A BAYESIAN GWAS METHOD UTILIZING HAPLOTYPE CLUSTERS FOR A COMPOSITE BREED POPULATION

Commercial beef cattle are often composites of multiple breeds. Current methods used to produce genomic predictors are based on the underlying assumption of animals being sampled from a homogeneous population. As a result, the predictors can perform poorly when used to predict the relative genetic merit of animals whose breed composition are different. In part, this is due to the changes in linkage disequilibrium between the markers and the quantitative trait loci as we move from one breed to the next. An alternative model based on breed specific haplotype clusters was developed to allow for differences in linkage disequilibrium across multiple breeds. The haplotype clusters were modeled as hidden states in a hidden Markov model where the genomic effects are associated with loci located on the unobserved clusters. Similar to the Bayes C model, we can model the genomic effects at the loci using a prior, which consists of a mixture of a multivariate normal and a point mass at zero distribution. The model will be used to construct genomic predictors using records on 5,000 cattle genotyped for 99,827 mapped SNPs representing various fractions of three different breeds

A BAYESIAN GWAS METHOD UTILIZING HAPLOTYPE CLUSTERS FOR A COMPOSITE BREED POPULATION

Commercial beef cattle are often composites of multiple breeds. Current methods used to produce genomic predictors are based on the underlying assumption of animals being sampled from a homogeneous population. As a result, the predictors can perform poorly when used to predict the relative genetic merit of animals whose breed composition are different. In part, this is due to the changes in linkage disequilibrium between the markers and the quantitative trait loci as we move from one breed to the next. An alternative model based on breed specific haplotype clusters was developed to allow for differences in linkage disequilibrium across multiple breeds. The haplotype clusters were modeled as hidden states in a hidden Markov model where the genomic effects are associated with loci located on the unobserved clusters. Similar to the Bayes C model, we can model the genomic effects at the loci using a prior, which consists of a mixture of a multivariate normal and a point mass at zero distribution. The model will be used to construct genomic predictors using records on 6,552 cattle genotyped for 99,827 mapped SNPs representing various fractions of three different breeds

The impact of selective genotyping on the response to selection using single-step genomic best linear unbiased prediction

Across the majority livestock species, routinely collected genomic and pedigree information has been incorporated into evaluations using single-step methods. As a result, strategies that reduce genotyping costs without reducing the response to selection are important as they could have substantial economic impacts on breeding programs. Therefore, the objective of the current study was to investigate the impact of selectively genotyping selection candidates on the selection response using simulation. Populations were simulated to mimic the genome and population structure of a swine and cattle population undergoing selection on an index comprised of the estimated breeding values (EBV) for 2 genetically correlated quantitative traits. Ten generations were generated and genotyping began generation 7. Two phenotyping scenarios were simulated that assumed the first trait was recorded early in life on all individuals and the second trait was recorded on all versus a random subset of the individuals. The EBV were generated from a bivariate animal model. Multiple genotyping scenarios were generated that ranged from not genotyping any selection candidates, a proportion of the selection candidates based on either their index value or chosen at random, and genotyping all selection candidates. An interim index value was utilized to decide who to genotype for the selective genotype strategy. The interim value assumed only the first trait was observed and the only genotypic information available was on animals in previous generations. Within each genotyping scenario 25 replicates were generated. Within each genotyping scenario the mean response per generation and the degree to which EBV were inflated/deflated was calculated. Across both species and phenotyping strategies, the plateau of diminishing returns was observed when 60% of the selection candidates with the largest index values were genotyped. When randomly genotyping selection candidates, either 80 or 100% of the selection candidates needed to be genotyped for there not to be a reduction in the index response. Across both populations, no differences in the degree that EBV were inflated/deflated for either trait 1 or 2 were observed between nongenotyped and genotyped animals. The current study has shown that animals can be selectively genotyped in order to optimize the response to selection as a function of the cost to conduct a breeding program using single-step genomic best linear unbiased prediction

Effects of Feeding Increasing Standardized Ileal Digestible Lysine on Growth Performance of 26- to 300-lb PIC Line 800-Sired Pigs

