Waterfowl Population Status, 2010

In the traditional survey area, which includes strata 1‒18, 20‒50, and 75‒77, the total duck population estimate was 40.9 ± 0.7 [SE] million birds. This estimate was similar to last year\u27s estimate of 42.0 ± 0.7 million birds and was 21% above the long-term average (1955‒2009). Estimated mallard (Anas platyrhynchos) abundance was 8.4 ± 0.3 million birds, which was similar to the 2009 estimate of 8.5 ± 0.2 million birds and 12% above the long-term average. Estimated abundance of gadwall (A. strepera; 3.0 ± 0.2 million) was similar to the 2009 estimate and 67% above the long-term average. Estimated abundance of American wigeon (A. americana; 2.4 ± 0.1 million) was similar to 2009 and the long-term average. The estimated abundance of green-winged teal (A. crecca) was 3.5 ± 0.2 million, which was similar to the 2009 estimate and 78% above their longterm average of 1.9 ± 0.02 million. The estimate of blue-winged teal abundance (A. discors) was 6.3 ± 0.4 million, which was 14% below the 2009 estimate and 36% above their long-term average of 4.7 ± 0.04 million. The estimate for northern pintails (A. acuta; 3.5 ± 0.2 million) was similar to the 2009 estimate, and 13% below the long-term average of 4.0 ± 0.04 million. Estimates of northern shovelers (A. clypeata; 4.1 ± 0.2 million) and redheads (Aythya americana; 1.1 ± 0.1 million) were similar to their 2009 estimates and were 76% and 63% above their long-term averages of 2.3 ± 0.02 million and 0.7 ± 0.01 million, respectively. The canvasback estimate (A. valisineria; 0.6 ± 0.05 million) was similar to the 2009 estimate and to the long-term average. The scaup estimate (A. affinis and A. marila combined; 4.2 ± 0.2 million) was similar to that of 2009 and 16% below the long-term average of 5.1 ± 0.05 million. Habitat conditions during the 2010 Waterfowl Breeding Population and Habitat Survey were characterized by average to below-average moisture, a mild winter, and early spring across the traditional and eastern survey areas. The total pond estimate (Prairie Canada and U.S. combined) was 6.7 ± 0.2 million. This was similar to the 2009 estimate and 34% above the long-term average (1974‒2009) of 5.0 ± 0.03 million ponds. The 2010 estimate of ponds in Prairie Canada was 3.7 ± 0.2 million. This was similar to last year\u27s estimate (3.6 ± 0.1 million) and to the long-term average (1961‒2009; 3.4 ± 0.03 million). The 2010 pond estimate for the north central U.S. was 2.9 ± 0.1 million, which was similar to last year\u27s estimate (2.9 ± 0.1 million) and 87% above the long-term average (1974‒2009; 1.6 ± 0.02 million). The projected mallard fall-flight index is 10.3 ± 0.9 million birds. The eastern survey area was restratifed in 2005 and is now composed of strata 51‒72. Estimates of mallards, scaup, scoters (black [Melanitta nigra], white-winged [M. fusca], and surf [M. perspicillata]), green-winged teal, American wigeon, bufflehead (Bucephala albeola), ring-necked duck (Aythya collaris), and goldeneyes (common [B. clangula] and Barrow\u27s [B. islandica]) were all similar to their 2009 estimates and long-term averages. The merganser (red-breasted [Mergus serrator], common [M. merganser], and hooded [Lophodytes cucullatus]) estimate was 386.4 thousand, which was 15% below the 2009 estimate, and 14% below the long-term average of 450.8 thousand. The American black duck (Anas rubripes) estimate was similar to the 2009 estimate and 7% below the long-term average of 478.9 thousand

Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures

BACKGROUND: Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures. RESULTS: We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life. CONCLUSION: We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches

BACKGROUND: Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. RESULTS: The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. CONCLUSION: We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions

Risk Alleles for Systemic Lupus Erythematosus in a Large Case-Control Collection and Associations with Clinical Subphenotypes

Systemic lupus erythematosus (SLE) is a genetically complex disease with heterogeneous clinical manifestations. Recent studies have greatly expanded the number of established SLE risk alleles, but the distribution of multiple risk alleles in cases versus controls and their relationship to subphenotypes have not been studied. We studied 22 SLE susceptibility polymorphisms with previous genome-wide evidence of association (p<5×10−8) in 1919 SLE cases from 9 independent Caucasian SLE case series and 4813 independent controls. The mean number of risk alleles in cases was 15.1 (SD 3.1) while the mean in controls was 13.1 (SD 2.8), with trend p = 4×10−128. We defined a genetic risk score (GRS) for SLE as the number of risk alleles with each weighted by the SLE risk odds ratio (OR). The OR for high-low GRS tertiles, adjusted for intra-European ancestry, sex, and parent study, was 4.4 (95% CI 3.8–5.1). We studied associations of individual SNPs and the GRS with clinical manifestations for the cases: age at diagnosis, the 11 American College of Rheumatology classification criteria, and double-stranded DNA antibody (anti-dsDNA) production. Six subphenotypes were significantly associated with the GRS, most notably anti-dsDNA (ORhigh-low = 2.36, p = 9e−9), the immunologic criterion (ORhigh-low = 2.23, p = 3e−7), and age at diagnosis (ORhigh-low = 1.45, p = 0.0060). Finally, we developed a subphenotype-specific GRS (sub-GRS) for each phenotype with more power to detect cumulative genetic associations. The sub-GRS was more strongly associated than any single SNP effect for 5 subphenotypes (the above plus hematologic disorder and oral ulcers), while single loci are more significantly associated with renal disease (HLA-DRB1, OR = 1.37, 95% CI 1.14–1.64) and arthritis (ITGAM, OR = 0.72, 95% CI 0.59–0.88). We did not observe significant associations for other subphenotypes, for individual loci or the sub-GRS. Thus our analysis categorizes SLE subphenotypes into three groups: those having cumulative, single, and no known genetic association with respect to the currently established SLE risk loci

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment