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Identification of a Novel Gene for Diabetic Traits in Rats, Mice, and Humans [Genetics](Genetics Via Acquire Media NewsEdge) ABSTRACT The genetic basis of type 2 diabetes remains incompletely defined despite the use of multiple genetic strategies. Multiparental populations such as heterogeneous stocks (HS) facilitate gene discovery by allowing fine mapping to only a few megabases, significantly decreasing the number of potential candidate genes compared to traditional mapping strategies. In the present work, we employed expression and sequence analysis in HS rats (Rattus norvegicus) to identify Tpcn2 as a likely causal gene underlying a 3.1-Mb locus for glucose and insulin levels. Global gene expression analysis on liver identified Tpcn2 as the only gene in the region that is differentially expressed between HS rats with glucose intolerance and those with normal glucose regulation. Tpcn2 also maps as a cis-regulating expression QTL and is negatively correlated with fasting glucose levels. We used founder sequence to identify variants within this region and assessed association between 18 variants and diabetic traits by conducting a mixed-model analysis, accounting for the complex family structure of the HS. We found that two variants were significantly associated with fasting glucose levels, including a nonsynonymous coding variant within Tpcn2. Studies in Tpcn2 knockout mice demonstrated a significant decrease in fasting glucose levels and insulin response to a glucose challenge relative to those in wild-type mice. Finally, we identified variants within Tpcn2 that are associated with fasting insulin in humans. These studies indicate that Tpcn2 is a likely causal gene that may play a role in human diabetes and demonstrate the utility of multiparental populations for positionally cloning genes within complex loci. TOdate,human genome-wideassociationstudies(GWAS) have identified .60 genes involved in type 2 diabetes (T2D) (Zeggini et al. 2008; Voight et al. 2010; Morris et al. 2012; DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium et al. 2014) and an additional 53 involved in related metabolic traits such as fasting glucose and insulin levels (Dupuis et al. 2010; Scott et al. 2012). Despite the relative success of human GWAS, when combined, these genes explain only a small percentage of the heritable variance (So et al. 2011; Morris et al. 2012), indicating many more genes have yet to be identified. Improving power with increased sample size, deep sequencing to identify rare variants, and identification of gene-gene and gene-environment interactions will help to identify at least some of this missing heritability in humans (Hu and Daly 2012). Animal models, however, offer an additional, complementary, method for identifying candidate genes and their related pathways. Advantages of animal models include the ability to control environment and genetics and assess gene expression levels in tissues that are not readily available from humans. Im- portantly, genes found for complex traits in animal models are frequently translated to humans (Swanberg et al. 2005; Aitman et al. 2006; Samuelson et al. 2007; Behmoaras et al. 2008; Petretto et al. 2008), demonstrating the utility of animal models for uncovering at least some of the missing heritability in humans. Particularly useful are multiparental or outbred popula- tions such as heterogeneous stocks (HS). HS animals are derived from crossing eight inbred strains and outbreeding them for many generations (see Flint and Eskin 2012 or Solberg Woods 2014 for a review on HS populations). The genetic makeup of the resulting progeny represents a random mosaic of the founder animals, with each HS rat being phe- notypically and genetically distinct. The distance between recombination events decreases with each generation of breeding, such that HS mice and rats have been used to map multiple complex traits to generally ,5 Mb (Talbot et al. 1999, 2003; Demarest et al. 2001; Valdar et al. 2006; Solberg Woods et al. 2010, 2012; Johnsen et al. 2011; Baud et al. 2013). This fine-resolution mapping significantly de- creases the number of potential candidate genes within each locus relative to those in conventional mapping methods (such as those using an F2 intercross or backcross). Once narrowed, HS founder sequence can be used to rapidly iden- tify potentially causative genes within at least some of these loci (Keane et al. 2011; Baud et al. 2013), thus making these populations ideal for positional cloning of genes involved in complex traits. We have used HS rats to fine-map traits involved in diabetes and related traits. Our work has shown that HS rats harbor alleles for glucose tolerance (Solberg Woods et al. 2010), as well as insulin resistance and b-cell dysfunction, the underlying causes of type 2 diabetes (Solberg Woods et al. 2012). We have identified a 3.1-Mb region on rat chromo- some 1 that maps both fasting and postprandial glucose (Solberg Woods et al. 2010, 2012), and this region overlaps a larger 7-Mb region that maps fasting insulin and insulin sensitivity (Solberg Woods et al. 2012). Although a great im- provement over previous mapping studies in F2 intercrosses (Galli et al. 1996; Gauguier et al. 1996; Chung et al. 1997; Kanemoto et al. 1998; Wei et al. 1999; Nobrega et al. 2009; Solberg Woods et al. 2009; Wallis et al. 2009), this region still contains a relatively large number of genes: 86 within the 3.1-Mb glucose region and .200 genes in the insulin region. Although a few plausible genes (including Kcnq1 and Igf2) stand out as likely candidates within the 7-Mb insulin region, none of the 86 genes within the 3.1-Mb region present them- selves as obvious potential biological candidate genes. The goal of the present study was to use expression analysis and next-generation sequencing to evaluate posi- tional candidate genes within this 3.1-Mb glucose locus, located on rat chromosome 1: 204.38-207.48 Mb, a complex region that may harbor multiple causal genes (Granhall et al. 2006; Solberg Woods et al. 2010, 2012). Expression and sequence analysis in the HS rats identified