Over one billion people worldwide suffer from obesity, and the number is continually rising. This epidemic is partly caused by the modern lifestyle. Animal models, especially mouse models, are crucial to identifying the genetic components of complex disorders and exploring the potential applications of these genetic findings. The body weight of the animals used in research is often measured regularly to monitor their health. Only endpoint measurements, such as ultimate body weight, are frequently examined in quantitative trait locus (QTL) studies; time series data, including weekly or biweekly body weight, are usually disregarded. QTL mapping using biweekly body weight measurements may be particularly intriguing in examining body weight gain in obesity research and identifying more genes associated with obesity and related metabolic disorders. This study is focused on identifying quantitative trait loci (QTLs) underlying body weight changes by analyzing biweekly weight measurements in collaborative cross (CC) mice maintained on a high-fat diet for 12 weeks. QTL analysis, utilizing 525 mice from 55 CC lines (308 males and 217 females), revealed genome-wide significant QTLs on different chromosomes for body weight changes over 12 weeks. This study unveiled 62 body weight QTLs, among which 28 novel QTLs associated with defined traits were observed and found not reported previously. In addition, 34 more QTLs were fine-mapped, as the genomic interval positions of these had been previously identified. These findings highlight genomic regions that influence body weight in CC mice, underscoring the value of time series data in identifying novel genetic factors.

Background Obesity is caused by genetics, the environment (e.g., diet) and their interactions. The brain, specifically the hypothalamus, plays an important role in obesity, but controlled studies using human brain tissue are not possible. The goal of this study was to conduct a genome wide association study using outbred heterogeneous stock (HS) rats to map genetic loci associated with diet-induced obesity. This was followed by RNAseq in the ventromedial hypothalamus (VMH) to identify candidate causal obesity genes. Methods We measured multiple metabolic traits (including fat and lean mass, fasting glucose, insulin, and lipids, glucose tolerance, food intake and activity levels) after long-term consumption of a low-fat (LFD) or high-fat diet (HFD) in 2000 HS rats, split equally by sex and diet. Rats were genotyped using low-coverage whole genome sequencing. RNAseq data was obtained from the VMH of a sub-set of 400 HS rats split equally by sex and diet. We used linear mixed models to detect physiological and expression quantitative trait loci (pQTLs and eQTLs, respectively) in the full dataset and separately by diet and sex. Genes with cis-eQTLs that overlapped pQTLs were assessed as candidate causal genes through mediation analysis. We also identified VMH genes differentially expressed by diet in both sexes, followed by pathway analysis. Results We identified 47 pQTLs where six mapped multiple traits, 11 were diet-specific and 13 were sex-specific. We identified nine candidate causal genes, including Pcare , Rbks, Mpv17 and Gpn1 within a pleiotropic pQTL for multiple adiposity traits on rat chr. 6 and Tnsfs9 within a pQTL for fat pad weight on rat chr. 9. We also identified Ccdc77 as a candidate gene within a LFD-specific pQTL for activity levels and Rtel1 and Polr3k within a male-specific pQTL for fatty liver. Genes involved in extracellular matrix and inflammation were dysregulated by diet, particularly in females, while males on HFD showed upregulation of several addiction pathways. Conclusions We identified novel candidate genes as neural regulators of diet-induced obesity and related traits and confirmed the importance of accounting for diet and sex in genetic studies of obesity.

Background: We investigated the flow of information from genome sequence to protein expression implied by the Central Dogma, to determine the impact of intermediate genomic levels in plants. Results: We performed genomic profiling of rosettes in two Arabidopsis accessions, Col-0 and Can-0, and assembled their genomes using long reads and chromatin interaction data. We measured gene and protein expression in biological replicates grown in a controlled environment, also measuring CpG methylation, ribosome-associated transcript levels and tRNA abundance. Each omic level is highly reproducible between biological replicates and between accessions despite their 0.5% sequence divergence; the single best predictor of any level in one accession is the corresponding level in the other. Within each accession, gene codon frequencies accurately model both mRNA and protein expression. The effects of a codon on mRNA and protein expression are highly correlated but are unrelated to genome-wide codon frequencies or to tRNA levels which instead match genome-wide amino acid frequencies. Ribosome-associated transcripts closely track mRNA levels. Conclusions: In the absence of environmental perturbation, neither methylation, tRNA nor ribosome-associated transcript levels add appreciable information about constitutive protein abundance beyond that in DNA codon frequencies and mRNA expression levels. The impact of constitutive gbM is mostly explained by gene codon composition. tRNA abundance tracks overall amino acid demand. However, genetic differences between accessions associate with differential gbM by inflating differential expression variation. Our data show that the Central Dogma holds only if both sequence and abundance information in mRNA are considered. ### Competing Interest Statement The authors have declared no competing interest.