The objective of this study was to evaluate the growth performance and economic returns of PIC 800 × 1050 pigs fed increasing SID Lys from approximately 26 to 300 lb. Pens of pigs were blocked by BW and randomly assigned to 1 of 5 dietary treatments in a randomized complete block design with 26 pigs per pen and 16 pens per treatment. Pens were provided 1 of 5 dietary treatments with increasing SID Lys at 85, 93, 100, 107, and 115% of current PIC recommendations within 6 different phases. Two base diets containing low Lys and high Lys were blended to meet target SID Lys levels for each treatment diet within phase. For the overall experimental period (d 0 to 143), feeding increasing SID Lys improved (linear, P ≤ 0.007) ADG and F/G, but did not impact ADFI (P \u3e 0.10). For carcass characteristics, a tendency (linear, P = 0.067) for increased HCW of pigs that were provided increasing SID Lys was observed. However, there was no evidence for differences (P \u3e 0.10) across treatments in carcass yield, backfat depth, loin depth, or carcass lean percentage. Increasing SID Lys of the diets increased (linear, P \u3c 0.001) feed cost and feed cost per lb of gain. There was no evidence of difference (P \u3e 0.10) in revenue for either ingredient price scenario, thus, feeding increasing levels of SID Lys reduced (linear, P \u3c 0.001) income over feed cost (IOFC) in both scenarios. The linear model (LM) served as the best fit for both growth and economic parameters. The LM model predicted maximum ADG and minimal F/G at levels greater than 115% of PIC’s current SID Lys recommendations. For IOFC, the LM model predicted maximum profitability at or below 85% of PIC’s current Lys recommendations. In conclusion, the optimal SID Lys level for PIC 800 × 1050 pigs from 26- to 300-lb depends upon the response criteria, with growth performance maximized at levels at or above 115% of PIC’s recommendation for SID Lys; however, economic responses were maximized at or below 85% of PIC’s current SID Lys recommendations

Effects of Standardized Ileal Digestible Threonine to Lysine Ratio on Growth Performance of PIC Line 337 × 1050 Pigs

The objective of this research was to evaluate the impact of varying SID Thr:Lys ratios on growth performance, removals, and mortality rates of late-nursery, grower, and finishing PIC 337 × 1050 pigs. In each experiment, pens of pigs were blocked by BW and randomly assigned to 1 of 5 dietary treatments in a randomized complete block design with 19 to 27 pigs per pen and 8, 7, and 7 replications per treatment in Exp. 1, 2, and 3, respectively. In Exp. 1, 987 pigs (initially 26.0 ± 0.70 lb) were used from 26 to 54 lb. In Exp. 2, 875 pigs (initially 95.5 ± 1.17 lb) were used from 95 to 155 lb. In Exp. 3, 824 pigs (initially 224.4 ± 1.85 lb) were used from 224 to 297 lb. Pens were randomly assigned to 1 of 5 dietary treatments with increasing SID Thr:Lys ratios at 53, 58, 62, 65, and 68% in Exp. 1 and 2, and 56.5, 60, 64, 68, and 72.5% in Exp. 3. Diets were corn-soybean meal-based. Diets with the lowest and highest Thr:Lys ratios were blended to achieve the target SID Thr:Lys treatments in each experiment. Between experiments, all pens of pigs were placed on a common diet for 23 (Exp. 1 and 2) and 32 d (Exp. 2 and 3) to provide opportunity for compensatory growth prior to initiation of the next experiment. In Exp. 1 (26 to 54 lb), ADG and final BW increased linearly (P ≤ 0.006) while ADFI, Thr intake/d, and Thr intake/kg of gain increased quadratically (P ≤ 0.001). Overall, F/G improved (quadratic, P ≤ 0.001) as Thr:Lys ratio increased. Additionally, Lys intake/d increased (quadratic, P \u3c 0.001) while Lys intake/ kg of gain decreased (quadratic, P\u3c 0.001) with increasing Thr:Lys ratio. The quadratic polynomial (QP) model predicted greater than 68% SID Thr:Lys was required for ADG from 26 to 54 lb, while a QP model suggested that minimum F/G was achieved at 62.1% SID Thr:Lys. In Exp. 2 (95 to 155 lb), ADG, final BW, Thr intake/d, and Thr intake/kg of gain increased (linear, P ≤ 0.05) and F/G improved (linear, P = 0.030) as dietary Thr:Lys increased. Moreover, Lys intake/kg of gain decreased (linear, P = 0.023) with increasing Thr:Lys ratio. For model analysis, QP models suggested optimum ADG and F/G were achieved at levels greater than 68% SID Thr:Lys. However, similar fitting broken-line quadratic (BLQ) and broken-line linear (BLL) models predicted no further improvement to F/G and ADG beyond 61 and 67% SID Thr:Lys, respectively. In Exp. 3 (224 to 297 lb), increasing SID Thr:Lys increased (linear, P ≤ 0.001) Thr intake/d and Thr intake/kg of gain. In addition, increasing SID Thr:Lys ratios tended (P ≤ 0.086) to quadratically increase (P≤ 0.086) ADFI and BW of pigs at the second marketing event. However, no other response criteria were impacted (P ≥ 0.10) by dietary Thr:Lys. Due to a lack of ADG and F/G responses, prediction models were not developed. In summary, these results suggest the optimal SID Thr:Lys level for 26- to 54-lb pigs is 62.1% for feed efficiency and greater than 68% for ADG. From 95 to 155 lb, the requirement was predicted at or above 61 and 67% SID Thr:Lys for F/G and ADG, respectively. However, with the variation in response criteria in Exp. 3 (224 to 297 lb), we were unable to statistically define a requirement estimate

