings provide strong support for the use of the HS population to study psychiatric disorders.

The development of model systems requires a detailed assessment of standing genetic variation across natural populations. The Brachypodium species complex has been promoted as a plant model for grass genomics with translation to small grain and biomass crops. To capture the genetic diversity within this species complex, thousands of Brachypodium accessions from around the globe were collected and genotyped by sequencing. Overall, 1897 samples were classified into two diploid or allopolyploid species, and then further grouped into distinct inbred genotypes. A core set of diverse B. distachyon diploid lines was selected for whole genome sequencing and high resolution phenotyping. Genome-wide association studies across simulated seasonal environments was used to identify candidate genes and pathways tied to key life history and agronomic traits under current and future climatic conditions. A total of 8, 22, and 47 QTL were identified for flowering time, early vigor, and energy traits, respectively. The results highlight the genomic structure of the Brachypodium species complex, and the diploid lines provided a resource that allows complex trait dissection within this grass model species.

Major depressive disorder (MDD) is a complex illness caused by both genetic and environmental factors. Antidepressant resistance also has a genetic component. To date, however, very few genes have been identified for major depression or antidepressant resistance. In this study, we investigated whether outbred heterogeneous stock (HS) rats would be a suitable model to uncover the genetics of depression and its connection to antidepressant resistance. The Wistar Kyoto (WKY) rat, one of the eight founders of the HS, is a recognized animal model of juvenile depression and is resistant to fluoxetine antidepressant treatment. We therefore hypothesized that adolescent HS rats would exhibit variation in both despair-like behavior and response to fluoxetine treatment. We assessed heritability of despair-like behavior and response to sub-acute fluoxetine using a modified forced swim test (FST) in 4-week-old HS rats. We also tested whether blood transcript levels previously identified as depression biomarkers in adolescent human subjects are differentially expressed in HS rats with high vs. low FST immobility. We demonstrate heritability of despair-like behavior in 4-week-old HS rats and show that many HS rats are resistant to fluoxetine treatment. In addition, blood transcript levels of Amfr, Cdr2 and Kiaa1539, genes previously identified in human adolescents with MDD, are differentially expressed between HS rats with high vs. low immobility. These data demonstrate that FST despair-like behavior will be amenable to genetic fine-mapping in adolescent HS rats. The overlap between human and HS blood biomarkers suggest that these studies may translate to depression in humans.

Plant respiration can theoretically be fueled by and dependent upon an array of central metabolism components; however, which ones are responsible for the quantitative variation found in respiratory rates is unknown. Here, large-scale screens revealed 2-fold variation in nighttime leaf respiration rate (RN) among mature leaves from an Arabidopsis (Arabidopsis thaliana) natural accession collection grown under common favorable conditions. RN variation was mostly maintained in the absence of genetic variation, which emphasized the low heritability of RN and its plasticity toward relatively small environmental differences within the sampling regime. To pursue metabolic explanations for leaf RN variation, parallel metabolite level profiling and assays of total protein and starch were performed. Within an accession, RN correlated strongly with stored carbon substrates, including starch and dicarboxylic acids, as well as sucrose, major amino acids, shikimate, and salicylic acid. Among different accessions, metabolite-RN correlations were maintained with protein, sucrose, and major amino acids but not stored carbon substrates. A complementary screen of the effect of exogenous metabolites and effectors on leaf RN revealed that (1) RN is stimulated by the uncoupler FCCP and high levels of substrates, demonstrating that both adenylate turnover and substrate supply can limit leaf RN, and (2) inorganic nitrogen did not stimulate RN, consistent with limited nighttime nitrogen assimilation. Simultaneous measurements of RN and protein synthesis revealed that these processes were largely uncorrelated in mature leaves. These results indicate that differences in preceding daytime metabolic activities are the major source of variation in mature leaf RN under favorable controlled conditions.

