TB193: The Cost of Producing Milk in Maine: Results from the 2005 Dairy Cost of Production Survey

This report summarizes the results of the 2005 Dairy Cost of Production survey implemented by The University of Maine and the Maine Milk Commission. This study summarizes data collected over the 2004 production year. Funding for this report was provided by the Maine Milk Commission. Analysis and discussion of the data in this report centers on an industry-wide group and three smaller sets called clusters. Cluster analysis attempts to identify groups of farms from within the sample of survey respondents that have relatively homogenous characteristics.https://digitalcommons.library.umaine.edu/aes_techbulletin/1015/thumbnail.jp

Economic and Fiscal Impacts of Proposed LNG Facility in Robbinston, Maine

The purpose of this study is to examine the economic and fiscal impacts of the proposed Downeast LNG facility on the Town of Robbinston, Washington County, and the State of Maine. The economic impact analysis focuses on the employment and income that are associated with the LNG facility construction and operations. The fiscal impact analysis considers additional local and state tax revenues associated with the facility, as well as increased local government expenditures that are projected to result from the LNG project. This report does not address the environmental, homeland security, or energy security impacts of the LNG facility. In addition, this report does not estimate any changes in the price of delivered natural gas in Maine that could potentially result from a new major energy supplier

TB204: Organic Milk Production in Maine: Attributes, Costs, and Returns

This report summarizes attributes, costs, and returns for organic dairy farms in Maine that responded to the 2008 dairy cost of production survey. This survey and analysis was conducted by the University of Maine in cooperation with the Maine Milk Commission. This publication reports on data collected over the 2007 production year. Analysis and discussion of the data revolve around four categories. The first category averages all 30 organic farms to create a statewide group. The authors then broke these 30 farms into three size groups (small, medium, and large) based on the number of cows on each farm. There were nine small farms with an average of 30 cows on each farm, 10 medium farms with an average of 55 cows each, and 11 large farms with about an average of 100 cows each. The analysis presented here discusses characteristics of the three size groups, along with the statewide group.https://digitalcommons.library.umaine.edu/aes_techbulletin/1009/thumbnail.jp

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10−8) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10−8). The top IBC association for SBP was rs2012318 (P= 6.4 × 10−6) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10−6) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexit

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity

Causal effect of plasminogen activator inhibitor type 1 on coronary heart disease

Background--Plasminogen activator inhibitor type 1 (PAI-1) plays an essential role in the fibrinolysis system and thrombosis. Population studies have reported that blood PAI-1 levels are associated with increased risk of coronary heart disease (CHD). However, it is unclear whether the association reflects a causal influence of PAI-1 on CHD risk. Methods and Results--To evaluate the association between PAI-1 and CHD, we applied a 3-step strategy. First, we investigated the observational association between PAI-1 and CHD incidence using a systematic review based on a literature search for PAI-1 and CHD studies. Second, we explored the causal association between PAI-1 and CHD using a Mendelian randomization approach using summary statistics from large genome-wide association studies. Finally, we explored the causal effect of PAI-1 on cardiovascular risk factors including metabolic and subclinical atherosclerosis measures. In the systematic meta-analysis, the highest quantile of blood PAI-1 level was associated with higher CHD risk comparing with the lowest quantile (odds ratio=2.17; 95% CI: 1.53, 3.07) in an age- and sex-adjusted model. The effect size was reduced in studies using a multivariable-adjusted model (odds ratio=1.46; 95% CI: 1.13, 1.88). The Mendelian randomization analyses suggested a causal effect of increased PAI-1 level on CHD risk (odds ratio=1.22 per unit increase of log-transformed PAI-1; 95% CI: 1.01, 1.47). In addition, we also detected a causal effect of PAI-1 on elevating blood glucose and high-density lipoprotein cholesterol. Conclusions--Our study indicates a causal effect of elevated PAI-1 level on CHD risk, which may be mediated by glucose dysfunction

Spatial Dependence and Omitted Variable Bias Effects on Efficiency Analysis: A Study of the Maine Dairy Industry

Firm efficiency analysis has evolved from mathematically estimating an industry\u27s mean level of efficiency and deriving producer-specific inefficiency scores to focusing on isolating pure inefficiency. Failing to isolate inefficiency attributable only to producer behavior, as opposed to other systematic factors, can affect parameter estimates and calculated efficiency scores. Misspecification bias may create significant statistical implications for frontier modeling. Scores may be biased and if so, the tendency is to under-estimate. Correlates may be misidentified due to this bias. Empirical studies have historically defined the production frontier and investigated correlates of inefficiency with a two-stage modeling process. The first-stage defines the structure of the frontier from which efficiency scores are derived. The second stage identifies systematic variation across these scores. This approach has been criticized since systematic variation found in the secondstage is indicative of omitted variable bias introduced during the first-stage. Incorporating exogenous farm-level characteristics into a one-stage frontier modeling process is hypothesized to better measure efficiency. Also, spatial information is incorporated into frontier estimation to control for the influence of a farm\u27s relative landscape position on production decisions. Von Thiinen-influenced distance-to-market measures and regionalized dummy variables have been used to capture spatial dependence in agriculture. These spatial measures are not highly refined and ignore knowledge sharing and social network externalities. If information-based externalities hold significant influence and are ignored, parameter estimates and efficiency scores are biased reflections of industry conditions. Using maximum-likelihood econometric techniques, short-run efficiency scores are estimated for Maine dairy production using farm-level data collected in 2001. All three types of efficiency are investigated: technical, allocative, and economic. Technology is represented by the Translog functional form. Efficiency scores obtained from the one- and two-stage frontiers are tested for the influence of spatial autocorrelation. Evidence of spatial clustering led to the inclusion of geo-specific variables into both the one-stage frontier and secondstage Tobit regression. This approach eliminated spatial clustering. Further comparison of the one- and two-stage modeling results finds that a larger portion of the variation of efficiency scores is accounted for by the onestage model. On average, observed production practices are found to be more efficient. Pair-wise means testing and cumulative frequency distributions support this finding. Efficiency levels are not improved for all producers. In some cases, the difference between scores is large enough to negatively affect the ranking of a particular producer. Thus, these findings suggest that rankings are sensitive to the modeling method