Sixteen-Year Longitudinal Evaluation of Blood-Based DNA Methylation Biomarkers for Early Prediction of Alzheimer's Disease.

BACKGROUND: DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer's disease (AD) prediction. OBJECTIVE: Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers. METHODS: EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10-16 years), and post-AD-onset time-points. RESULTS: EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (p-values: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOEɛ4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for ɛ4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.

APOE ɛ4, but not polygenic Alzheimer's disease risk, is related to longitudinal decrease in hippocampal brain activity in non-demented individuals.

The hippocampus is affected early in Alzheimer's disease (AD) and altered hippocampal functioning influences normal cognitive aging. Here, we used task-based functional MRI to assess if the APOE ɛ4 allele or a polygenic risk score (PRS) for AD was linked to longitudinal changes in memory-related hippocampal activation in normal aging (baseline age 50-95, n = 292; n = 182 at 4 years follow-up, subsequently non-demented for at least 2 years). Mixed-models were used to predict level and change in hippocampal activation by APOE ɛ4 status and PRS based on gene variants previously linked to AD at p ≤ 1, p < 0.05, or p < 5e-8 (excluding APOE). APOE ɛ4 and PRSp<5e-8 significantly predicted AD risk in a larger sample from the same study population (n = 1542), while PRSp≤1 predicted memory decline. APOE ɛ4 was linked to decreased hippocampal activation over time, with the most prominent effect in the posterior hippocampi, while PRS was unrelated to hippocampal activation at all p-thresholds. These results suggests a link for APOE ɛ4, but not for AD genetics in general, on functional changes of the hippocampi in normal aging.

Independent role of Alzheimer's disease genetics and C-reactive protein on cognitive ability in aging.

Apolipoprotein E (APOE) ε4, the strongest genetic risk factor for late onset Alzheimer's disease (LOAD), has been associated with cognitive decline independent from AD pathology, but the role for other LOAD risk genes in normal cognitive aging is less studied. We examined the effect of APOE ε4 and several different polygenic risk scores (PRS) for LOAD on cognitive level and decline in aging, using longitudinal data from the UK Biobank. While PRS-LOAD including all variants (except APOE) predicted cognitive level, APOE ε4 and PRS-LOAD based on 17 non-APOE gene variants with strong association to AD (p < 5e-8) predicted age-related decline in verbal numeric reasoning. The effect on decline were partly driven by 4 variants involved in the immune system. Those variants also predicted serum levels of the inflammatory marker C-reactive protein (CRP), but CRP did not mediate the effect on decline. Those findings suggest genetic variations in immune functions play a role in aspects of cognitive aging that may be independent of LOAD pathology as well as systemic inflammation measured by CRP.

Longitudinal effects of using and discontinuing central nervous system medications on cognitive functioning.

PURPOSE: To investigate the longitudinal effect of using and discontinuing central nervous system (CNS) medications on cognitive performance. METHODS: Using longitudinal cognitive data from population representative adults aged 25-100 years (N = 2188) from four test waves 5 years apart, we investigated both the link between use of CNS medications (opioids, anxiolytics, hypnotics and sedatives) on cognitive task performance (episodic memory, semantic memory, visuospatial ability) across 15 years, and the effect of discontinuing these medications in linear mixed effects models. RESULTS: We found that opioid use was associated with decline in visuospatial ability whereas using anxiolytics, hypnotics and sedatives was not associated with cognitive decline over 15 years. A link between drug discontinuation and cognitive improvement was seen for opioids as well as for anxiolytics, hypnotics and sedatives. CONCLUSIONS: Although our results may be confounded by subjacent conditions, they suggest that long-term use of CNS medications may have domain-specific negative effects on cognitive performance over time, whereas the discontinuation of these medications may partly reverse these effects. These results open up for future studies that address subjacent conditions on cognition to develop a more complete understanding of the cognitive effects of CNS medications.

Candidates for drug repurposing to address the cognitive symptoms in schizophrenia.