We investigate the flow of genetic information from DNA to RNA to protein as described by the Central Dogma in molecular biology, to determine the impact of intermediate genomic levels on plant protein expression. We perform genomic profiling of rosette leaves in two Arabidopsis accessions, Col-0 and Can-0, and assemble their genomes using long reads and chromatin interaction data. We measure gene and protein expression in biological replicates grown in a controlled environment, also measuring CpG methylation, ribosome-associated transcript levels, and tRNA abundance. Each omic level is highly reproducible between biological replicates and between accessions despite their 1

Global food demand is expected to increase between 55 and 70% by 2050. Plant breeders and geneticists are constantly under pressure to develop high-yielding climate-resilient varieties using novel approaches. The quest for simplifying complex traits and efforts for developing high-yielding varieties during the twenty-first century led to a paradigm shift from phenotypic-based selection to genome-based breeding. On one hand, the development and utilization of diverse genetic resources, and advances in genomics on the other hand provided a kick start for the understanding the genetics of economically important complex traits at a faster pace. Further, the next-generation sequencing revolutionized our understanding of the genome architecture. As a result, there has been an increasing demand for statistical and bioinformatics tools to analyse and manage the enormous amount of data generated from sequencing of genomes, transcriptomes, proteome and metabolomes. In this chapter, we review the intervention of bioinformatics and computational tools for deploying the tremendous wealth of data for plant genetics and breeding research.

Plants are attacked by diverse herbivorous pests with different host specializations. While host plant resistance influences pest pressure, how resistance impacts the behaviors of generalist and specialist herbivores, and the relationship to resistance, is less well known. Here, we investigated the short-term (< 1 h) behavioral changes of a generalist herbivore, the two-spotted spider mite (TSM), and a specialist herbivore, the Banks grass mite (BGM), after introduction to no-choice Tanglefoot leaf-arenas (2 × 2 cm) of three maize inbred lines (B73, B75, and B96). The widely-used inbred line B73 is susceptible to spider mites, while B75 and B96 are known to be mite resistant, especially to TSM. Video tracking was used to record TSM and BGM walking, probing, feeding, resting, web-building and travel distance on arenas of each line. Mite oviposition was also recorded after 72 h. B75, a resistant line, decreased the feeding behavior (i.e., time) of both mite species compared to B73 (susceptible control) and B96. Moreover, TSM appeared to be sensitive to both resistant lines (B75 and B96) with reduced oviposition, and increased resting and web-building times compared to susceptible B73. In contrast, the specialist BGM showed no difference in oviposition, resting and web-building time across all maize inbred lines. Our findings of quite broad and short-term responses of TSM to B75 and B96 are consistent with a role for constitutive or rapidly induced plant defenses in maize in conferring TSM resistance. Other mechanisms of plant resistance may be needed, however, for defense against specialists like BGM.

Abstract Background Over one billion people worldwide suffer from obesity, and the number is continually rising. This epidemic is partly caused by the modern lifestyle, which is defined by excessive eating of food high in calories and little physical activity. However, genetic variation sets the stage and affects how the disease develops and advances. Animal models, especially mice models, are crucial to identifying the genetic components of complex disorders and exploring the potential applications of these genetic findings. The body weight of the animals used in research is often measured regularly to monitor their health. Only endpoint measurements, like ultimate body weight, are often examined in quantitative trait locus (QTL) studies; time series data, such as weekly or biweekly body weight, are usually disregarded. QTL mapping utilizing biweekly body weight measurements may be particularly intriguing in examining body weight gain in obesity research and identifying more genes connected with obesity and related metabolic problems. Results This study is focused on identifying QTL underlying body weight changes by analyzing biweekly weight measurements in collaborative cross (CC) mice maintained on a high-fat diet for 12 weeks. QTL analysis, utilizing 525 mice from 55 CC lines (308 male and 217 female), revealed genome-wide significant QTL on different chromosomes for body weight changes over 12 weeks. This study unveiled 62 body weight QTLs, among which 28 novels associated with defined traits were observed and found not reported previously. In addition, 34 more QTLs were fine-mapped as the genomic interval positions of these were previously identified. Conclusions These findings illuminate genomic regions influencing body weight in CC mice and emphasize the utility of time series data in uncovering novel genetic factors.