two-pore segment channel 2 (Tpcn2)asaplausiblecandi- date gene within this region. We confirm the role of Tpcn2 by demonstrating that Tpcn2 knockout mice exhibit altered fasting glucose and insulin in response to a glucose chal- lenge. We also demonstrate significant association between SNPs in Tpcn2 and fasting insulin in humans. Together this work indicates that Tpcn2 is a strong candidate for being a causal gene that underlies the glucose and insulin phenotypes at this QTL. Materials and Methods Animals Heterogeneous stock colony: The NMcwi:HS colony, here- after referred to as HS, was initiated by the National Institutes of Health (NIH) in 1984, using the following eight inbred founder strains: ACI/N, BN/SsN, BUF/N, F344/N, M520/N, MR/N, WKY/N, and WN/N (Hansen and Spuhler 1984). This colony has been maintained at the Medical College of Wis- consin (MCW) since 2006 and has been through .60 gener- ations of breeding (see Solberg Woods et al. 2010 for history of the breeding colony). Founding inbred substrains: Expression analysis was con- ducted in the following substrains (abbreviated names used throughout the article are in parentheses): ACI/Eur (ACI), BN/SsnHsd (BN), BUF/NHsd (BUF), F344/NHsd (F344), M520/N, and WKY/NHsd (WKY). Other than M520/N, which is the original founder of the HS colony, substrains were chosen based on origin similarities to the original founders, as determined by data obtained from the Rat Genome Database: http://rgd.mcw.edu/strains/. The M520 and ACI rats are maintained at MCW. All other substrains were or- dered from Harlan Sprague Dawley (Indianapolis). We were unable to identify substrains for two of the founders (MR/N and WN/N), so these strains were not tested. Cluster analysis of glucose and insulin levels in HS rats Rats for expression analysis were chosen from a group of 522 HS rats previously used to map glucose tolerance (Solberg Woods et al. 2010) (see Supporting Information, File S2). As previously described, HS rats underwent an intra- peritoneal glucose tolerance test (IPGTT) test at 16 weeks of age in which blood glucose and plasma insulin were mea- sured at fasting and at 15, 30, 60, and 90 min after a 1-g/kg glucose injection (Solberg Woods et al. 2010, 2012). Glu- cose area under the curve (glucose_AUC) and insulin_AUC were determined using a trapezoidal analysis. The quanti- tative insulin sensitivity check (QUICKI), a measure of in- sulin sensitivity that has been validated in both rats and humans (Katz et al. 2000; Cacho et al. 2008), was calculated as 1=½log I0 þ log G0 ^; where I0 is fasting insulin and G0 is fasting glucose (Solberg Woods et al. 2012). HS rats were killed at 17 weeks of age, 1 week after the glucose toler- ance test. Fasting cholesterol, triglycerides, and c-peptide were determined from plasma at the time the animals were killed. Livers were immediately removed and frozen in liquid nitrogen for subsequent determination of transcript abun- dance. Rats were killed by decapitation to avoid gene expres- sion changes that can occur when using inhalant drugs such as CO2 or isoflurane (Kadar et al. 2011; Taylor and Cummins 2011). All protocols were approved by the Institutional Ani- mal Care and Use Committee at MCW. To determine how many phenotypic groups existed within the HS colony, a K-means cluster analysis was conducted. Clusters were determined using the following glucose and insulin measurements: glucose_AUC, insulin_AUC, peak time, the difference between baseline and the peak value, the slope of the line from right before the peak to peak time, and slope from peak time to right after the peak. The K-means cluster- ing has an animal's phenotype values added to a cluster based on minimizing differences from the means of the K clusters. Canonical discrimination using SAS 9.1 was used to deter- mine how the clustering related to the phenotype measures. Expression analysis Expression analysis was conducted in 46 male HS rats and 2 males from each of six inbred substrains, for a total of 58 animals. Total RNA was extracted from frozen liver tissue, using TRIzol reagent [Invitrogen (now Life Technologies), Carlsbad,CA].Purified RNA (50 ng) was amplified using an Affymetrix two-cycle cDNA synthesis kit, and complementary RNA was synthesized, labeled, fragmented, and hybridized to the Gene-Chip rat genome 230_2.0 array in accordance with standard protocols (Affymetrix, Santa Clara, CA). The rat genome 230_2.0 array contains .31,000 probe sets repre- senting .17,734 unique UniGenes, containing probes for 57 of the 86 genes (66.3%) within our region of interest [rat chromosome 1: 204.38-207.48 Mb (Solberg Woods et al. 2010, 2012)]. After hybridization, arrays were washed and stainedwithanAffymetrixWashandStainkitplusanAffy- metrix GeneChip Fluidics Station 450 and scanned with an Affymetrix 7G laser scanner. Data were analyzed with Expres- sion Console 1.1.2 software (Affymetrix) and normalized with robust multichip analysis (RMA) (http://www.bioconductor. org/) to determine signal log ratios (log2R). The statistical significance of differential gene expression was determined through ANOVA, using Partek (St. Louis) Genomics Suite 6.5. Differential expression (DE) was defined as those genes with P # 0.05 and log2R $ 0.5. Because we planned to verify differential expression of genes of interest with quantitative RT-PCR (qRT-PCR), we did not correct for multiple testing (Hessner et al. 2004). Real-time qRT-PCR We initially confirmed differential expression of Tpcn2 using qRT-PCRin10HSratswithglucoseintoleranceand10with normal glucose regulation. qRT-PCR was also conducted in the founder strains, using 2 animals from each inbred strain. Upon confirming Tpcn2 as a plausible candidate gene, we used qRT- PCR to assess gene expression levels in a total of 120 male HS rats. qRT-PCR was conducted on an ABI Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA). Specific oligonucleotide primers were designed with Primer3 (http://binoinfo.ut.ee/primer3-0.4.0/primer3/) (Koressaar and Remm 2007; Untergasser et al. 2012). To avoid differences in expression levels based on sequence variation (Huang et al. 2009), we ensured no sequence