In the protein-protein interactome, we have previously identified a significant overlap between schizophrenia risk genes and genes associated with cognitive performance. Here, we further studied this overlap to identify potential candidate drugs for repurposing to treat the cognitive symptoms in schizophrenia. We first defined a cognition-related schizophrenia interactome from network propagation analyses, and identified drugs known to target more than one protein within this network. Thereafter, we used gene expression data to further select drugs that could counteract schizophrenia-associated gene expression perturbations. Additionally, we stratified these analyses by sex to identify sex-specific pharmacological treatment options for the cognitive symptoms in schizophrenia. After excluding drugs contraindicated in schizophrenia, we identified 12 drug repurposing candidates, most of which have anti-inflammatory and neuroprotective effects. Sex-stratified analyses showed that out of these 12 drugs, four were identified in females only, three were identified in males only, and five were identified in both sexes. Based on our bioinformatics analyses of disease genetics, we suggest 12 candidate drugs that warrant further examination for repurposing to treat the cognitive symptoms in schizophrenia, and suggest that these symptoms could be addressed by sex-specific pharmacological treatment options.

Polygenic Risk for Schizophrenia Has Sex-Specific Effects on Brain Activity during Memory Processing in Healthy Individuals.

Genetic risk for schizophrenia has a negative impact on memory and other cognitive abilities in unaffected individuals, and it was recently shown that this effect is specific to males. Using functional MRI, we investigated the effect of a polygenic risk score (PRS) for schizophrenia on brain activation during working memory and episodic memory in 351 unaffected participants (167 males and 184 females, 25-95 years), and specifically tested if any effect of PRS on brain activation is sex-specific. Schizophrenia PRS was significantly associated with decreased brain activation in the left dorsolateral prefrontal cortex (DLPFC) during working-memory manipulation and in the bilateral superior parietal lobule (SPL) during episodic-memory encoding and retrieval. A significant interaction effect between sex and PRS was seen in the bilateral SPL during episodic-memory encoding and retrieval, and sex-stratified analyses showed that the effect of PRS on SPL activation was male-specific. These results confirm previous findings of DLPFC inefficiency in schizophrenia, and highlight the SPL as another important genetic intermediate phenotype of the disease. The observed sex differences suggest that the previously shown male-specific effect of schizophrenia PRS on cognition translates into an additional corresponding effect on brain functioning.

Sex-specific effects of polygenic risk for schizophrenia on lifespan cognitive functioning in healthy individuals.

Polygenic risk for schizophrenia has been associated with lower cognitive ability and age-related cognitive change in healthy individuals. Despite well-established neuropsychological sex differences in schizophrenia patients, genetic studies on sex differences in schizophrenia in relation to cognitive phenotypes are scarce. Here, we investigated whether the effect of a polygenic risk score (PRS) for schizophrenia on childhood, midlife, and late-life cognitive function in healthy individuals is modified by sex, and if PRS is linked to accelerated cognitive decline. Using a longitudinal data set from healthy individuals aged 25-100 years (N = 1459) spanning a 25-year period, we found that PRS was associated with lower cognitive ability (episodic memory, semantic memory, visuospatial ability), but not with accelerated cognitive decline. A significant interaction effect between sex and PRS was seen on cognitive task performance, and sex-stratified analyses showed that the effect of PRS was male-specific. In a sub-sample, we observed a male-specific effect of the PRS on school performance at age 12 (N = 496). Our findings of sex-specific effects of schizophrenia genetics on cognitive functioning across the lifespan indicate that the effects of underlying disease genetics on cognitive functioning is dependent on biological processes that differ between the sexes.

Short leukocyte telomeres predict 25-year Alzheimer's disease incidence in non-APOE ε4-carriers.

BACKGROUND: Leukocyte telomere length (LTL) has been shown to predict Alzheimer's disease (AD), albeit inconsistently. Failing to account for the competing risks between AD, other dementia types, and mortality, can be an explanation for the inconsistent findings in previous time-to-event analyses. Furthermore, previous studies indicate that the association between LTL and AD is non-linear and may differ depending on apolipoprotein E (APOE) ε4 allele carriage, the strongest genetic AD predictor. METHODS: We analyzed whether baseline LTL in interaction with APOE ε4 predicts AD, by following 1306 initially non-demented subjects for 25 years. Gender- and age-residualized LTL (rLTL) was categorized into tertiles of short, medium, and long rLTLs. Two complementary time-to-event models that account for competing risks were used; the Fine-Gray model to estimate the association between the rLTL tertiles and the cumulative incidence of AD, and the cause-specific hazard model to assess whether the cause-specific risk of AD differed between the rLTL groups. Vascular dementia and death were considered competing risk events. Models were adjusted for baseline lifestyle-related risk factors, gender, age, and non-proportional hazards. RESULTS: After follow-up, 149 were diagnosed with AD, 96 were diagnosed with vascular dementia, 465 died without dementia, and 596 remained healthy. Baseline rLTL and other covariates were assessed on average 8 years before AD onset (range 1-24). APOE ε4-carriers had significantly increased incidence of AD, as well as increased cause-specific AD risk. A significant rLTL-APOE interaction indicated that short rLTL at baseline was significantly associated with an increased incidence of AD among non-APOE ε4-carriers (subdistribution hazard ratio = 3.24, CI 1.404-7.462, P = 0.005), as well as borderline associated with increased cause-specific risk of AD (cause-specific hazard ratio = 1.67, CI 0.947-2.964, P = 0.07). Among APOE ε4-carriers, short or long rLTLs were not significantly associated with AD incidence, nor with the cause-specific risk of AD. CONCLUSIONS: Our findings from two complementary competing risk time-to-event models indicate that short rLTL may be a valuable predictor of the AD incidence in non-APOE ε4-carriers, on average 8 years before AD onset. More generally, the findings highlight the importance of accounting for competing risks, as well as the APOE status of participants in AD biomarker research.

Short Leukocyte Telomeres, But Not Telomere Attrition Rates, Predict Memory Decline in the 20-Year Longitudinal Betula Study.

Leukocyte telomere length (LTL) is a proposed biomarker for aging-related disorders, including cognitive decline and dementia. Long-term longitudinal studies measuring intra-individual changes in both LTL and cognitive outcomes are scarce, precluding strong conclusions about a potential aging-related relationship between LTL shortening and cognitive decline. This study investigated associations between baseline levels and longitudinal changes in LTL and memory performance across an up to 20-year follow-up in 880 dementia-free participants from a population-based study (mean baseline age: 56.8 years, range: 40-80; 52% female). Shorter baseline LTL significantly predicted subsequent memory decline (r = .34, 95% confidence interval: 0.06, 0.82), controlling for age, sex, and other relevant covariates. No significant associations were however observed between intra-individual changes in LTL and memory, neither concurrently nor with a 5-year time-lag between LTL shortening and memory decline. These results support the notion of short LTL as a predictive factor for aging-related memory decline, but suggest that LTL dynamics in adulthood and older age may be less informative of cognitive outcomes in aging. Furthermore, the results highlight the importance of long-term longitudinal evaluation of outcomes in biomarker research.

Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies.

Individual differences in cognitive performance increase with advancing age, reflecting marked cognitive changes in some individuals along with little or no change in others. Genetic and lifestyle factors are assumed to influence cognitive performance in ageing by affecting the magnitude and extent of age-related brain changes (i.e., brain maintenance or atrophy), as well as the ability to recruit compensatory processes. The purpose of this review is to present findings from the Betula study and other longitudinal studies, with a focus on clarifying the role of key biological and environmental factors assumed to underlie individual differences in brain and cognitive ageing. We discuss the vital importance of sampling, analytic methods, consideration of non-ignorable dropout, and related issues for valid conclusions on factors that influence healthy neurocognitive ageing.

Effects of polygenic risk for Alzheimer's disease on rate of cognitive decline in normal aging.