Field-grown plants have variable exposure to sunlight as a result of shifting cloud-cover, seasonal changes, canopy shading, and other environmental factors. As a result, they need to have developed a method for dissipating excess energy obtained from periodic excessive sunlight exposure. Non-photochemical quenching (NPQ) dissipates excess energy as heat, however the physical and molecular genetic mechanics of NPQ variation are not understood. In this study, we investigated the genetic loci involved in NPQ by first growing different Arabidopsis thaliana accessions in local and seasonal climate conditions, then measured their NPQ kinetics through development by chlorophyll fluorescence. We used genome-wide association studies (GWAS) to identify 15 significant quantitative trait loci (QTL) for a range of photosynthetic traits, including a QTL co-located with known NPQ gene PSBS (AT1G44575). We found there were large alternative regulatory segments between the PSBS promoter regions of the functional haplotypes and a significant difference in PsbS protein concentration. These findings parallel studies in rice showing recurrent regulatory evolution of this gene. The variation in the PSBS promoter and the changes underlying other QTLs could give insight to allow manipulations of NPQ in crops to improve their photosynthetic efficiency and yield.B.P. & J.B. conceived the project; B.P., J.B., P.W. and T.R. designed the research plan and analysis; P.W. supervised the experiments; T.R. performed most of the experiments and analysis; P.G., T.S., A.A. & E.A. designed and undertook experimental design, experiments and analysis for Figure 4; R.C. did the GWAS analysis; P.W., T.R. & A.A. wrote the article with contributions of all the authors.

ObjectiveObesity is influenced by genetic and environmental factors. Despite success of human genome wide association studies (GWAS), the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify genetic loci underlying obesity and related morphometric and metabolic traits.MethodsWe measured obesity-relevant traits including body weight, body length, body mass index, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat GWAS to date. Genetic loci were identified using a linear mixed model that accounted for the complex family relationships of the HS and covariate to account for differences among the three phenotyping centers.ResultsWe identified 32 independent loci, several of which contained only a single gene (e.g. Epha5, Nrg1 and Klhl14) or obvious candidate genes (Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity-related traits, and extensive pleiotropy at individual loci.ConclusionsThese studies demonstrate utility of HS rats for investigating the genetics of obesity related traits across institutions and identify several candidate genes for future functional testing.

Replication is considered to be critical for genome-wide association studies (GWAS) in humans, but is not routinely performed in model organisms. We explored replication using an advanced intercross line (AIL) which is the simplest possible multigenerational intercross. We re-genotyped a previously published cohort of LG/J x SM/J AIL mice (F34; n=428) using a denser marker set and also genotyped a novel cohort of AIL mice (F39-43; n=600) for the first time. We identified 110 significant loci in the F34cohort, 36 of which were new discoveries attributable to the denser marker set; we also identified 27 novel significant loci in the F39-43cohort. For traits measured in both cohorts (locomotor activity, body weight, and coat color), the genetic correlations were high, although, the F39-43cohort showed systematically lower SNP-heritability estimates. We then attempted to replicate loci identified in either F34or F39-43in the other cohort. Albino coat color was robustly replicated; we observed only partial replication of associations for locomotor activity and body weight. Finally, we performed a mega-analysis of locomotor activity and body weight by combining F34and F39-43cohorts (n=1,028), which identified four novel loci. The incomplete replication was inconsistent with simulations we performed to estimate our power to replicate. This may reflect: 1) false positives errors in the discovery cohort, 2) environmental or genetic heterogeneity between the two samples, or 3) the systematic over estimation of the effect sizes at significant loci (“Winner’s Curse”). Our results demonstrate that it is difficult to replicate GWAS results even when using similarly sized discovery and replication cohorts drawn from the same population.

The development of model systems requires a detailed assessment of standing genetic variation across natural populations. TheBrachypodiumspecies complex has been promoted as a plant model for grass genomics with translational to small grain and biomass crops. To capture the genetic diversity within this species complex, thousands ofBrachypodiumaccessions from around the globe were collected and sequenced using genotyping by sequencing (GBS). Overall, 1,897 samples were classified into two diploid or allopolyploid species and then further grouped into distinct inbred genotypes. A core set of diverseB. distachyondiploid lines were selected for whole genome sequencing and high resolution phenotyping. Genome-wide association studies across simulated seasonal environments was used to identify candidate genes and pathways tied to key life history and agronomic traits under current and future climatic conditions. A total of 8, 22 and 47 QTLs were identified for flowering time, early vigour and energy traits, respectively. Overall, the results highlight the genomic structure of theBrachypodiumspecies complex and allow powerful complex trait dissection within this new grass model species.

Muscle fiber cross-sectional area (CSA) and proportion of different fiber types are important determinants of muscle function and overall metabolism. Genetic variation plays a substantial role in phenotypic variation of these traits; however, the underlying genes remain poorly understood. This study aimed to map quantitative trait loci (QTL) affecting differences in soleus muscle fiber traits between the LG/J and SM/J mouse strains. Fiber number, CSA, and proportion of oxidative type I fibers were assessed in the soleus of 334 genotyped female and male mice of the F34generation of advanced intercross lines (AIL) derived from the LG/J and SM/J strains. To increase the QTL detection power, these data were combined with 94 soleus samples from the F2intercross of the same strains. Transcriptome of the soleus muscle of LG/J and SM/J females was analyzed by microarray. Genome-wide association analysis mapped four QTL (genome-wide P < 0.05) affecting the properties of muscle fibers to chromosome 2, 3, 4, and 11. A 1.5-LOD QTL support interval ranged between 2.36 and 4.67 Mb. On the basis of the genomic sequence information and functional and transcriptome data, we identified candidate genes for each of these QTL. The combination of analyses in F2and F34AIL populations with transcriptome and genomic sequence data in the parental strains is an effective strategy for refining QTL and nomination of the candidate genes.

A major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed method is very useful for achieving optimal statistical power for QTL identification and for disclosing the most relevant traits. It is also a practical strategy to effectively take advantage of multitrait analysis when the number of traits under consideration is too large, making the usual multivariate analysis of all traits challenging. We study the impact of selection bias and the usage of permutation tests in the context of variable selection and develop a powerful implementation procedure of variable selection for genome scanning. We demonstrate the proposed method and selection procedure in a backcross population, using both simulated and real data. The extension to other experimental mapping populations is straightforward.

The rewarding property of opioids likely contributes to their abuse potential. Therefore, determining the genetic basis of opioid reward could aid in understanding the neurobiological mechanisms of opioid addiction, provided that it is a heritable trait. Here, we characterized the rewarding property of the widely abused prescription opioid oxycodone (OXY) in the conditioned place preference (CPP) assay using LG/J and SM/J parental inbred mouse strains and 17 parent-offspring families of a LG/J × SM/J F47 /F48 advanced intercross line (AIL). Following OXY training (5 mg/kg, i.p.), SM/J mice and AIL mice, but not LG/J mice, showed an increase in preference for the OXY-paired side, suggesting a genetic basis for OXY-CPP. SM/J mice showed greater locomotor activity than LG/J mice in response to both saline and OXY. LG/J, SM/J, and AIL mice all exhibited robust OXY-induced locomotor sensitization. Narrow-sense heritability (h(2) ) estimates of the phenotypes using linear regression and maximum likelihood estimation showed good agreement (r = 0.91). OXY-CPP was clearly not a heritable trait whereas drug-free- and OXY-induced locomotor activity and sensitization were significantly and sometimes highly heritable (h(2) = 0.30-0.84). Interestingly, the number of transitions between the saline- and OXY-paired sides emerged as a reliably heritable trait following OXY training (h(2) = 0.46-0.66) and could represent a genetic component of drug-seeking behavior. Thus, although OXY-CPP does not appear to be amenable to genome-wide quantitative trait locus mapping, this protocol will be useful for mapping other traits potentially relevant to opioid abuse.

The JAX Diversity Outbred population is a new mouse resource derived from partially inbred Collaborative Cross strains and maintained by randomized outcrossing. As such, it segregates the same allelic variants as the Collaborative Cross but embeds these in a distinct population architecture in which each animal has a high degree of heterozygosity and carries a unique combination of alleles. Phenotypic diversity is striking and often divergent from phenotypes seen in the founder strains of the Collaborative Cross. Allele frequencies and recombination density in early generations of Diversity Outbred mice are consistent with expectations based on simulations of the mating design. We describe analytical methods for genetic mapping using this resource and demonstrate the power and high mapping resolution achieved with this population by mapping a serum cholesterol trait to a 2-Mb region on chromosome 3 containing only 11 genes. Analysis of the estimated allele effects in conjunction with complete genome sequence data of the founder strains reduced the pool of candidate polymorphisms to seven SNPs, five of which are located in an intergenic region upstream of the Foxo1 gene.