variants were present in any of the HS founders at the location of the Tpcn2 primers. The primers for Tpcn2 were forward 59-TATGTGTTCGC CATGATTGG-39 and reverse 59-AGCAGCAAAGTCGTCGAAGT (Integrated DNA Technologies). We used primers for GAPDH, forward 59-CATGGAGAAGGCTGGGGCTC-39 and reverse 59-AACGGATACATTGGGGGTAG-39,andb-2 microglobulin (B2M), forward 59-CCGTGATCTTTCTGGTGCTT-39 and reverse 59-TTTTGGGCTCCTTCAGAGTG-39 (Integrated DNA Techno- logies), as internal controls. We used iQ SYBR Green Supermix (Bio-Rad, Hercules, CA) according to the manufacturer's instructions. Synthesis of first-strand cDNA from 3 mgofRNA per animal was accomplished with random hexamers (Invitro- gen, now Life Technologies) and Superscript III (Invitrogen, now Life Technologies), according to the manufacturer's instructions. Triplicate Tpcn2 and GAPDH or B2M were per- formed for each sample in 20-ml reactions, which included 1 ml of cDNA and 10 ml of iQ SYBR Green Supermix (Bio-Rad) possessing 1.2 mlofTpcn2 (10 mM), GAPDH, or B2M specific primers and 7.8 ml of deionized water. Reactions were cycled as follows: stage 1, 95^, 3 min; stage 2, 55 cycles of 95^, 30 sec, 55^, 30 sec, 72^, 30 sec; and stage 3, melt curve of 95^, 10 sec, 60^, 1 min, 95^, 1 sec, 40^, 10 sec. Standard curves were created using 1:10, 1:100, and 1:1000 concen- trations of Tpcn2. Specificity for a qRT-PCR was verified by a melting curve analysis. The data were analyzed with Se- quence Detection System 2.3 software (Applied Biosystems), using the cycle threshold (ct) for quantification. Relative gene expression data (fold change) between samples was accom- plished using the mathematical model described by Pfaffl (2001), using the mean ct score of 120 animals as the reference. Genome-wide genotyping Tail samples from the original eight inbred founders were obtainedfromtheNIH.WeextractedDNAfromtailtissuefrom HS and founder strains, using the QIAGEN (Valencia, CA) DNeasy kit. All eight original founder inbred strains and the 46 HS rats used for expression analysis were genotyped using the Affymetrix GeneChip Targeted Genotyping technology on a custom 10K SNP array panel as previously described (Saar et al. 2008). The samples were genotyped by the Vander- bilt Microarray Shared Resource center at Vanderbilt University in Nashville, Tennessee (currently VANTAGE: http://vantage.vanderbilt.edu). From the 10,846 SNPs that are on the array, 7634 were informative and pro- duced reliable results in the HS rats. From these final informative markers, the average spacing was 353 kb apart, with an average heterozygosity of 28.8%. These data have been deposited in the Gene Expression Omni- bus (GEO) database under accession no. GSE57118 (see: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token= sjmjgkwulbebtmd&acc=GSE57118). Genetic mapping of expression QTL Expression mapping was conducted for all genes on the expression array that were located within the QTL (57 of 86). Genome-wide fine mapping was conducted by linkage disequilibrium mapping, testing for the significance of association between expression levels and inferred haplo- type descent at each locus, as previously described (Solberg Woods et al. 2010, 2012). Briefly, haplotype descent along each HS rat genome was inferred using the haplotype recon- structionmethodHAPPY(Mottet al. 2000), which applies a hidden Markov model simultaneously to the genotypes of the eight founder strains and the 46 HS rats. At each interval m ¼ 1; ... ; 7634 between adjacent pairs of markers, HAPPY produces for each rat i a vector gi ðmÞ containing the probabilities of descent from each of the 8ð8 þ1Þ=2¼ 36 unique haplotype pairs (diplotypes) (Valdar et al. 2009). Using Bagpipe software (Valdar et al. 2009), this is then used in the following mixed-model regression to predict rat i's expression levels yi ; fðyiÞ¼QTLiðmÞ þsibshipk½i^ þ residuali; (1) where f ðyiÞ is a transformed version of the expression levels, QTLi ðmÞ¼bT gi ðmÞ models the putative effect of the QTL, sibshipk½i^ is the (random) effect of the sibship k to which i belongs, and residuali accounts for individual variation (fur- ther parameters defined as in Solberg Woods et al. 2010). Models were fitted by restricted-estimate maximum likeli- hood, and the significance of association at each interval m was assessed by comparing the fit of Equation 1 with that of the null model, which is Equation 1 without the QTL term. Genome-wide significance thresholds were estimated by parametric bootstrap from the fitted null model (Valdar et al. 2009; Solberg Woods et al. 2010). Prior to genetic analysis, expression levels for each gene were normalized using a van der Waerden normal score. Sequencing and analysis We sequenced 5 Mb within rat chromosome 1: 200-208 Mb in the eight inbred founder strains of the HS colony. We designed a custom 385K NimbleGen array that included all coding regions and all regions that are highly conserved. The array was composed of 385,000 long oligonucleotide probes and used a tiling design to target our unique genomic region. Five to eight mg of genomic DNA from each strain was nebulized and polished, and linkers were added accord- ing to the manufacturer's standards (Roche NimbleGen). Samples were then hybridized to the custom NimbleGen 385K array. DNA not specific to these regions were washed off and targeted DNA sequences were eluted and amplified by emulsion PCR. To calculate whether targeted sequences were enriched, both pre- and postcapture DNAs were analyzed by qRT-PCR, using four real-time developed and optimized by our laboratory. The region was then sequenced on the Roche GS-FLX 454 system (Roche 454, Branford, CT) according to the manufacturer's protocol. Briefly, Roche "A" and "B" adapters were annealed onto cap- tured fragments of template DNA. The library was single stranded and quantified on an Agilent 2100 Bioanalyzer. Li- brary fragments were annealed to beads and emulsion PCR was performed. After clonal amplification by PCR, the emul- sion was broken chemically and beads were collected and enriched. Enriched beads were then inserted into the Roche Picotiter Plate, using the appropriate gasket, and