9 research outputs found
Bovine chromosome 20: milk production QTL and candidate gene analysis in the Italian Holstein-Friesian breed
Bovine chromosome 20 (BTA20) was studied to identify QTL for milk yield and protein percentage in the Italian Holstein-Friesian breed using a selective milk DNA pooling strategy in a daughter design with sire haplotype analysis. Several QTL were identified. The effect of the known GHR F279Y and PRLR S18N mutations were in for the most part confirmed. However, it was also shown that these markers cannot explain all significant effects observed on BTA20 for the investigated traits
The BovMAS Consortium: investigation of bovine chromosome 14 for quantitative trait loci affecting milk production and quality traits in the Italian Holstein Friesian breed
Many studies have demonstrated that quantitative trait loci (QTL) can be identified and mapped in commercial dairy cattle populations using genetic markers in daughter and granddaughter designs.The final objective of these studies is to identify genes or markers that can be used in breeding schemes via marker assisted selection (MAS)
Validation of a novel multivariate method of defining HIV-associated cognitive impairment
Background. The optimum method of defining cognitive impairment in virally suppressed people living with HIV is unknown.
We evaluated the relationships between cognitive impairment, including using a novel multivariate method (NMM), patient–
reported outcome measures (PROMs), and neuroimaging markers of brain structure across 3 cohorts.
Methods. Differences in the prevalence of cognitive impairment, PROMs, and neuroimaging data from the COBRA, CHARTER,
and POPPY cohorts (total n = 908) were determined between HIV-positive participants with and without cognitive impairment defined using the HIV-associated neurocognitive disorders (HAND), global deficit score (GDS), and NMM criteria.
Results. The prevalence of cognitive impairment varied by up to 27% between methods used to define impairment (eg, 48% for HAND
vs 21% for NMM in the CHARTER study). Associations between objective cognitive impairment and subjective cognitive complaints generally were weak. Physical and mental health summary scores (SF-36) were lowest for NMM-defined impairment (P < .05).
There were no differences in brain volumes or cortical thickness between participants with and without cognitive impairment defined using the HAND and GDS measures. In contrast, those identified with cognitive impairment by the NMM had reduced mean
cortical thickness in both hemispheres (P < .05), as well as smaller brain volumes (P < .01). The associations with measures of white
matter microstructure and brain-predicted age generally were weaker.
Conclusion. Different methods of defining cognitive impairment identify different people with varying symptomatology and
measures of brain injury. Overall, NMM-defined impairment was associated with most neuroimaging abnormalities and poorer selfreported health status. This may be due to the statistical advantage of using a multivariate approac