Genetic variation plays a substantial role in variation in strength, but the underlying mechanisms remain poorly understood. The objective of the present study was to examine the mechanisms underlying variation in muscle mass, a predictor of strength, between LG/J and SM/J strains, which are the inbred progeny of mice selected, respectively, for high and low body weight. We measured weight of five hindlimb muscles in LG/J and SM/J males and females, in F1and F2intercrosses, and in an advanced intercross (AI), F34, between the two. F2mice were genotyped using 162 SNPs throughout the genome; F34mice were genotyped at 3,015 SNPs. A twofold difference in muscle mass between the LG/J and SM/J mouse strains was observed. Integrated genome-wide association analysis in the combined population of F2and AI identified 22 quantitative trait loci (QTL; genome-wide P < 0.05) affecting muscle weight on Chr 2 (2 QTL), 4, 5, 6 (7 QTL), 7 (4 QTL), 8 (4 QTL), and 11 (3 QTL). The LG/J allele conferred greater muscle weight in all cases. The 1.5-LOD QTL support intervals ranged between 0.3 and 13.4 Mb (median 3.7 Mb) restricting the list of candidates to between 5 and 97 genes. Selection for body weight segregated the alleles affecting skeletal muscle, the most abundant tissue in the body. Combination of analyses in an F2and AI was an effective strategy to detect and refine the QTL in a genome-wide manner. The achieved resolution facilitates further elucidation of the underlying genetic mechanisms affecting muscle mass.

Model organisms offer many advantages for the genetic analysis of complex traits. However, identification of specific genes is often hampered by a lack of recombination between the genomes of inbred progenitors. Recently, genome-wide association studies (GWAS) in humans have offered gene-level mapping resolution that is possible because of the large number of accumulated recombinations among unrelated human subjects. To obtain analogous improvements in mapping resolution in mice, we used a 34th generation advanced intercross line (AIL) derived from two inbred strains (SM/J and LG/J). We used simulations to show that familial relationships among subjects must be accounted for when analyzing these data; we then used a mixed model that included polygenic effects to address this problem in our own analysis. Using a combination of F2 and AIL mice derived from the same inbred progenitors, we identified genome-wide significant, subcentimorgan loci that were associated with methamphetamine sensitivity, (e.g., chromosome 18; LOD = 10.5) and non-drug-induced locomotor activity (e.g., chromosome 8; LOD = 18.9). The 2-LOD support interval for the former locus contains no known genes while the latter contains only one gene (Csmd1). This approach is broadly applicable in terms of phenotypes and model organisms and allows GWAS to be performed in multigenerational crosses between and among inbred strains where familial relatedness is often unavoidable.

Psychostimulant addiction is a heritable substance use disorder; however its genetic basis is almost entirely unknown. Quantitative trait locus (QTL) mapping in mice offers a complementary approach to human genome-wide association studies and can facilitate environment control, statistical power, novel gene discovery, and neurobiological mechanisms. We used interval-specific congenic mouse lines carrying various segments of chromosome 11 from the DBA/2J strain on an isogenic C57BL/6J background to positionally clone a 206 kb QTL (50,185,512–50,391,845 bp) that was causally associated with a reduction in the locomotor stimulant response to methamphetamine (2 mg/kg, i.p.; DBA/2J < C57BL/6J)—a non-contingent, drug-induced behavior that is associated with stimulation of the dopaminergic reward circuitry. This chromosomal region contained only two protein coding genes—heterogeneous nuclear ribonucleoprotein, H1 (Hnrnph1) and RUN and FYVE domain-containing 1 (Rufy1). Transcriptome analysis via mRNA sequencing in the striatum implicated a neurobiological mechanism involving a reduction in mesolimbic innervation and striatal neurotransmission. For instance, Nr4a2 (nuclear receptor subfamily 4, group A, member 2), a transcription factor crucial for midbrain dopaminergic neuron development, exhibited a 2.1-fold decrease in expression (DBA/2J < C57BL/6J; p 4.2 x 10−15). Transcription activator-like effector nucleases (TALENs)-mediated introduction of frameshift deletions in the first coding exon of Hnrnph1, but not Rufy1, recapitulated the reduced methamphetamine behavioral response, thus identifying Hnrnph1 as a quantitative trait gene for methamphetamine sensitivity. These results define a novel contribution of Hnrnph1 to neurobehavioral dysfunction associated with dopaminergic neurotransmission. These findings could have implications for understanding the genetic basis of methamphetamine addiction in humans and the development of novel therapeutics for prevention and treatment of substance abuse and possibly other psychiatric disorders., Author Summary Both genetic and environmental factors can powerfully modulate susceptibility to substance use disorders. Quantitative trait locus (QTL) mapping is an unbiased discovery-based approach that is used to identify novel genetic factors and provide new mechanistic insight into phenotypic variation associated with disease. In this study, we focused on the genetic basis of variation in sensitivity to the acute locomotor stimulant response to methamphetamine which is a behavioral phenotype in rodents that is associated with stimulated dopamine release and activation of the brain reward circuitry involved in addiction. Using brute force monitoring of recombination events associated with changes in behavior, we fortuitously narrowed the genotype-phenotype association down to just two genes that we subsequently targeted using a contemporary genome editing approach. The gene that we validated–Hnrnph1 –is an RNA binding protein that did not have any previously known function in psychostimulant behavior or psychostimulant addiction. Our behavioral data combined with our gene expression results provide a compelling rationale for a new line of investigation regarding Hnrnph1 and its role in neural development and plasticity associated with the addictions and perhaps other dopamine-dependent psychiatric disorders.

Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for

The genes underlying variation in skeletal muscle mass are poorly understood. Although many quantitative trait loci (QTLs) have been mapped in crosses of mouse strains, the limited resolution inherent in these conventional studies has made it difficult to reliably pinpoint the causal genetic variants. The accumulated recombination events in an advanced intercross line (AIL), in which mice from two inbred strains are mated at random for several generations, can improve mapping resolution. We demonstrate these advancements in mapping QTLs for hindlimb muscle weights in an AIL ( n = 832) of the C57BL/6J (B6) and DBA/2J (D2) strains, generations F8–F13. We mapped muscle weight QTLs using the high-density MegaMUGA SNP panel. The QTLs highlight the shared genetic architecture of four hindlimb muscles and suggest that the genetic contributions to muscle variation are substantially different in males and females, at least in the B6D2 lineage. Out of the 15 muscle weight QTLs identified in the AIL, nine overlapped the genomic regions discovered in an earlier B6D2 F2 intercross. Mapping resolution, however, was substantially improved in our study to a median QTL interval of 12.5 Mb. Subsequent sequence analysis of the QTL regions revealed 20 genes with nonsense or potentially damaging missense mutations. Further refinement of the muscle weight QTLs using additional functional information, such as gene expression differences between alleles, will be important for discerning the causal genes.

Climate change has altered life history events in many plant species; however, little is known about genetic variation underlying seasonal thermal response. In this study, we simulated current and three future warming climates and measured flowering time across a globally diverse set of Arabidopsis thaliana accessions. We found that increased diurnal and seasonal temperature (1°–3°) decreased flowering time in two fall cohorts. The early fall cohort was unique in that both rapid cycling and overwintering life history strategies were revealed; the proportion of rapid cycling plants increased by 3–7% for each 1° temperature increase. We performed genome-wide association studies (GWAS) to identify the underlying genetic basis of thermal sensitivity. GWAS identified five main-effect quantitative trait loci (QTL) controlling flowering time and another five QTL with thermal sensitivity. Candidate genes include known flowering loci; a cochaperone that interacts with heat-shock protein 90; and a flowering hormone, gibberellic acid, a biosynthetic enzyme. The identified genetic architecture allowed accurate prediction of flowering phenotypes (R2 > 0.95) that has application for genomic selection of adaptive genotypes for future environments. This work may serve as a reference for breeding and conservation genetic studies under changing environments.

Agriculture requires a second green revolution to provide increased food, fodder, fiber, fuel and soil fertility for a growing population while being more resilient to extreme weather on finite land, water, and nutrient resources. Advances in phenomics, genomics and environmental control/sensing can now be used to directly select yield and resilience traits from large collections of germplasm if software can integrate among the technologies. Traits could be Captured throughout development and across environments from multi-dimensional phenotypes, by applying Genome Wide Association Studies (GWAS) to identify causal genes and background variation and functional structural plant models (FSPMs) to predict plant growth and reproduction in target environments. TraitCapture should be applicable to both controlled and field environments and would allow breeders to simulate regional variety trials to pre-select for increased productivity under challenging environments.

We used simulations to evaluate methods for assessing statistical significance in association studies. When the statistical model appropriately accounted for relatedness among individuals, unrestricted permutation tests and a few other simulation-based methods effectively controlled type I error rates; otherwise, only gene dropping controlled type I error but at the expense of statistical power.

Fear conditioning (FC) may provide a useful model for some components of post-traumatic stress disorder (PTSD). We used a C57BL/6J × DBA/2J F(2) intercross (n = 620) and a C57BL/6J × DBA/2J F(8) advanced intercross line (n = 567) to fine-map quantitative trait loci (QTL) associated with FC. We conducted an integrated genome-wide association analysis in QTLRel and identified five highly significant QTL affecting freezing to context as well as four highly significant QTL associated with freezing to cue. The average percent decrease in QTL width between the F(2) and the integrated analysis was 59.2%. Next, we exploited bioinformatic sequence and expression data to identify candidate genes based on the existence of non-synonymous coding polymorphisms and/or expression QTLs. We identified numerous candidate genes that have been previously implicated in either fear learning in animal models (Bcl2, Btg2, Dbi, Gabr1b, Lypd1, Pam and Rgs14) or PTSD in humans (Gabra2, Oprm1 and Trkb); other identified genes may represent novel findings. The integration of F(2) and AIL data maintains the advantages of studying FC in model organisms while significantly improving resolution over previous approaches.

Sensitivity to the locomotor stimulant effects of methamphetamine is a heritable trait that utilizes neurocircuitry also associated with the rewarding effects of drugs. We used the power of a C57BL/6J x DBA/2J F2 intercross (n = 676) and the precision of a C57BL/6J x DBA/2J F8 advanced intercross line (Aap: B6, D2 – G8; or F8 AIL; n = 552) to identify and narrow quantitative trait loci (QTL) associated with sensitivity to the locomotor stimulant effects of methamphetamine. We used the program QTLRel to simultaneously map QTL in the F2 and F8 AIL mice. We identified six genome-wide significant QTLs associated with locomotor activity at baseline and seven genome-wide significant QTLs associated with methamphetamine induced locomotor activation. The average percent decrease in QTL width between the F2 and the integrated analysis was 65%. Additionally, these QTLs showed a distinct temporal specificity within each session that allowed us to further refine their locations, and identify one QTL with a 1.8-LOD support interval = 1.47 Mb. Next, we utilized publicly available bioinformatics resources to exploit strain-specific sequence data and strain- and region-specific expression data to identify candidate genes. These results illustrate the power of AILs in conjunction with sequence and gene expression data to investigate the genetic underpinnings of behavioral and other traits.

Red blood cells are essential for oxygen transport and other physiologic processes. Red cell characteristics are typically determined by complete blood counts which measure parameters such as hemoglobin levels and mean corpuscular volumes; these parameters reflect the quality and quantity of red cells in the circulation at any particular moment. To identify the genetic determinants of red cell parameters, we performed genome-wide association analysis on LG/J × SM/J F2 and F34 advanced intercross lines using single nucleotide polymorphism genotyping and a novel algorithm for mapping in the combined populations. We identified significant quantitative trait loci for red cell parameters on chromosomes 6, 7, 8, 10, 12, and 17; our use of advanced intercross lines reduced the quantitative trait loci interval width from 1.6- to 9.4-fold. Using the genomic sequences of LG/J and SM/J mice, we identified nonsynonymous coding single nucleotide polymorphisms in candidate genes residing within quantitative trait loci and performed sequence alignments and molecular modeling to gauge the potential impact of amino acid substitutions. These results should aid in the identification of genes critical for red cell physiology and metabolism and demonstrate the utility of advanced intercross lines in uncovering genetic determinants of inherited traits.

