1992 SANTEE COOPER ANNUAL REPORT

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

1993 Santee Cooper Annual Report

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

SANTEE COOPER ANNUAL REPORT 1994

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

Santee Cooper 1995 Annual Report

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

1997 Annual Report

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

SANTEE COOPER SOUTH CAROLINA'S COMMON THREAD 1998 Annual Report

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

SANTEE COOPER 1999 ANNUAL REPORT

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

2000 ANNUAL REPORT

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

SANTEE COOPER 2001 ANNUAL REPORT

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

Composing a SOUND FUTURE '13 ANNUAL REPORT

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

LIGHTING the PATH 2014 ANNUAL REPORT

Santee Cooper annually publishes a report with statistics, analysis, audit, leadership, and budget statements.

Effects of the orientation of the mountainside, altitude and varieties on the quality of the coffee beverage from the "Matas de Minas" region, brazilian Southeast.

Based upon qualitative parameters experiments, this study aims to investigate how the elements of the environment, where the coffee is produced, contribute to the final quality of the product. For the analyses, it was used approximately one kilogram of coffee cherry samples collected in 14municipalities previously chosen on the East side of the Minas Gerais State, Brazil. The coffee cherry samples were collected and analyzed for each of the two varieties in four levels of altitude for each exposure side of the mountain in relation to the Sun. The quality of the coffee was evaluated through the analysis of its physical characteristics and sensory analysis, popularly known as "Test of drink or Cupping" carried out by three tasters that belonging to the group of Q -Graders, according to the rules of national and international competitions of the Brazilian Association of Special Coffees (BSCA). Were performed analysis by means descriptive statistics, analysis of variance and multivariate analysis, all of them aiming to study the individual sensor y characteristics of quality of the coffee beverage from the ?Matas de Minas? region. Path coefficient analysis also was carried out for the partition of the phenotypic correlation coefficients into measures of direct and indirect effects, in order to determine the individual sensory characteristics that playeda major role in the beverage final score. The results demonstrat e that it is not possible to concl usively establish the differences among coffees evaluated with basis on varieties and environmental factors previously cited. It can be concluded that it is not recommended to associate the quality of coffee only to a specific factor whether from the environment or being it a specific of the culture of coffee. However, the cafes that were evaluated had intrinsic quality, which were derived from the specific characteristics of the ?Matas de Minas? region where they were grown.

Meta-analysis of gene-level associations for rare variants based on single-variant statistics.

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.

Combined analysis of grey matter voxel-based morphometry and white matter tract-based spatial statistics in late-life bipolar disorder.

Background: Previous magnetic resonance imaging (MRI) studies in young patients with bipolar disorder indicated the presence of grey matter concentration changes as well as microstructural alterations in white matter in various neocortical areas and the corpus callosum. Whether these structural changes are also present in elderly patients with bipolar disorder with long-lasting clinical evolution remains unclear. Methods: We performed a prospective MRI study of consecutive elderly, euthymic patients with bipolar disorder and healthy, elderly controls. We conducted a voxel-based morphometry (VBM) analysis and a tract-based spatial statistics (TBSS) analysis to assess fractional anisotropy and longitudinal, radial and mean diffusivity derived by diffusion tensor imaging (DTI). Results: We included 19 patients with bipolar disorder and 47 controls in our study. Fractional anisotropy was the most sensitive DTI marker and decreased significantly in the ventral part of the corpus callosum in patients with bipolar disorder. Longitudinal, radial and mean diffusivity showed no significant between-group differences. Grey matter concentration was reduced in patients with bipolar disorder in the right anterior insula, head of the caudate nucleus, nucleus accumbens, ventral putamen and frontal orbital cortex. Conversely, there was no grey matter concentration or fractional anisotropy increase in any brain region in patients with bipolar disorder compared with controls. Limitations: The major limitation of our study is the small number of patients with bipolar disorder. Conclusion: Our data document the concomitant presence of grey matter concentration decreases in the anterior limbic areas and the reduced fibre tract coherence in the corpus callosum of elderly patients with long-lasting bipolar disorder.

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.