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

We study natural DNA polymorphisms and associated phenotypes in the Arabidopsis relative Cardamine hirsuta. We observed strong genetic differentiation among several ancestry groups and broader distribution of Iberian relict strains in European C. hirsuta compared to Arabidopsis. We found synchronization between vegetative and reproductive development and a pervasive role for heterochronic pathways in shaping C. hirsuta natural variation. A single, fast-cycling ChFRIGIDA allele evolved adaptively allowing range expansion from glacial refugia, unlike Arabidopsis where multiple FRIGIDA haplotypes were involved. The Azores islands, where Arabidopsis is scarce, are a hotspot for C. hirsuta diversity. We identified a quantitative trait locus (QTL) in the heterochronic SPL9 transcription factor as a determinant of an Azorean morphotype. This QTL shows evidence for positive selection, and its distribution mirrors a climate gradient that broadly shaped the Azorean flora. Overall, we establish a framework to explore how the interplay of adaptation, demography, and development shaped diversity patterns of 2 related plant species.

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

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.

Additional file 3: Table S2. Phenotypic variances with standard errors, due to additive and dominance effects (calculated by genome-wide SNP heritability or from accumulated significant QTLs) of 242 physical traits in F2 pigs (Table S2-1), 206 physical traits of HS rats (Table S2-2) and 124 physical traits of HS mice (Table S2-3), together with average values across each trait class.

Background Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively. Results We systematically investigate both dominance—here representing any non-additive within-locus interaction—and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance. Conclusions Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

Additional file 6: Table S5. Summary of novel pleiotropic QTLs detected by AD model within each population. (Different shared QTL regions are separated by graey background and the rows with red numbers indicate novel QTLs where -log10(P)values of ADvsA Model > 5).

A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables. T2D is regarded as a silent illness that can advance for years before being diagnosed. Finding genetic markers for T2D and metabolic syndrome in groups with similar environmental exposure is therefore essential to understanding the mechanism of such complex characteristic illnesses. So herein, we demonstrated the exclusive use of the collaborative cross (CC) mouse reference population to identify novel quantitative trait loci (QTL) and, subsequently, suggested genes associated with host glucose tolerance in response to a high-fat diet. In this study, we used 539 mice from 60 different CC lines. The diabetogenic effect in response to high-fat dietary challenge was measured by the three-hour intraperitoneal glucose tolerance test (IPGTT) test after 12 weeks of dietary challenge. Data analysis was performed using a statistical software package IBM SPSS Statistic 23. Afterward, blood glucose concentration at the specific and between different time points during the IPGTT assay and the total area under the curve (AUC0-180) of the glucose clearance was computed and utilized as a marker for the presence and severity of diabetes. The observed AUC0-180 averages for males and females were 51,267.5 and 36,537.5 mg/dL, respectively, representing a 1.4-fold difference in favor of females with lower AUC0-180 indicating adequate glucose clearance. The AUC0-180 mean differences between the sexes within each specific CC line varied widely within the CC population. A total of 46 QTL associated with the different studied phenotypes, designated as T2DSL and its number, for Type 2 Diabetes Specific Locus and its number, were identified during our study, among which 19 QTL were not previously mapped. The genomic interval of the remaining 27 QTL previously reported, were fine mapped in our study. The genomic positions of 40 of the mapped QTL overlapped (clustered) on 11 different peaks or close genomic positions, while the remaining 6 QTL were unique. Further, our study showed a complex pattern of haplotype effects of the founders, with the wild-derived strains (mainly PWK) playing a significant role in the increase of AUC values.

Abstract A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables. Diabetes polygenic variants frequently run in families. T2D is regarded as a silent illness that can advance for years before being diagnosed. Numerous metabolic issues are noticed once T2D is diagnosed, either as comorbidities or as side effects. Finding genetic markers for T2D and metabolic syndrome in groups with similar environmental exposure is therefore essential to understanding the mechanism of such complex characteristic illnesses. So herein, we demonstrated the exclusive use of the collaborative cross (CC) mouse reference population to identify novel quantitative trait loci (QTL) and, subsequently, suggested genes associated with host glucose tolerance in response to a high-fat diet. In the current study, we used 539 mice from 60 different CC lines. The diabetogenic effect in response to high-fat dietary challenge was measured by the intraperitoneal glucose tolerance test (IPGTT) after 12 weeks of dietary challenge. Data analysis was performed using a statistical software package IBM SPSS Statistic 23. Using the mouse genome informatics (MGI) database resource enabled us to examine the reported QTL in detail for genetic features related to glucose metabolism. Afterward, the total area under the curve (AUC0-180) of the glucose clearance was computed and utilized as a marker for the presence and severity of diabetes. The observed AUC0-180 averages for males and females were 51267.5 and 36537.5 mg/dL, respectively, representing a 1.4-fold difference in favor of females with lower AUC0-180 indicating adequate glucose clearance. The AUC0-180 mean differences between the sexes within each specific CC line varied widely within the CC population. A total of 46 new QTL, designated as T2DSL and its number, for T ype 2 D iabetes S pecific L ocus and its n umber, were identified during our study, among which 19 QTL are novel. The interval locations of these novel QTL have not been found in any other previously identified QTL. The remaining 27 QTL observed during our study have been fine mapped. As far as the genome-wide significance is concerned, three sets of QTL were identified during our entire study. The first group is the one which had a 50% of genome-wide significance, the second group is of 90%, and the third group had a 95% of genome-wide significance. Further, our study showed a complex pattern of haplotype effects of the founders, with the wild-derived strains (mainly PWK) playing a significant role in the increase of AUC values.

Abstract A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables. Diabetes polygenic variants frequently run in families. T2D is regarded as a silent illness that can advance for years before being diagnosed. Numerous metabolic issues are noticed once T2D is diagnosed, either as comorbidities or as side effects. Finding genetic markers for T2D and metabolic syndrome in groups with similar environmental exposure is therefore essential to understanding the mechanism of such complex characteristic illnesses. So herein, we demonstrated the exclusive use of the collaborative cross (CC) mouse reference population to identify novel quantitative trait loci (QTL) and, subsequently, suggested genes associated with host glucose tolerance in response to a high-fat diet. In the current study, we used 539 mice from 60 different CC lines. The diabetogenic effect in response to high-fat dietary challenge was measured by the intraperitoneal glucose tolerance test (IPGTT) after 12 weeks of dietary challenge. Data analysis was performed using a statistical software package IBM SPSS Statistic 23. Using the mouse genome informatics (MGI) database resource enabled us to examine the reported QTL in detail for genetic features related to glucose metabolism. Afterward, the total area under the curve (AUC0-180) of the glucose clearance was computed and utilized as a marker for the presence and severity of diabetes. The observed AUC0-180 averages for males and females were 51267.5 and 36537.5 mg/dL, respectively, representing a 1.4-fold difference in favor of females with lower AUC0-180 indicating adequate glucose clearance. The AUC0-180 mean differences between the sexes within each specific CC line varied widely within the CC population. A total of 46 new QTL, designated as T2DSL and its number, for Type 2 Diabetes Specific Locus and its number, were identified during our study, among which 19 QTL are novel. The interval locations of these novel QTL have not been found in any other previously identified QTL. The remaining 27 QTL observed during our study have been fine mapped. As far as the genome-wide significance is concerned, three sets of QTL were identified during our entire study. The first group is the one which had a 50% of genome-wide significance, the second group is of 90%, and the third group had a 95% of genome-wide significance. Further, our study showed a complex pattern of haplotype effects of the founders, with the wild-derived strains (mainly PWK) playing a significant role in the increase of AUC values.

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

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

Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene coexpression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the "African American Genetics of Metabolism and Expression" and "Metabolic Syndrome in Men." We used weighted gene coexpression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous "consensus genes" whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight coexpressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization, and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., Hcar1), inflammation (e.g., Rgs1), adipogenesis (e.g., Tmem120b), or no previously known role in obesity (e.g., St14 and Ms4a6a). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.