Most people's cognitive abilities decline with age, with significant and partly genetically driven, individual differences in rate of change. Although APOE ɛ4 and genetic scores for late-onset Alzheimer's disease (LOAD) have been related to cognitive decline during preclinical stages of dementia, there is limited knowledge concerning genetic factors implied in normal cognitive aging. In the present study, we examined three potential genetic predictors of age-related cognitive decline as follows: (1) the APOE ɛ4 allele, (2) a polygenic score for general cognitive ability (PGS-cog), and (3) a polygenic risk score for late-onset AD (PRS-LOAD). We examined up to six time points of cognitive measurements in the longitudinal population-based Betula study, covering a 25-year follow-up period. Only participants that remained alive and non-demented until the most recent dementia screening (1-3 years after the last test occasion) were included (n = 1087). Individual differences in rate of cognitive change (composite score) were predicted by the PRS-LOAD and APOE ɛ4, but not by PGS-cog. To control for the possibility that the results reflected a preclinical state of Alzheimer's disease in some participants, we re-ran the analyses excluding cognitive data from the last test occasion to model cognitive change up-until a minimum of 6 years before potential onset of clinical Alzheimers. Strikingly, the association of PRS-LOAD, but not APOE ɛ4, with cognitive change remained. The results indicate that PRS-LOAD predicts individual difference in rate of cognitive decline in normal aging, but it remains to be determined to what extent this reflects preclinical Alzheimer's disease brain pathophysiology and subsequent risk to develop the disease.

Interactome overlap between schizophrenia and cognition.

Cognitive impairments constitute a core feature of schizophrenia, and a genetic overlap between schizophrenia and cognitive functioning in healthy individuals has been identified. However, due to the high polygenicity and complex genetic architecture of both traits, overlapping biological pathways have not yet been identified between schizophrenia and normal cognitive ability. Network medicine offers a framework to study underlying biological pathways through protein-protein interactions among risk genes. Here, established network-based methods were used to characterize the biological relatedness of schizophrenia and cognition by examining the genetic link between schizophrenia risk genes and genes associated with cognitive performance in healthy individuals, through the protein interactome. First, network separation showed a profound interactome overlap between schizophrenia risk genes and genes associated with cognitive performance (SAB = -0.22, z-score = -6.80, p = 5.38e-12). To characterize this overlap, network propagation was thereafter used to identify schizophrenia risk genes that are close to cognition-associated genes in the interactome network space (n = 140, of which 54 were part of the direct genetic overlap). Schizophrenia risk genes close to cognition were enriched for pathways including long-term potentiation and Alzheimer's disease, and included genes with a role in neurotransmitter systems important for cognitive functioning, such as glutamate and dopamine. These results pinpoint a subset of schizophrenia risk genes that are of particular interest for further examination in schizophrenia patient groups, of which some are druggable genes with potential as candidate targets for cognitive enhancing drugs.

Identification of genetic heterogeneity of Alzheimer's disease across age.

The risk of APOE for Alzheimer's disease (AD) is modified by age. Beyond APOE, the polygenic architecture may also be heterogeneous across age. We aim to investigate age-related genetic heterogeneity of AD and identify genomic loci with differential effects across age. Stratified gene-based genome-wide association studies and polygenic variation analyses were performed in the younger (60-79 years, N = 14,895) and older (≥80 years, N = 6559) age-at-onset groups using Alzheimer's Disease Genetics Consortium data. We showed a moderate genetic correlation (rg = 0.64) between the two age groups, supporting genetic heterogeneity. Heritability explained by variants on chromosome 19 (harboring APOE) was significantly larger in younger than in older onset group (p < 0.05). APOE region, BIN1, OR2S2, MS4A4E, and PICALM were identified at the gene-based genome-wide significance (p < 2.73 × 10-6) with larger effects at younger age (except MS4A4E). For the novel gene OR2S2, we further performed leave-one-out analyses, which showed consistent effects across subsamples. Our results suggest using genetically more homogeneous individuals may help detect additional susceptible loci.

