Enhancing Learning through Assessment: Case Study using feedback from a Human Dimension Survey

The Department of Wildlife and Fisheries Sciences at Texas A&M University is in the process of developing an assessment plan for measuring and evaluating each academic degree program (including student learning outcomes) in order to achieve objectives for institutional effectiveness. Assessment is a necessary component of any truly dynamic and progressive educational program. Assessment by evaluation can enhance student learning as well as augment instruction given by the professor. It also allows professors to determine which students reach or exceed learning targets and inform them so students can work to improve their weaknesses. Because there is no assessment plan currently in place for Texas A and M University's Department of Wildlife and Fisheries Sciences, I evaluated results from a previously developed survey that had been given in multiple years to students enrolled in Wildlife Conservation and Management (WFSC 201), an entrance level course of the department. In 2008 I administered the survey to students enrolled in WFSC 201 and to senior-level students enrolled in Conservation Biology and Wildlife Habitat Management (WFSC 406), an upper level course, to evaluate undergraduate students' beliefs on various wildlife issues, interest in animals, and knowledge status of endangered species. The research presented in this thesis contributes a general overview of assessment as it relates to undergraduate degree programs in wildlife and fisheries sciences. The focus was in particular to the evaluation of student conservation issues, animal interest, and species knowledge as it relates to student background (student classification, gender, hometown population size, and participation in youth groups. The results from analyses of responses to specific questions from a survey administered to undergraduates in the Department of Wildlife and Fisheries Sciences at Texas A&M University suggest that conservation beliefs and animal interest were highly correlated with gender and hometown population size. Students responding as males and having small hometown population size were more concerned about issues related to land or wildlife usage by humans and students responding as females and having large hometown population sizes were more concerned about issues related to habitat degradation and species viability. Males were also interested in mostly game species and females were interested in those of conservation, domestic, and herptiles. Lastly, the results from the knowledge question suggest that males attain and retain more knowledge of endangered species over females, and this relationship remains the same in non seniors and seniors. These results should be useful to the faculty currently and in the future as they develop an effective departmental assessment plan for the Department of Wildlife and Fisheries Sciences at Texas A&M University

The complex genetics of gait speed:Genome-wide meta-analysis approach

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging

Association of common genetic variants with brain microbleeds

OBJECTIVE: To identify common genetic variants associated with the presence of brain microbleeds (BMBs). METHODS: We performed geno

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p[SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI] = 4.4 × 10-10; p[SSBI] = 1.2 × 10-4), diabetes (p[BI] = 1.7 × 10-8; p[SSBI] = 2.8 × 10-3), previous cardiovascular disease (p[BI] = 1.0 × 10-18; p[SSBI] = 2.3 × 10-7), stroke (p[BI] = 3.9 × 10-69; p[SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p[BI] = 1.43 × 10-157; p[SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based study - Supplemental data

This file contains the supplemental data to the article entitled "Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based study". It contains additional methods paragraphs 1 to 4, supplemental tables 1 to 17, supplemental figures 1 to 7, and additional reference

Data from: Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Objective: We explored genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely small sub-cortical BI (SSBI), in eighteen population-based cohorts (N=20,949) from five ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in seven population-based cohorts (N=6,862, 1,483 with BI, 630 with SBBI), and tested associations with related phenotypes including ischemic stroke and pathologically-defined BI. Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, P=1.77×10-8 and LINC00539/ZDHHC20, P=5.82×10-9. Both have been associated with blood pressure (BP) related phenotypes, but did not replicate in the smaller follow-up sample nor show associations with related phenotypes. Age and sex-adjusted associations with BI and SSBI were observed for BP traits (P-value for BI, P[BI]=9.38×10-25; P[SSBI]=5.23×10-14 for hypertension), smoking (P[BI]=4.4×10-10; P[SSBI]=1.2×10-4), diabetes (P[BI]=1.7×10-8; P[SSBI]=2.8×10-3), previous cardiovascular disease (P[BI]=1.0×10-18; P[SSBI]=2.3×10-7), stroke (P[BI]=3.9×10-69; P[SSBI]=3.2×10-24), and MRI-defined white matter hyperintensity burden (P[BI]=1.43×10-157; P[SSBI]=3.16×10-106), but not with body-mass-index or cholesterol. GRS of BP traits were associated with BI and SSBI (P≤0.0022), without indication of directional pleiotropy. Conclusions: In this multi-ethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI