Identification of Circulating Proteins associated With General Cognitive Function among Middle-Aged and Older adults

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p \u3c 4.9E-5, n up to 7289). Of these, 45 were replicated using SomaScan data, and three were replicated using Olink data at Bonferroni-corrected significance. Enrichment analysis linked the proteins associated with general cognitive function to cell signaling pathways and synapse architecture. Mendelian randomization analysis implicated higher levels of NECTIN2, a protein mediating viral entry into neuronal cells, with higher Alzheimer\u27s disease (AD) risk (p = 2.5E-26). Levels of 14 other protein measures were implicated as consequences of AD susceptibility (p \u3c 2.0E-4). Proteins implicated as causes or consequences of AD susceptibility may provide new insight into the potential relationship between immunity and AD susceptibility as well as potential therapeutic targets

Genome-wide meta-analysis of muscle weakness identifies 15 susceptibility loci in older men and women.

Low muscle strength is an important heritable indicator of poor health linked to morbidity and mortality in older people. In a genome-wide association study meta-analysis of 256,523 Europeans aged 60 years and over from 22 cohorts we identify 15 loci associated with muscle weakness (European Working Group on Sarcopenia in Older People definition: n = 48,596 cases, 18.9% of total), including 12 loci not implicated in previous analyses of continuous measures of grip strength. Loci include genes reportedly involved in autoimmune disease (HLA-DQA1 p = 4 × 10-17), arthritis (GDF5 p = 4 × 10-13), cell cycle control and cancer protection, regulation of transcription, and others involved in the development and maintenance of the musculoskeletal system. Using Mendelian randomization we report possible overlapping causal pathways, including diabetes susceptibility, haematological parameters, and the immune system. We conclude that muscle weakness in older adults has distinct mechanisms from continuous strength, including several pathways considered to be hallmarks of ageing

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.Peer reviewe

Genome-wide meta-analysis of muscle weakness identifies 15 susceptibility loci in older men and women

Low muscle strength is an important heritable indicator of poor health linked to morbidity and mortality in older people. In a genome-wide association study meta-analysis of 256,523 Europeans aged 60 years and over from 22 cohorts we identify 15 loci associated with muscle weakness (European Working Group on Sarcopenia in Older People definition: n = 48,596 cases, 18.9% of total), including 12 loci not implicated in previous analyses of continuous measures of grip strength. Loci include genes reportedly involved in autoimmune disease (HLA-DQA1p = 4 × 10−17), arthritis (GDF5p = 4 × 10−13), cell cycle control and cancer protection, regulation of transcription, and others involved in the development and maintenance of the musculoskeletal system. Using Mendelian randomization we report possible overlapping causal pathways, including diabetes susceptibility, haematological parameters, and the immune system. We conclude that muscle weakness in older adults has distinct mechanisms from continuous strength, including several pathways considered to be hallmarks of ageing

Genetic and environmental associations with disease risk and drug response in Alaska Native people, and a responsive justice approach to reconciling statistical and ethical research demands.

Thesis (Ph.D.)--University of Washington, 2015Genetic research with diverse and underserved communities is important for expanding the benefits of genetic discoveries and their application to personalized medicine. In partnership with Alaska Native communities, we identified and characterized novel and known variation in CYP2C9, VKORC1, CYP4F2, CYP4F11, and GGCX, 5 genes known to affect warfarin disposition and response, in 350 Yup’ik people and 365 customer-owners of Southcentral Foundation. Through resequencing and targeted genotyping, we identified two common novel variants M1L and N218I in CYP2C9 and high frequencies of the VKORC1 haplotype (-1639G>A and 1173C>T) that are expected to result in increased warfarin sensitivity. We also observed high frequencies of CYP4F2*3, which may increase vitamin K conservation and necessitate a higher warfarin dose to achieve the desired anticoagulation effect. Individual patient needs will depend on a complex mixture of genetic traits. Because of seasonal variation in sunlight exposure, vitamin D deficiency is a concern for people living in circumpolar regions. We characterized 25(OH)D3 concentrations in 743 Yup’ik people living in the Yukon Kuskokwim delta of southwestern Alaska and identified sources of inter-individual variability, including season of sample collection, genetic variation in CYP2R1 and DHCR7, age, gender, body mass index, the degree of consumption of the traditional diet, and inland or coastal geography of the community. Yup’ik participants on average had adequate concentrations of 25(OH)D3 (31.1 +/- 0.9 ng/mL), but a younger age ( 33 years), lower levels of a biomarker of traditional food consumption, and greater fluctuation with changes in sunlight exposure. Younger Yup’ik participants may be at increased risk for adverse outcomes associated with vitamin D deficiency, especially during seasons of low sunlight exposure. Finally, in genetic epidemiology research, genome-wide markers or pedigree information is used to adjust for population substructure and prevent confounding of results. Population substructure and the concerns of communities with respect to the methods used to adjust for it are described. A responsive justice framework is suggested as a tool to approach these conflicting demands of research, presenting as an example the stratification of participants by self-identified ancestral language group to approximate the statistical adjustments needed for population stratification