The present study measured variation in body weight using a combined analysis in an F(2) intercross and an F(34) advanced intercross line (AIL). Both crosses were derived from inbred LG/J and SM/J mice, which were selected for large and small body size prior to inbreeding. Body weight was measured at 62 (± 5) days of age. Using an integrated GWAS and forward model selection approach, we identified 11 significant QTLs that affected body weight on ten different chromosomes. With these results we developed a full model that explained over 18% of the phenotypic variance. The median 1.5-LOD support interval was 5.55 Mb, which is a significant improvement over most prior body weight QTLs. We identified nonsynonymous coding SNPs between LG/J and SM/J mice in order to further narrow the list of candidate genes. Three of the genes with nonsynonymous coding SNPs (Rad23b, Stk33, and Anks1b) have been associated with adiposity, waist circumference, and body mass index in human GWAS, thus providing evidence that these genes may underlie our QTLs. Our results demonstrate that a relatively small number of loci contribute significantly to the phenotypic variance in body weight, which is in marked contrast to the situation in humans. This difference is likely to be the result of strong selective pressure and the simplified genetic architecture, both of which are important advantages of our system.

Background Existing software for quantitative trait mapping is either not able to model polygenic variation or does not allow incorporation of more than one genetic variance component. Improperly modeling the genetic relatedness among subjects can result in excessive false positives. We have developed an R package, QTLRel, to enable more flexible modeling of genetic relatedness as well as covariates and non-genetic variance components. Results We have successfully used the package to analyze many datasets, including F34 body weight data that contains 688 individuals genotyped at 3105 SNP markers and identified 11 QTL. It took 295 seconds to estimate variance components and 70 seconds to perform the genome scan on an Linux machine equipped with a 2.40GHz Intel(R) Core(TM)2 Quad CPU. Conclusions QTLRel provides a toolkit for genome-wide association studies that is capable of calculating genetic incidence matrices from pedigrees, estimating variance components, performing genome scans, incorporating interactive covariates and genetic and non-genetic variance components, as well as other functionalities such as multiple-QTL mapping and genome-wide epistasis.

Intramuscular fat content and water-holding capacity are important traits in livestock as they influence meat quality, nutritive value of the muscle, and animal health. As a model for livestock, two inbred lines of the Berlin Muscle Mouse population, which had been long-term selected for high muscle mass, were used to identify genomic regions affecting intramuscular fat content and water-holding capacity. The intramuscular fat content of the Musculus longissimus was on average 1.4 times higher in BMMI806 than in BMMI816 mice. This was accompanied by a 1.5 times lower water-holding capacity of the Musculus quadriceps in BMMI816 mice. Linkage analyses with 332 G(3) animals of reciprocal crosses between these two lines revealed quantitative trait loci for intramuscular fat content on chromosome 7 and for water-holding capacity on chromosome 2. In part, the identified loci coincide with syntenic regions in pigs in which genetic effects for the same traits were found. Therefore, these muscle-weight-selected mouse lines and the produced intercross populations are valuable genetic resources to identify genes that could also contribute to meat quality in other species.

Monitoring the photosynthetic performance of plants is a major key to understanding how plants adapt to their growth conditions. Stress tolerance traits have a high genetic complexity as plants are constantly, and unavoidably, exposed to numerous stress factors, which limits their growth rates in the natural environment. Arabidopsis thaliana, with its broad genetic diversity and wide climatic range, has been shown to successfully adapt to stressful conditions to ensure the completion of its life cycle. As a result, A. thaliana has become a robust and renowned plant model system for studying natural variation and conducting gene discovery studies. Genome wide association studies (GWAS) in restructured populations combining natural and recombinant lines is a particularly effective way to identify the genetic basis of complex traits. As most abiotic stresses affect photosynthetic activity, chlorophyll fluorescence measurements are a potential phenotyping technique for monitoring plant performance under stress conditions. This review focuses on the use of chlorophyll fluorescence as a tool to study genetic variation underlying the stress tolerance responses to abiotic stress in A. thaliana.