loaded onto the machine. Nucleotides flowed over the plate in a specific order and enzymatic cascade reaction caused incorporation of the specific nucleotides to illuminate. An image of every flow was taken with a CCD camera and images were strung to- gether into a flowgram, which was transformed into usable sequence data. Reads were separated by their multiplex identifiers, using the program sfftools (Roche). For each sample, demulti- plexed reads were then trimmed and assembled against the rat chromosome 1 reference sequences (Rn4), using the GS Reference Mapper (http://454.com/products/analysis-software/ index.asp). Variant detection was performed using the high- confidence differences strategies implemented in GS Refer- ence Mapper software. The high-confidence differences (HCDiff) strategy requires the following criteria for a variant to be reported: (1) There must be at least three reads with the difference; (2) there must be both forward and reverse reads showing the difference, unless there are at least five reads with quality scores .20 (or 30 if the difference involves a 5-mer or higher); and (3) if the difference is a single-base overcall or undercall, then the reads with the difference must form the consensus of the sequenced reads. Since the time of this work, all founder strains have been fully sequenced (Baud et al. 2013), and this information is currently available in the Rat Genome Database (http://rgd. mcw.edu). We have compared our sequence to what is avail- able online. Some discrepancies were noted between the data sets. To determine the accurate sequence, these regions were sequenced using Sanger sequence technology. Taqman genotyping We genotyped 18 SNPs within the 3.1-Mb QTL in 508 male HS rats and all eight inbred founders. SNPs were chosen based on the founder strain distribution pattern (SDP) as a means of de fi ning general haplotype blocks (Yalcin et al. 2004a). To ensure most haplotyes were represented, we used a measure of entropy as previously described (Yalcin et al. 2005). Briefly, a sliding window was used in which each SNP within the window is prioritized based on its abil- ity to explain the diversity between the founder strains. SNPs were then given a score and those with the highest scores were chosen for genotyping. In addition to SNPs with high entropy scores, we also genotyped nonsynonymous coding variants in the F344 strain, the founder that contrib- utes the susceptibility allele at the QTL (Solberg Woods et al. 2010, 2012). SNPs were spaced an average of 164.5 kb apart. Samples were genotyped using 59-exonuclease TaqMan tech- nology (Applied Biosystems) with differently fluorescence- labeled probes. Custom TaqMan SNP Genotyping Assays (Applied Biosystems) were used. Ten nanograms of DNA was used in a total volume of 5 ml containing 2 ml of 5ng/ml DNA, 2 ml13 TaqMan Universal PCR Master Mix (Applied Biosystems), 0.125 ml probe, and 0.875 ml water for each sample. PCR was carried out on a GeneAmp PCR System 9700 (Applied Biosystems) and the post-PCR plate was read on the ViiA 7 real-time PCR system (Applied Biosystems), following the manufacturer's instructions. ViiA 7 RUO ver- sion 1.2.1 software was used to assign genotypes, applying the allelic discrimination test. The overall call rate was 98.8%. Glucose tolerance test in Tpcn2 knockout mice Wild-type and knockout mice were created using embryonic stem cells from the 129P2 strain carrying a gene trap vector and injected into C57BL/6 blastocysts, as previously described (Calcraftet al. 2009). We confirmed that Tpcn2 is not transcribed in the knockout mice by running qPCR on multiple tissues in knockout and wild-type mice (see Figure S1). PCR was conducted as described above, using primers for GAPDH (forward 39-CTGAAGGGCATCTTGGGCTA-59 and reverse 39-GCCGTATTCATTGTCATACCA-59) and Tpcn2 (forward 39-CCAGGCTACCTGTCCTACCA-59 and reverse 39- CAGGAAGCGAAACACAATCA-59). Heterozygous mice were bred and set up as breeder pairs. Male Tpcn2 knockout, heterozygote, and wild-type mice from the heterozygote breeders were phenotyped at 9-13 weeks of age (n = 5-7 in each group), using the IPGTT, described above and in Solberg Woods et al. (2010, 2012). Blood was collected after an overnight fast and at 15, 30, 60, 90, and 120 min after a 1-g/kg glucose injection. We used the Ascensia Elite system for reading blood glucose values (Bayer, Elkhart, IN). We also collected blood at each time point for subsequent analysis of plasma insulin levels, which was assayed using an ultrasensitive ELISA kit from Alpco Diagnostics (Salem, NH). Statistical analysis A mixed model was used to determine association between 18 SNPs within the chromosome 1 QTL and diabetic phenotypes that map to this region (glucose_AUC, fasting glucose, fasting insulin, insulin_AUC, and QUICKI) and Tpcn2 expression levels (as determined by qPCR). The model included all appropriate covariates (location, injector, collector, and number of glucose injections) and accounted for the complex family relationships of the HS, as previously described (Solberg Woods et al. 2010, 2012). The significance threshold was determined using the Bonferroni method to account for multiple comparisons (18 SNPs by six traits). Correlations between Tpcn2 expression levels (as determined by qRT-PCR) and these traits were assessed using Pearson's correlation coefficient. Pearson's correlation coefficient was also used to determine correlations between all microarray probes within the chromosome 1 region and metabolic traits, using a Bonferroni correction to determine significance. A one-way ANOVA was used to determine whether there were statistical differences between wildtype, heterozygote, and Tpcn2 knockout mice. If a trend toward significance was noted, a t-test was used to assess significance between wild-type and knockout mice only. Tpcn2 lookup in human GWAS We used data from two large consortiums to determine whether Tpcn2 may play a role in human diabetes. Diabetes Genetics Replication and Meta-analysis (DIAGRAM) has collected 2.4 million SNPs in .47,000 cases and controls (Zeggini et al. 2008; Voight et al. 2010), with 34,840 cases and 114,981 controls recently genotyped on the custom Metabochip (Morris et al. 2012). The Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) has collected phenotypic information for quantitative glycemic traits such as glucose and insulin in .40,000 individuals (Dupuis et al. 2010), with 133,010 individuals genotyped on the custom Metabochip (Scott et al. 2012). Both DIAGRAM and MAGIC cohorts consist of males and females mainly of European descent. Through collaboration with these investigators, we inquired whether a subset of 39 SNPs within Tpcn2 was associated with diabetic traits in humans. The 39 SNPs were chosen based on information from the HapMap project (www.hapmap.org), choosing SNPs with an average r2 . 