Effects of Standardized Ileal Digestible Tryptophan to Lysine Ratio on Growth Performance of PIC Line 337 × 1050 Pigs

The objective of these experiments was to evaluate the impact of varying SID Trp:Lys ratios on growth performance, removals, and mortality rates of PIC 337 × 1050 finishing pigs. In each experiment, pens of pigs were blocked by BW and randomly assigned to 1 of 5 dietary treatments in a randomized complete block design with 22 to 27 pigs per pen and 6 or 7 replications per treatment. In Exp. 1, 840 pigs (initially 101.2 ± 2.08 lb) were used from 101 to 161 lb. In Exp. 2, 801 pigs (initially 219.8 ± 3.44 lb) were used from 220 to 281 lb. Dietary treatments were corn-soybean meal-based with 30 or 20% DDGS (Exp. 1 and 2, respectively) and contained increasing SID Trp:Lys ratios at 15, 17.5, 19, 21, and 23%. Diets containing low and high Trp:Lys ratios were blended to achieve the target SID Trp:Lys treatment levels in Exp. 1, while diets containing low, medium, and high Trp:Lys ratios were blended to achieve the target SID Trp:Lys treatment levels in Exp. 2. Between experiments, all pens of pigs were placed on a common diet for 27 d and pens were reallotted to dietary treatment at the start of Exp. 2. In Exp. 1, increasing the SID Trp:Lys ratio increased (quadratic, P ≤ 0.008) ADG, ADFI, and final BW and improved (quadratic, P = 0.007) F/G. As expected, increasing SID Trp:Lys increased (linear, P \u3c 0.001) Trp intake, g/d. In addition, Trp intake per kg of gain and Lys intake/d increased (quadratic, P ≤ 0.009), while Lys intake per kg of gain decreased (quadratic, P = 0.008) with increasing SID Trp:Lys ratio. There was no difference between Trp:Lys ratios on the percentage of removals, mortalities, or total removals (P \u3e 0.10). For model analysis in 101- to 161-lb pigs, the developed broken-line linear models suggested no further improvement to ADG and F/G beyond 19.0 and 19.3% SID Trp:Lys, respectively. Meanwhile, a similar fitting quadratic polynomial (QP) model suggested minimum F/G was achieved at 21.5% SID Trp:Lys. In Exp. 2, increasing the SID Trp:Lys ratio increased (linear, P ≤ 0.001) Trp intake and Trp intake per kg of gain (quadratic, P = 0.050). However, no other observed response criteria were significantly impacted (P≥ 0.10). Models to predict optimal Trp:Lys ratios were not analyzed for 220- to 281-lb pigs due to the lack of observed differences for ADG and F/G. In summary, these results suggest the optimal SID Trp:Lys level for 101- to 161-lb pigs was predicted at or above 19.0 and 19.3% SID Trp:Lys for ADG and F/G, respectively. With the variation in response criteria observed in Exp. 2 (220 to 281 lb), we were unable to statistically define a requirement estimate

Development of a prototype clinical decision support tool for osteoporosis disease management: a qualitative study of focus groups

