Burcu F. Darst

Cardiorespiratory fitness alters the influence of a polygenic risk score on biomarkers of AD

Objective: To examine whether a polygenic risk score (PRS) derived from APOE4, CLU, and ABCA7 is associated with CSF biomarkers of Alzheimer disease (AD) pathology and whether higher cardiorespiratory fitness (CRF) modifies the association between the PRS and CSF biomarkers. Methods: Ninety-five individuals from the Wisconsin Registry for Alzheimers Prevention were included in these cross-sectional analyses. They were genotyped for APOE4, CLU, and ABCA7, from which a PRS was calculated for each participant. The participants underwent lumbar puncture for CSF collection. β-Amyloid 42 (Aβ42), Aβ40, total tau (t-tau), and phosphorylated tau (p-tau) were quantified by immunoassays, and Aβ42/Aβ40 and tau/Aβ42 ratios were computed. CRF was estimated from a validated equation incorporating sex, age, body mass index, resting heart rate, and self-reported physical activity. Covariate-adjusted regression analyses were used to test for associations between the PRS and CSF biomarkers. In addition, by including a PRS×CRF term in the models, we examined whether these associations were modified by CRF. Results: A higher PRS was associated with lower Aβ42/Aβ40 (p < 0.001), higher t-tau/Aβ42 (p = 0.012), and higher p-tau/Aβ42 (p = 0.040). Furthermore, we observed PRS × CRF interactions for Aβ42/Aβ40 (p = 0.003), t-tau/Aβ42 (p = 0.003), and p-tau/Aβ42 (p = 0.001). Specifically, the association between the PRS and these CSF biomarkers was diminished in those with higher CRF. Conclusions: In a late-middle-aged cohort, CRF attenuates the adverse influence of genetic vulnerability on CSF biomarkers. These findings support the notion that increased cardiorespiratory fitness may be beneficial to those at increased genetic risk for AD.

Pathway-Specific Polygenic Risk Scores as Predictors of Amyloid-β Deposition and Cognitive Function in a Sample at Increased Risk for Alzheimer’s Disease

Polygenic risk scores (PRSs) have been used to combine the effects of variants with small effects identified by genome-wide association studies. We explore the potential for using pathway-specific PRSs as predictors of early changes in Alzheimers disease (AD)-related biomarkers and cognitive function. Participants were from the Wisconsin Registry for Alzheimers Prevention, a longitudinal study of adults who were cognitively asymptomatic at enrollment and enriched for a parental history of AD. Using genes associated with AD in the International Genomics of Alzheimers Projects meta-analysis, we identified clusters of genes that grouped into pathways involved in amyloid-β (Aβ) deposition and neurodegeneration: Aβ clearance, cholesterol metabolism, and immune response. Weighted pathway-specific and overall PRSs were developed and compared to APOE alone. Mixed models were used to assess whether each PRS was associated with cognition in 1,200 individuals, cerebral Aβ deposition measured using amyloid ligand (Pittsburgh compound B) positron emission imaging in 168 individuals, and cerebrospinal fluid Aβ deposition, neurodegeneration, and tau pathology in 111 individuals, with replication performed in an independent sample. We found that PRSs including APOE appeared to be driven by the inclusion of APOE, suggesting that the pathway-specific PRSs used here were not more predictive than an overall PRS or APOE alone. However, pathway-specific PRSs could prove to be useful as more knowledge is gained on the genetic variants involved in specific biological pathways of AD.

BDNF Val66Met predicts cognitive decline in the Wisconsin Registry for Alzheimer's Prevention

Objective: To examine the influence of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism on longitudinal cognitive trajectories in a large, cognitively healthy cohort enriched for Alzheimer disease (AD) risk and to understand whether β-amyloid (Aβ) burden plays a moderating role in this relationship. Methods: One thousand twenty-three adults (baseline age 54.94 ± 6.41 years) enrolled in the Wisconsin Registry for Alzheimers Prevention underwent BDNF genotyping and cognitive assessment at up to 5 time points (average follow-up 6.92 ± 3.22 years). A subset (n = 140) underwent 11C-Pittsburgh compound B (PiB) scanning. Covariate-adjusted mixed-effects regression models were used to elucidate the effect of BDNF on cognitive trajectories in 4 cognitive domains, including verbal learning and memory, speed and flexibility, working memory, and immediate memory. Secondary mixed-effects regression models were conducted to examine whether Aβ burden, indexed by composite PiB load, modified any observed BDNF-related cognitive trajectories. Results: Compared to BDNF Val/Val homozygotes, Met carriers showed steeper decline in verbal learning and memory (p = 0.002) and speed and flexibility (p = 0.017). In addition, Aβ burden moderated the relationship between BDNF and verbal learning and memory such that Met carriers with greater Aβ burden showed even steeper cognitive decline (p = 0.033). Conclusions: In a middle-aged cohort with AD risk, carriage of the BDNF Met allele was associated with steeper decline in episodic memory and executive function. This decline was exacerbated by greater Aβ burden. These results suggest that the BDNF Val66Met polymorphism may play an important role in cognitive decline and could be considered as a target for novel AD therapeutics.

Heritability of Cognitive Traits Among Siblings with a Parental History of Alzheimer's Disease

Cognitive decline is one of the hallmark features of Alzheimers disease, but many studies struggle to find strong associations between cognitive function and genetic variants. In order to identify which aspects of cognition are more likely to have a strong genetic component, we assessed the heritability of various cognitive functions related to Alzheimers disease in 303 initially asymptomatic middle-aged adult siblings with a parental history of Alzheimers disease from the Wisconsin Registry for Alzheimers Prevention. Participants underwent extensive cognitive testing, and six cognitive factors were identified via factor analysis. Working Memory and Visual Learning & Memory had the highest heritability (52% and 41%, respectively). Inclusion of APOE allele counts did not notably change heritability estimates, indicating that there are likely additional genetic variants contributing to cognition. These findings suggest that future genetic studies should focus on the cognitive domains of Working Memory and Visual Learning & Memory.

Macrovascular and microvascular cerebral blood flow in adults at risk for Alzheimer's disease

Capillary hypoperfusion is reported in asymptomatic adults at‐risk for Alzheimers disease (AD), but the extent that can be explained by reduced flow in intracranial arteries is unknown.

Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

BackgroundRandom forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact its ability to identify strong predictors. The Random Forest-Recursive Feature Elimination algorithm (RF-RFE) mitigates this problem in smaller data sets, but this approach has not been tested in high-dimensional omics data sets.ResultsWe integrated 202,919 genotypes and 153,422 methylation sites in 680 individuals, and compared the abilities of RF and RF-RFE to detect simulated causal associations, which included simulated genotype–methylation interactions, between these variables and triglyceride levels. Results show that RF was able to identify strong causal variables with a few highly correlated variables, but it did not detect other causal variables.ConclusionsAlthough RF-RFE decreased the importance of correlated variables, in the presence of many correlated variables, it also decreased the importance of causal variables, making both hard to detect. These findings suggest that RF-RFE may not scale to high-dimensional data.

Prioritization of family member sequencing for the detection of rare variants

BackgroundThe advent of affordable sequencing has enabled researchers to discover many variants contributing to disease, including rare variants. There are methods for determining the most informative individuals for sequencing, but the application of these methods is more complex when working with families. Sets of large families can be beneficial in finding rare variants, but it may be unfeasible to sequence all members of these family sets.MethodsUsing simulated data from the Genetic Analysis Workshop 19, we apply multiple regression to identify cases and controls. To find the best controls for each case, we used kinship coefficients to match within families. Selected cases and controls were analyzed for rare variants, collapsed by gene, associated with hypertension using the family-based rare variant association test (FARVAT).ResultsThe gene with the strongest simulated effect, MAP4, did not meet the Bonferroni corrected significance threshold. However, analysis of cases and controls using our selection method substantially improved the significance of MAP4, despite the reduction in sample size.ConclusionsTaking the additional steps to select the optimal cases and controls from large family data sets can help ensure that only informative individuals are included in analysis and may improve the ability to detect rare variants.

Transmission and decorrelation methods for detecting rare variants using sequencing data from related individuals

BackgroundAdvances in whole genome sequencing have enabled the investigation of rare variants, which could explain some of the missing heritability that genome-wide association studies are unable to detect. Most methods to detect associations with rare variants are developed for unrelated individuals; however, several methods exist that utilize family studies and could have better power to detect such associations.MethodsUsing whole genome sequencing data and simulated phenotypes provided by the organizers of the Genetic Analysis Workshop 19 (GAW19), we compared family-based methods that test for associations between rare and common variants with a quantitative trait. This was done using 2 fairly novel methods: family-based association test for rare variants (FBAT-RV), which is a transmission-based method that utilizes the transmission of genetic information from parent to offspring; and Minimum p value Optimized Nuisance parameter Score Test Extended to Relatives (MONSTER), which is a decorrelation method that instead attempts to adjust for relatedness using a regression-based method. We also considered family-based association test linear combination (FBAT-LC) and FBAT-Min P, which are slightly older methods that do not allow for the weighting of rare or common variants, but contrast some of the limitations of FBAT-RV.ResultsMONSTER had much higher overall power than FBAT-RV and FBAT-Min P. Interestingly, FBAT-LC had similar overall power as MONSTER. MONSTER had the highest power for a gene accounting for a larger percent of the phenotypic variance, whereas MONSTER and FBAT-LC both had the highest power for a gene accounting for moderate variance. FBAT-LC had the highest power for a gene accounting for the least variance.ConclusionsBased on the simulated data from GAW19, MONSTER and FBAT-LC were the most powerful of the methods assessed. However, there are limitations to each of these methods that should be carefully considered when conducting an analysis of rare variants in related individuals. This emphasizes the need for methods that can incorporate the advantages of each of these methods into 1 family-based association test for rare variants.

Metabolites associated with early cognitive changes implicated in Alzheimer's disease

We investigated the metabolomics of early cognitive changes related to Alzheimer’s disease (AD) in order to better understand mechanisms that could contribute to early stages and progression of this disease. This investigation used longitudinal plasma samples from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a cohort of participants who were dementia free at enrollment and enriched with a parental history of AD. Metabolomic profiles were quantified for 2,338 fasting plasma samples among 1,206 participants, each with up to three study visits. Of 1,097 metabolites tested, levels of seven were associated with executive function trajectories, including an amino acid and three fatty acids, but none were associated with delayed recall trajectories. Our time-varying metabolomic results suggest potential mechanisms that could contribute to the earliest signs of cognitive decline. In particular, fatty acids may be associated with cognition in a manner that is more complex than previously suspected.

Longitudinal plasma metabolomics of aging and sex

Understanding how metabolites are longitudinally influenced by age and sex could facilitate the identification of metabolomic profiles and trajectories that indicate disease risk. We investigated the metabolomics of age and sex using longitudinal plasma samples from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a cohort of participants who were dementia free at enrollment. Metabolomic profiles were quantified for 2,316 fasting plasma samples among 1,187 participants, each with up to three study visits. Of 1,097 metabolites tested, 608 (55.4%) were associated with age and 680 (62.0%) with sex after correcting for multiple testing. Approximately twice as many metabolites were associated with age in stratified analyses of women versus men, and 63 metabolite trajectories significantly differed by sex, most notably including sphingolipids, which tended to increase in women and decrease in men with age. Using genome-wide genotyping, we also report the heritabilities of metabolites investigated, which ranged dramatically (0.2-99.2%); however, the median heritability of 36.2% suggests that many metabolites are highly influenced by a complex combination of genomic and environmental influences. These findings offer a more profound description of the aging process and may inform many new hypotheses regarding the role metabolites play in healthy and accelerated aging.