0.8. All but 2 SNPs had an average r2 . 0.9. Fourteen of the 39 SNPs were upstream and 4 were downstream of the Tpcn2 gene (see Table S1). For those SNPs that reached an unadjusted level of significance at P # 0.05, we used the Benjamini and Hochberg (1995) false discovery rate to account for multiple comparisons. Results Glucose and insulin phenotypes in HS rats separate into five main clusters Although we initially identified six clusters based on glucose and insulin values, one of the clusters (cluster 5) contained a single animal and was excluded from subsequent analysis. There are two dominant phenotypic factors that characterized the clusters. The first (which explained 65% of the variability between clusters) consists of the magnitude of the peak of the insulin curve and the glucose curve, the rate of increase of the glucose from baseline to peak, the rate of increase of insulin from baseline to peak, and glucose_AUC. The second factor (which explained 31% of the variability between the clusters) consists of the rate of decrease of the insulin curve after the peak and the rate of decrease of the glucose curve after the peak. The five clusters are shown in Figure 1 and demonstrate the importance of taking into account both glucose and insulin in the clustering. Note that animals in clusters 2 and 3 exhibit very similar plasma insulin levels, but differ significantly in blood glucose levels. Similarly, animals from clusters 3 and 4 exhibit similar blood glucose levels, but differ significantly in plasma insulin levels. For the expression analysis, glucose intolerant HS rats were chosen from cluster 1 (high glucose, moderate insulin) and HS rats with normal glucose regulation were chosen from clusters 2 and 4 (low glucose and insulin and moderate glucose and insulin, respectively). The glucose intolerant group is highly significantly different from the normal glucose group (glucose_AUC = 13,458.9 vs. 9061, F1,44 = 398.7, P , 2e-16). Animals in the glucose intolerant group also exhibit differences in several other phenotypes, including significantly higher levels of body weight (349.1 vs. 314.4 g, F1,44 = 15.47, P = 0.000294), fasting glucose (90.3 vs. 77.5 mg/dl, F1,44 = 42.65, P = 5.59e- 08), fasting insulin (1.8 vs. 1.1 ng/ml, F1,44 = 7.52, P = 0.009), insulin_AUC (311.3 vs. 204.3, F1,44 = 17.10, P = 0.000157), QUICKI (0.48 vs. 0.54, F1,44 = 7.96, P = 0.007) fasting c-peptide (1808.0 vs. 1505.8 pg/ml, F1,44 = 5.75, P = 0.021), and fasting triglycerides (86.2 vs. 72.3 mg/dl, F1,44 = 8.0, P = 0.007). No differences are seen between the groups for fasting cholesterol or fat pad weights (see Table S2). Tpcn2 is the only gene measured within the 3.1-Mb QTL that is differentially expressed between HS rats with different glucose profiles We identify 48 probes genome-wide that are differentially expressed between HS rats with glucose intolerance and those with normal glucose regulation, 3 of which had an unknown location (see Table S3). These data have been de- posited in the GEO database under accession no. GSE57118 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token= sjmjgkwulbebtmd&acc=GSE57118). Of these 48 genes, only one falls within the 3.1-Mb glucose region on rat chromo- some 1: Tpcn2. Statistics for the other probes within the 3.1-Mb region are listed in Table S4. Using qRT-PCR, we confirm differential expression of Tpcn2 against both GAPDH (F1,19 = 4.36, P = 0.05; see Figure 2A) and B2M (F1,18 = 7.03, P = 0.017; data not shown). HS founder strains also show different levels of Tpcn2 expression, with the F344 strain showing the least amount of Tpcn2 expression and the BN strain showing the greatest amount of Tpcn2 expres- sion (see Figure 2B). All fold-change differences are expressed relative to the F344 founder substrain. Tpcn2 maps as a cis-eQTL to the same region as the physiological QTL Using haplotype reconstruction and mixed-model linear regression, which takes into account sibship relationships, we identify a single sharp peak (marker interval gko.18d13. rp2.b1.157 and Cpn.1207450582, 205.22-207.45 Mb; log P = 12.09) for expression levels of Tpcn2 (see Figure 3). Only one other gene within this region, Acy3, maps as a cis-eQTL, but with a much lower log P (marker interval WKYOa01b03. r1.818 and rat106.025.f16.p1ca.195, 204.32-207.50 Mb; log P = 6.28). The genome-wide significance threshold is log P = 5.95 for both Tpcn2 and Acy3 expression levels, where 2log P is defined as -log10(P-value). Nonsynonymous coding variants are identified within three genes, including Tpcn2 As expected, we identify ^2000-3000 SNPs between each strain and the BN reference genome (Saar et al. 2008). Within the 3.1-Mb glucose locus, we identify three genes with a nonsynonymous amino acid change in the F344 founder strain: Tpcn2, RGD1311946,andGpr152 (see Table 1). We focused on variants in the F344 strain because our previous work demonstrates that the F344 strain contributes to the susceptibility allele at the QTL for glucose tolerance (Solberg Woods et al. 2010) as well as for fasting glucose and insulin (Solberg Woods et al. 2012). It is important to note that loci for fasting glucose and insulin exhibit a complex genetic ar- chitecture with several other founder haplotype combinations leading to an increase in fasting glucose or insulin. Because of this, we were unable to run a simple haplotype analysis, as demonstrated by other groups (Svenson et al. 2012; Logan et al. 2013). The