The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease

BackgroundMany machine learning approaches are limited to classification of outcomes rather than longitudinal prediction. One strategy to use machine learning in clinical risk prediction is to classify outcomes over a given time horizon. However, it is not well-known how to identify the optimal time horizon for risk prediction. ObjectiveIn this study, we aim to identify an optimal time horizon for classification of incident myocardial infarction (MI) using machine learning approaches looped over outcomes with increasing time horizons. Additionally, we sought to compare the performance of these models with the traditional Framingham Heart Study (FHS) coronary heart disease gender-specific Cox proportional hazards regression model. MethodsWe analyzed data from a single clinic visit of 5201 participants of a cardiovascular health study. We examined 61 variables collected from this baseline exam, including demographic and biologic data, medical history, medications, serum biomarkers, electrocardiographic, and echocardiographic data. We compared several machine learning methods (eg, random forest, L1 regression, gradient boosted decision tree, support vector machine, and k-nearest neighbor) trained to predict incident MI that occurred within time horizons ranging from 500-10,000 days of follow-up. Models were compared on a 20% held-out testing set using area under the receiver operating characteristic curve (AUROC). Variable importance was performed for random forest and L1 regression models across time points. We compared results with the FHS coronary heart disease gender-specific Cox proportional hazards regression functions. ResultsThere were 4190 participants included in the analysis, with 2522 (60.2%) female participants and an average age of 72.6 years. Over 10,000 days of follow-up, there were 813 incident MI events. The machine learning models were most predictive over moderate follow-up time horizons (ie, 1500-2500 days). Overall, the L1 (Lasso) logistic regression demonstrated the strongest classification accuracy across all time horizons. This model was most predictive at 1500 days follow-up, with an AUROC of 0.71. The most influential variables differed by follow-up time and model, with gender being the most important feature for the L1 regression and weight for the random forest model across all time frames. Compared with the Framingham Cox function, the L1 and random forest models performed better across all time frames beyond 1500 days. ConclusionsIn a population free of coronary heart disease, machine learning techniques can be used to predict incident MI at varying time horizons with reasonable accuracy, with the strongest prediction accuracy in moderate follow-up periods. Validation across additional populations is needed to confirm the validity of this approach in risk prediction

Genetic associations with a fever after measles-containing vaccines

Children have elevated fever risk 1 to 2 weeks after the first dose of a measles-containing vaccine (MCV), which is likely affected by genetic, immunologic, and clinical factors. Fever after MCV is associated with febrile seizures, though may also be associated with higher measles antibody titers. This exploratory study investigated genetic and immunologic associations with a fever after MCV. Concurrent with a randomized Phase 3 clinical trial of 12–15-month-olds who received their first measles-mumps-rubella (MMR) vaccine in which parents recorded post-vaccination temperatures daily, we consented a subset to collect additional blood and performed human leukocyte antigens (HLA) typing. Association between fever 5–12 days after MMR (“MMR-associated”) and HLA type was assessed using logistic regression. We compared 42-day post-vaccination geometric mean titers (GMT) to measles between children who did and did not have fever using a t-test. We enrolled 86 children and performed HLA typing on 82; 13 (15.1%) had MMR-associated fever. Logistic regressions identified associations between MMR-associated fever and HLA Class I loci A-29:02 (P = .036), B-57:01 (P = .018), C-06:02 (P = .006), C-14:02 (P = .022), and Class II loci DRB1-15 (P = .045). However, Bonferroni's adjustment for multiple comparisons suggests that these associations could have been due to chance. Ninety-eight percent of children had protective antibody titers to measles; however, GMT was higher among those with fever compared with children without fever (P = .006). Fever after the measles vaccine correlated with genetic factors and higher immune response. This study suggests a possible genetic susceptibility to MMR-associated fever