Polygenic hazard score, amyloid deposition and Alzheimer's neurodegeneration.

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-ß protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.

GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies.

Motivation: Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using non-local priors in an iterative variable selection framework. Results: We develop a variable selection method, named, iterative non-local prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors. The hallmark of our method is the introduction of 'structured screen-and-select' strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations and concatenates variable selection within that hierarchy. Extensive simulation studies with single nucleotide polymorphisms having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype. Availability and implementation: An R-package for implementing the GWASinlps method is available at https://cran.r-project.org/web/packages/GWASinlps/index.html. Supplementary information: Supplementary data are available at Bioinformatics online.

Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease.

Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond APOE. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (p = 1.07e-5), and PHS was significantly more predictive than APOE alone (p = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model p = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both APOE and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.

Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia.

OBJECTIVE: Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel "druggable" pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product. METHOD: The protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328). RESULTS: Schizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms. CONCLUSIONS: The network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.

Polygenic hazard score: an enrichment marker for Alzheimer's associated amyloid and tau deposition.

There is an urgent need for identifying nondemented individuals at the highest risk of progressing to Alzheimer's disease (AD) dementia. Here, we evaluated whether a recently validated polygenic hazard score (PHS) can be integrated with known in vivo cerebrospinal fluid (CSF) or positron emission tomography (PET) biomarkers of amyloid, and CSF tau pathology to prospectively predict cognitive and clinical decline in 347 cognitive normal (CN; baseline age range = 59.7-90.1, 98.85% white) and 599 mild cognitively impaired (MCI; baseline age range = 54.4-91.4, 98.83% white) individuals from the Alzheimer's Disease Neuroimaging Initiative 1, GO, and 2. We further investigated the association of PHS with post-mortem amyloid load and neurofibrillary tangles in the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 485, age at death range = 71.3-108.3). In CN and MCI individuals, we found that amyloid and total tau positivity systematically varies as a function of PHS. For individuals in greater than the 50th percentile PHS, the positive predictive value for amyloid approached 100%; for individuals in less than the 25th percentile PHS, the negative predictive value for total tau approached 85%. High PHS individuals with amyloid and tau pathology showed the steepest longitudinal cognitive and clinical decline, even among APOE ε4 noncarriers. Among the CN subgroup, we similarly found that PHS was strongly associated with amyloid positivity and the combination of PHS and biomarker status significantly predicted longitudinal clinical progression. In the ROSMAP cohort, higher PHS was associated with higher post-mortem amyloid load and neurofibrillary tangles, even in APOE ε4 noncarriers. Together, our results show that even after accounting for APOE ε4 effects, PHS may be useful in MCI and preclinical AD therapeutic trials to enrich for biomarker-positive individuals at highest risk for short-term clinical progression.

Leveraging genome characteristics to improve gene discovery for putamen subcortical brain structure.

Discovering genetic variants associated with human brain structures is an on-going effort. The ENIGMA consortium conducted genome-wide association studies (GWAS) with standard multi-study analytical methodology and identified several significant single nucleotide polymorphisms (SNPs). Here we employ a novel analytical approach that incorporates functional genome annotations (e.g., exon or 5'UTR), total linkage disequilibrium (LD) scores and heterozygosity to construct enrichment scores for improved identification of relevant SNPs. The method provides increased power to detect associated SNPs by estimating stratum-specific false discovery rate (FDR), where strata are classified according to enrichment scores. Applying this approach to the GWAS summary statistics of putamen volume in the ENIGMA cohort, a total of 15 independent significant SNPs were identified (conditional FDR < 0.05). In contrast, 4 SNPs were found based on standard GWAS analysis (P < 5 × 10-8). These 11 novel loci include GATAD2B, ASCC3, DSCAML1, and HELZ, which are previously implicated in various neural related phenotypes. The current findings demonstrate the boost in power with the annotation-informed FDR method, and provide insight into the genetic architecture of the putamen.