variants within Tpcn2 and RGD1311946 are not predicted to be damaging by PolyPhen or SIFT. The var- iant within Gpr152, however, results in a premature stop co- don and could potentially lead to changes in protein function. Variants within and near Tpcn2 are significantly associated with fasting glucose and Tpcn2 expression levels Using a Bonferroni-adjusted region-wide threshold of 3.33, we found that 2 SNPs, located at 205,653,821 bp (2log P =3.62) and 205,715,459 bp (2log P =3.35),aresignificantly associ- ated with fasting glucose (see Figure 4, Table 1, and Table S5). The SNP at 205,715,459 bp harbors the nonsynonymous coding variant within Tpcn2, while the SNP at 205,653,821 bp is located in the intron of the neighboring mammary cancer- associated protein Rmt1 gene. These SNPs and 3 additional SNPs are also associated with Tpcn2 expression levels: 205,653,821 bp (2log P = 6.58), 205,715,459 bp (2log P = 6.17),205,898,738bp(2logP=4.90),206,242,238bp(2log P = 3.69), and 206,726,856 bp (2 log P = 4.43). The addi- tional SNPs include the nonsynonymous coding variants within RGD1311946 and Gpr152, as well as a coding variant within Cpt1a. No associations are found between the 18 SNPs and glucose_AUC, fasting insulin, insulin_AUC, or QUICKI. Genotypes for the 18 SNPs can be found in File S3. Tpcn2 expression levels negatively correlate with fasting glucose Using the qRT-PCR data in 120 HS rats, we demonstrate asignificant negative correlation between Tpcn2 expression levels and fasting glucose and glucose_AUC (r = 20.302, P = 0.001 and r = 20.246, P = 0.007, respectively; see Figure 5). After removing 6 animals that have extremely high expres- sion values (.2 SD from the mean), only fasting glucose remains significantly correlated with expression levels (r = 20.232, P =0.013).Tpcn2 expression levels are not sig- nificantly correlated with fasting insulin, insulin_AUC, and QUICKI (r = 20.034, 20.063, and 0.078, respectively). qRT-PCR data can be found in File S4. To determine whether any other genes in the region are correlated with the diabetic traits, we determined correlation coefficients between expres- sion levels of the microarray probes within the chromosome 1 region and six metabolic traits. We confirm the significant negative correlation between fasting glucose and Tpcn2 expression levels (r = 20.559, P = 0.0000549). Based on a Bonferroni-adjusted significance level of 0.00009, we also find a marginally signifi cant correlation between RGD1307603, a gene with no known function, and QUICKI (r = 0.544, P = 0.00000925). Correlation coeffi cients and unadjusted P-values are reported in Table S6. Tpcn2 knockout mice exhibit decreased fasting glucose and insulin_AUC We find a significant main effect of genotype on insulin_AUC (F2,15 = 4.41, P = 0.031), with knockout mice exhibiting significantly decreased insulin response relative to wild-type mice (KO = 126.2, Het = 147.2, WT = 165.4, P = 0.029, Tukey's post-hoc test; see Figure 6 and Table S7). There is a trend toward signi fi cance for fasting glucose (KO = 96.2, Het = 116.3, WT = 111.0, F2,15 = 3.56, P = 0.056) and QUICKI (KO = 0.54, Het = 0.49, WT = 0.49, F2,15 = 3.32, P = 0.066). Upon running a t -test between knockout and wild-type mice only, we find that Tpcn2 knockout mice ex- hibit significantly decreased fasting glucose levels relative to wild-type mice (t(8.9) = 2.28, P = 0.048). Tpcn2 knockout mice do not differ significantly from wild-type mice in fast- ing insulin levels or glucose_AUC. Means for all traits are reported in Table S7. Raw data for the knockout, wild-type, and heterozygote mice can be accessed in File S5. Association between Tpcn2 and fasting insulin and HOMA_IR in humans Although we looked up associations in 39 SNPs, MAGIC was able to provide statistics on only 14 SNPs and DIAGRAM provided statistics on 11 SNPs (see Table S1). Information 24 S.-W. Tsaih et al. was not available for the other SNPs likely because they were either rare variants or failed imputation quality control for other reasons. Of the 14 SNPs from MAGIC, 4 SNPs reach unadjusted levels of significance for fasting insulin, 2 of which remain marginally significant after accounting for mul- tiple comparisons (rs10736671, P = 0.051, and rs11604251, P = 0.051; see Table 2). Three of the 4 SNPs reach unad- justed levels of significance for HOMA_IR, a measure of in- sulin sensitivity (Matthews et al. 1985), but do not remain significant after adjusting for multiple comparisons. Signifi- cance is not found between SNPs within Tpcn2 and type 2 diabetes or fasting glucose (data not shown). Discussion In our previous work, we used HS rats to identify a 3.1-Mb region that contains a QTL for glucose tolerance and fasting glucose (Solberg Woods et al. 2010, 2012) and that overlaps larger QTL for fasting insulin and insulin sensitivity (Solberg Woods et al. 2012). In the present study, we use both anal- ysis of gene expression and association analysis to identify Tpcn2 as a likely causal gene within this locus. These find- ings are supported by altered glucose and insulin levels in a Tpcn2 knockout mouse, as well as by an association be- tween variants within Tpcn2 and fasting insulin levels in humans. The role of Tpcn2 in regulating glucose and insulin levels is currently unknown. Tpcn2 channels localize to the lyso- some and are likely receptors for the calcium-mobilizing agent NAADP (Brailoiu et al. 2009; Calcraft et al. 2009; Schieder et al. 2010). Several studies indicate that NAADP may play a role in insulin signaling of the b-cells (Kim et al. 2008; Naylor et al. 2009; Shawl et al. 2009; Alejandro et al. 2010; Arredouani et al. 2010), and a recent study suggests NAADP is involved in glucose homeostasis (Park et al. 2013). It is also interesting to note that Tpcn2 and other calcium-handling genes (Tpcn1 and IP3R1) have recently been found to be differentially expressed in patients with heart failure (Garcia-Rua et al. 2012). Although previous studies have not looked at the role of Tpcn2 in liver, alter- ations of calcium channels such as CaMKII have been shown to lead to impaired liver gluconeogenesis (Ozcan et al. 2012), while Cav2.3 calcium channel knockout mice exhibit altered glucose levels, likely as a result of reduced insulin sensitivity (Matsuda et al. 2001). Together these studies suggest a possible role for Tpcn2 in both the b-cell and the liver. Future studies will test the hypothesis that Tpcn2 is involved in glucose-stimulated insulin secretion of the b-cells as well as probe a potential role of this gene in reg- ulating insulin sensitivity and gluconeogenesis. Global gene expression analysis in HS rats was used to initially identify Tpcn2 as a candidate gene within the glu- cose QTL. We show that Tpcn2 is differentially expressed in HS rats with glucose intolerance relative to those with nor- mal glucose regulation and demonstrate that this gene maps as a cis-eQTL to the same region as the physiological QTL. Several previous studies have found that genes that map as cis-eQTL within previously identified physiological QTL can be considered prime candidate genes (Hubner et al. 2005; Chen et al. 2008; Morrissey et al. 2011). Demonstrating a correlation between expression levels and fasting glucose levels further supports that this gene may be playing a causal role at the QTL (Farber et al. 2009; Leduc et al. 2011; Morrissey et al. 2011). Upon assessing association between 18 SNPs within the QTL and traits that map to this region (including five metabolic traits and Tpcn2 expression levels), we find that the nonsynonymous coding variant within Tpcn2, as well as an intronic variant in the neighboring Rmt1 gene, exhibits the highest level of association with fasting glucose levels and Tpcn2 expression levels, further supporting a potential causal role for this gene. Despite having a slightly higher level of association, we do not believe that Rmt1 plays a causal role in diabetes because it is neither correlated with the diabetic traits nor exhibits expression differences. It is interesting to note that three additional SNPs in the region exhibit significant association with Tpcn2 expression levels, indicating that Tpcn2 expression may be regulated by other genes in the region. Three of these SNPs, including the Tpcn2 SNP, are from the F344 founder, the strain that exhib- its very low Tpcn2 expression levels (Figure 2) and that contributes the susceptibility allele at this locus (Solberg Woods et al. 2010, 2012). It is unlikely that any of the identified variants are causal, however, because the signifi- cance levels of these SNPs do not surpass that of the haplo- type mapping for either fasting glucose (Solberg Woods et al. 2012) or Tpcn2 expression levels (Figure 3). A merge analysis, in which genotypes for all SNPs in the region are imputed, may be useful to identify the causative variant (Yalcin et al. 2005; Keane et al. 2011; Baud et al. 2013). However, because this method is most successful when a sin- gle causative variant underlies the locus (Baud et al. 2013), it may prove less useful for the current region in which multiple genes/variants may be playing a role. Upon investigating glucose and insulin levels in Tpcn2 knockout mice, we find that knockout mice exhibit signifi- cantly decreased fasting glucose and decreased insulin re- sponse to a glucose challenge relative to wild-type mice. These findings contrast with the results in the HS rats, in which decreased Tpcn2 expression levels are associated with increased, as opposed to decreased, fasting glucose and in- sulin levels. There are several possible reasons for this dis- crepancy. Because the mice were created on a mixed C57/ 129 background, we cannot rule out the possibility that alleles from the 129 strain, as opposed to the gene knockout, are causing the differences in phenotype. To minimize this effect, however, only siblings from heterozygous breeder pairs were tested. Another possibility is related to the fact that a full gene knockout can have a very different effect on a phenotype than natural allelic variation (Flint and Eskin 2012). Furthermore, several studies have shown that differ- ent variants within the same gene, or within the same GWAS location, can have opposite effects on a single phenotype (Cohen et al. 2005; Flister et al. 2013). To test the role specifically of the F344 variant on glucose and insulin reg- ulation, quantitative complementation can be used (Yalcin et al. 2004b, 2010). Alternatively, if we are able to identify the causal variant(s) within this region, we can use genetic techniques to investigate the specific role of this (or these) variant(s) (Katter et al. 2013). To determine whether Tpcn2 may be playing a role in human diabetes, we collaborated with investigators from the DIAGRAM and MAGIC consortiums. Tpcn2 has not pre- viously reached significance levels in any of the published genome-wide scans (Morris et al. 2012; Scott et al. 2012). However, genome-wide thresholds are conservative because of the need to account for the multiple testing across the genome. In the present study, we were able to assess 14 of 39 SNPs within the Tpcn2 gene and find that two are mar- ginally significantly associated with fasting insulin levels in humans. Although no significant associations were found between these SNPs and diabetes or fasting glucose levels, the majority of SNPs that we investigated were not repre- sented within the MAGIC or DIAGRAM meta-analyses. A potential reason for this is that some of these SNPs may be rare variants and thus could not be properly imputed. It is therefore possible that rare variants within Tpcn2 may be associated with diabetes and related traits (see Steinthorsdottir et al. 2014; Zuk et al. 2014). The fact that variants within all three species alter diabetic phenotypes, however, is strong evidence that this gene plays a role in metabolic regulation. This work demonstrates the power of highly recombinant animal models such as the HS for relative rapid identifica- tion of a candidate gene within a QTL. Despite relatively complex genetic architecture at the QTL, we were able to identify a highly plausible causal gene within only a few years. It is important to recognize, however, that we are not able to rule out any other genes within this QTL at this time, particularly the 29 genes that were not assessed on the expression array. In fact, our mapping studies indicate thatmorethanonegenelikelyunderliestheinsulinlocus (Solberg Woods et al. 2012), and it is reasonable to ex- pect that Tpcn2 may interact with one or more genes within this region. Of particular interest is Gpr152,a G-protein coupled receptor in which the F344 variant en- codes a premature stop codon, thus likely affecting protein function. The variant within Gpr152 was also significantly associated with Tpcn2 expression levels, suggesting a po- tential interaction between these genes. Future studies will investigate a potential role for these other genes, as well as potential interactions between genes within this region. In sum, we have identified Tpcn2 as a highly plausible causal gene within a QTL for both glucose and insulin traits. We have used expression and sequence analysis in a highly recombinant population to identify this gene in only a few years. A causal role for this gene is supported by alterations in both glucose and insulin in a Tpcn2 knockout mouse as well as data from clinical cohorts. Fu- ture work will identify the causative variant within this gene and begin to unravel the underlying mechanisms involved, as well as investigate a potential role for other genes in the region. The gene expression data, association analysis, and confirmationinbothmiceandhumansdem- onstrate Tpcn2 as a new gene contributing to glucose and insulin regulation. Acknowledgments We thank Shawn Levy and Melanie Robinson from Vanderbilt University (currently at Hudson Alpha, Inc.) for running the 10K genotyping arrays. We thank Caitlin O'Meara and Josef Lazar for helpful discussions regarding follow-up studies of Tpcn2. This work was funded by grant R01 DK-088975 (to L. C.S.W.), a Basic Research Award from Advancing a Healthier Wisconsin Research (to L.C.S.W.), a Children's Research In- stitute Pilot and Innovation award (to L.C.S.W.), and funding from the Individualized Medicine Institute at the Medical College of Wisconsin (to L.C.S.W.). MAGIC lookup was funded by grant K24 DK080140 (to J.M.). Knockout mice were created using funding from the Medical Research Coun- cil (United Kingdom) programme grant G0901521 (to J.P.). File S1 ** List of MAGIC (Meta-Analysis of Glucose and Insulin-Related traits) Consortium investigators File S1 is available for download as a pdf at http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.114.162982/-/DC1. Files S2-S5 Available for download as Excel files at http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.114.162982/-/DC1 File S2 Phenotype data used for the cluster analysis File S3 HS genotype data for 18 SNPs within the 3.1 Mb QTL on rat chromosome 1 File S4 Quantitative rt-PCR data for Tpcn2 and GAPDH in 120 HS rats File S5 Glucose and insulin levels during the IPGTT in Tpcn2 knock-out, heterozygous and wild-type mice 39 human SNPs within or near Tpcn2 were used to determine association with diabetes and related traits. DIAGRAM assessed association between 11 SNPs (in italics) and diabetes while MAGIC assessed association between 14 SNPs (in bold) and fasting glucose, fasting insulin and the homeostatic model assessment of insulin resistance (HOMA_IR). Associations between 14 SNPs and fasting insulin and HOMA_IR, as assessed by MAGIC, are listed above. Multiple comparisons were adjusted for using the Benjamini and Hochberg FDR method (BH). No significant associations were found between the SNPs and diabetes or fasting glucose. Associations could not be determined for SNPs that are not in bold because of insufficient data. * p = 0.051 after adjusting for multiple corrections. Tables S3-S6 Available for download as .docx files at http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.114.162982/-/DC1 Table S3 Differentially Expressed Genes between Glucose Intolerant and Normal Glucose HS rats (Genome-wide) Table S4 Mean expression levels for all probes within the 3.1 Mb chromosome 1 region Table S5 Significance of association (-log10P) between 18 SNPs within the 3.1 Mb QTL on rat chromosome 1 and all traits that map to this locus Table S6 Correlations between probes within the chromosome 1 region and six diabetic traits Literature Cited Aitman, T. J., R. Dong, T. J. Vyse, P. J. Norsworthy, M. D. Johnson et al., 2006 Copy number polymorphism in Fcgr3 predisposes to glomerulonephritis in rats and humans. Nature 439(7078): 851-855. Alejandro, E. U., T. B. Kalynyak, F. Taghizadeh, K. S. Gwiazda, E. K. Rawstron et al., 2010 Acute insulin signaling in pancreatic beta-cells is mediated by multiple Raf-1 dependent pathways. Endocrinology 151(2): 502-512. 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Chenoweth Shirng-Wern Tsaih,* Katie Holl,*,[dagger] Shuang Jia,*,[dagger] Mary Kaldunski,*,[dagger] Michael Tschannen,* Hong He,*,[dagger] Jaime Wendt Andrae,* Shun-Hua Li,[dagger] Alex Stoddard,*,[dagger] Andrew Wiederhold,[dagger] John Parrington,[double dagger] Margarida Ruas da Silva,[double dagger] Antony Galione,[double dagger] James Meigs,§ Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) Investigators,1 Raymond G. Hoffmann,*,[dagger] Pippa Simpson,*,[dagger] Howard Jacob,*,[dagger] Martin Hessner,*,[dagger] and Leah C. Solberg Woods*,[dagger],2 *Human and Molecular Genetics Center and [dagger]Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, [double dagger]Department of Pharmacology, University of Oxford, Oxford, OX1 3QT, United Kingdom, and §Department of Medicine, Harvard Medical School, General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts 02114 Copyright © 2014 by the Genetics Society of America doi: 10.1534/genetics.114.162982 Manuscript received February 14, 2014; accepted for publication May 25, 2014 Available freely online through the author-supported open access option. Supporting information is available online at http://www.genetics.org/lookup/suppl/ doi:10.1534/genetics.114.162982/-/DC1. 1MAGIC Investigators are listed in Supporting Information, File S1. 2Correspondingauthor:TBRC,C2315,MedicalCollegeofWisconsin,8701Watertown Plank Rd., Milwaukee, WI 53226. E-mail: [email protected] (c) 2014 Genetics Society of America |