<p>Abstract</p> <p>Background</p> <p>Osteoporosis affects over 200 million people worldwide, and represents a significant cost burden. Although guidelines are available for best practice in osteoporosis, evidence indicates that patients are not receiving appropriate diagnostic testing or treatment according to guidelines. The use of clinical decision support systems (CDSSs) may be one solution because they can facilitate knowledge translation by providing high-quality evidence at the point of care. Findings from a systematic review of osteoporosis interventions and consultation with clinical and human factors engineering experts were used to develop a conceptual model of an osteoporosis tool. We conducted a qualitative study of focus groups to better understand physicians' perceptions of CDSSs and to transform the conceptual osteoporosis tool into a functional prototype that can support clinical decision making in osteoporosis disease management at the point of care.</p> <p>Methods</p> <p>The conceptual design of the osteoporosis tool was tested in 4 progressive focus groups with family physicians and general internists. An iterative strategy was used to qualitatively explore the experiences of physicians with CDSSs; and to find out what features, functions, and evidence should be included in a working prototype. Focus groups were conducted using a semi-structured interview guide using an iterative process where results of the first focus group informed changes to the questions for subsequent focus groups and to the conceptual tool design. Transcripts were transcribed verbatim and analyzed using grounded theory methodology.</p> <p>Results</p> <p>Of the 3 broad categories of themes that were identified, major barriers related to the accuracy and feasibility of extracting bone mineral density test results and medications from the risk assessment questionnaire; using an electronic input device such as a Tablet PC in the waiting room; and the importance of including well-balanced information in the patient education component of the osteoporosis tool. Suggestions for modifying the tool included the addition of a percentile graph showing patients' 10-year risk for osteoporosis or fractures, and ensuring that the tool takes no more than 5 minutes to complete.</p> <p>Conclusions</p> <p>Focus group data revealed the facilitators and barriers to using the osteoporosis tool at the point of care so that it can be optimized to aid physicians in their clinical decision making.</p

Expert range maps of global mammal distributions harmonised to three taxonomic authorities

Aim: Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW). Location: Global. Taxon: All extant mammal species. Methods: Range maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species). Results: Range maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use. Main conclusion: Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.Fil: Marsh, Charles J.. Yale University; Estados UnidosFil: Sica, Yanina. Yale University; Estados UnidosFil: Burguin, Connor. University of New Mexico; Estados UnidosFil: Dorman, Wendy A.. University of Yale; Estados UnidosFil: Anderson, Robert C.. University of Yale; Estados UnidosFil: del Toro Mijares, Isabel. University of Yale; Estados UnidosFil: Vigneron, Jessica G.. University of Yale; Estados UnidosFil: Barve, Vijay. University Of Florida. Florida Museum Of History; Estados UnidosFil: Dombrowik, Victoria L.. University of Yale; Estados UnidosFil: Duong, Michelle. University of Yale; Estados UnidosFil: Guralnick, Robert. University Of Florida. Florida Museum Of History; Estados UnidosFil: Hart, Julie A.. University of Yale; Estados UnidosFil: Maypole, J. Krish. University of Yale; Estados UnidosFil: McCall, Kira. University of Yale; Estados UnidosFil: Ranipeta, Ajay. University of Yale; Estados UnidosFil: Schuerkmann, Anna. University of Yale; Estados UnidosFil: Torselli, Michael A.. University of Yale; Estados UnidosFil: Lacher, Thomas. Texas A&M University; Estados UnidosFil: Wilson, Don E.. National Museum of Natural History; Estados UnidosFil: Abba, Agustin Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Aguirre, Luis F.. Universidad Mayor de San Simón; BoliviaFil: Arroyo Cabrales, Joaquín. Instituto Nacional de Antropología E Historia, Mexico; MéxicoFil: Astúa, Diego. Universidade Federal de Pernambuco; BrasilFil: Baker, Andrew M.. Queensland University of Technology; Australia. Queensland Museum; AustraliaFil: Braulik, Gill. University of St. Andrews; Reino UnidoFil: Braun, Janet K.. Oklahoma State University; Estados UnidosFil: Brito, Jorge. Instituto Nacional de Biodiversidad; EcuadorFil: Busher, Peter E.. Boston University; Estados UnidosFil: Burneo, Santiago F.. Pontificia Universidad Católica del Ecuador; EcuadorFil: Camacho, M. Alejandra. Pontificia Universidad Católica del Ecuador; EcuadorFil: de Almeida Chiquito, Elisandra. Universidade Federal do Espírito Santo; BrasilFil: Cook, Joseph A.. University of New Mexico; Estados UnidosFil: Cuéllar Soto, Erika. Sultan Qaboos University; OmánFil: Davenport, Tim R. B.. Wildlife Conservation Society; TanzaniaFil: Denys, Christiane. Muséum National d'Histoire Naturelle; FranciaFil: Dickman, Christopher R.. The University Of Sydney; AustraliaFil: Eldridge, Mark D. B.. Australian Museum; AustraliaFil: Fernandez Duque, Eduardo. University of Yale; Estados UnidosFil: Francis, Charles M.. Environment And Climate Change Canada; CanadáFil: Frankham, Greta. Australian Museum; AustraliaFil: Freitas, Thales. Universidade Federal do Rio Grande do Sul; BrasilFil: Friend, J. Anthony. Conservation And Attractions; AustraliaFil: Giannini, Norberto Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; ArgentinaFil: Gursky-Doyen, Sharon. Texas A&M University; Estados UnidosFil: Hackländer, Klaus. Universitat Fur Bodenkultur Wien; AustriaFil: Hawkins, Melissa. National Museum of Natural History; Estados UnidosFil: Helgen, Kristofer M.. Australian Museum; AustraliaFil: Heritage, Steven. University of Duke; Estados UnidosFil: Hinckley, Arlo. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Holden, Mary. American Museum of Natural History; Estados UnidosFil: Holekamp, Kay E.. Michigan State University; Estados UnidosFil: Humle, Tatyana. University Of Kent; Reino UnidoFil: Ibáñez Ulargui, Carlos. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Jackson, Stephen M.. Australian Museum; AustraliaFil: Janecka, Mary. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Jenkins, Paula. Natural History Museum; Reino UnidoFil: Juste, Javier. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Leite, Yuri L. R.. Universidade Federal do Espírito Santo; BrasilFil: Novaes, Roberto Leonan M.. Universidade Federal do Rio de Janeiro; BrasilFil: Lim, Burton K.. Royal Ontario Museum; CanadáFil: Maisels, Fiona G.. Wildlife Conservation Society; Estados UnidosFil: Mares, Michael A.. Oklahoma State University; Estados UnidosFil: Marsh, Helene. James Cook University; AustraliaFil: Mattioli, Stefano. Università degli Studi di Siena; ItaliaFil: Morton, F. Blake. University of Hull; Reino UnidoFil: Ojeda, Agustina Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaFil: Ordóñez Garza, Nicté. Instituto Nacional de Biodiversidad; EcuadorFil: Pardiñas, Ulises Francisco J.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Pavan, Mariana. Universidade de Sao Paulo; BrasilFil: Riley, Erin P.. San Diego State University; Estados UnidosFil: Rubenstein, Daniel I.. University of Princeton; Estados UnidosFil: Ruelas, Dennisse. Museo de Historia Natural, Lima; PerúFil: Schai-Braun, Stéphanie. Universitat Fur Bodenkultur Wien; AustriaFil: Schank, Cody J.. University of Texas at Austin; Estados UnidosFil: Shenbrot, Georgy. Ben Gurion University of the Negev; IsraelFil: Solari, Sergio. Universidad de Antioquia; ColombiaFil: Superina, Mariella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Tsang, Susan. American Museum of Natural History; Estados UnidosFil: Van Cakenberghe, Victor. Universiteit Antwerp; BélgicaFil: Veron, Geraldine. Université Pierre et Marie Curie; FranciaFil: Wallis, Janette. Kasokwa-kityedo Forest Project; UgandaFil: Whittaker, Danielle. Michigan State University; Estados UnidosFil: Wells, Rod. Flinders University.; AustraliaFil: Wittemyer, George. State University of Colorado - Fort Collins; Estados UnidosFil: Woinarski, John. Charles Darwin University; AustraliaFil: Upham, Nathan S.. University of Yale; Estados UnidosFil: Jetz, Walter. University of Yale; Estados Unido

Expert range maps of global mammal distributions harmonised to three taxonomic authorities

AimComprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW).LocationGlobal.TaxonAll extant mammal species.MethodsRange maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species).ResultsRange maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use.Main conclusionExpert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis