scholarly journals Sleep Polygenic Risk Score Is Associated with Cognitive Changes over Time

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 63
Author(s):  
Angeliki Tsapanou ◽  
Niki Mourtzi ◽  
Sokratis Charisis ◽  
Alex Hatzimanolis ◽  
Eva Ntanasi ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Spatial Ability ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Cognitive Changes ◽  
Cognitive Domains ◽  
Polygenic Risk ◽  
Sleep Medication ◽  
Over Time

Sleep problems have been associated with cognition, both cross-sectionally and longitudinally. Specific genes have been also associated with both sleep regulation and cognition. In a large group of older non-demented adults, we aimed to (a) validate the association between Sleep Polygenic Risk Score (Sleep PRS) and self-reported sleep duration, and (b) examine the association between Sleep PRS and cognitive changes in a three-year follow-up. Participants were drawn from the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD). A structured, in-person interview, consisting of a medical history report and physical examination, was conducted for each participant during each of the visits (baseline and first follow-up). In total, 1376 participants were included, having all demographic, genetic, and cognitive data, out of which, 688 had at least one follow-up visit. In addition, an extensive neuropsychological assessment examining five cognitive domains (memory, visuo-spatial ability, attention/speed of processing, executive function, and language) was administered. A PRS for sleep duration was created based on previously published, genome-wide association study meta-analysis results. In order to assess the relationship between the Sleep PRS and the rate of cognitive change, we used generalized estimating equations analyses. Age, sex, education, ApolipoproteinE-ε4 genotype status, and specific principal components were used as covariates. On a further analysis, sleep medication was used as a further covariate. Results validated the association between Sleep PRS and self-reported sleep duration (B = 1.173, E-6, p = 0.001). Further, in the longitudinal analyses, significant associations were indicated between increased Sleep PRS and decreased visuo-spatial ability trajectories, in both the unadjusted (B = −1305.220, p = 0.018) and the adjusted for the covariates model (B = −1273.59, p = 0.031). Similarly, after adding sleep medication as a covariate (B = −1372.46, p = 0.019), none of the associations between Sleep PRS and the remaining cognitive domains were significant. PRS indicating longer sleep duration was associated with differential rates of cognitive decline over time in a group of non-demented older adults. Common genetic variants may influence the association between sleep duration and healthy aging/cognitive health.

scholarly journals A polygenic risk score analysis of psychosis endophenotypes across brain functional, structural, and cognitive domains

2017 ◽  
Vol 177 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Siri Ranlund ◽  
Stella Calafato ◽  
Johan H. Thygesen ◽  
Kuang Lin ◽  
Wiepke Cahn ◽  
...  
Keyword(s):  
Risk Score ◽  
Polygenic Risk Score ◽  
Cognitive Domains ◽  
Polygenic Risk ◽  
Score Analysis

scholarly journals A Polygenic Risk Score for Coronary Artery Disease Improves the Prediction of Early-Onset Myocardial Infarction and Mortality in Men

2021 ◽  
Author(s):  
Hasanga D. Manikpurage ◽  
Aida Eslami ◽  
Nicolas Perrot ◽  
Zhonglin Li ◽  
Christian Couture ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Score ◽  
Predictive Accuracy ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Artery Disease ◽  
Genetically Determined

ABSTRACTBackgroundSeveral risk factors for coronary artery disease (CAD) have been described, some of which are genetically determined. The use of a polygenic risk score (PRS) could improve CAD risk assessment, but predictive accuracy according to age and sex is not well established.MethodsA PRSCAD including the weighted effects of >1.14 million SNPs associated with CAD was calculated in UK Biobank (n=408,422), using LDPred. Cox regressions were performed, stratified by age quartiles and sex, for incident MI and mortality, with a median follow-up of 11.0 years. Improvement in risk prediction of MI was assessed by comparing PRSCAD to the pooled cohort equation with categorical net reclassification index using a 2% threshold (NRI0.02) and continuous NRI (NRI>0).ResultsFrom 7,746 incident MI cases and 393,725 controls, hazard ratio (HR) for MI reached 1.53 (95% CI [1.49-1.56], p=2.69e-296) per standard deviation (SD) increase of PRSCAD. PRSCAD was significantly associated with MI in both sexes, with a stronger association in men (interaction p=0.002), particularly in those aged between 40-51 years (HR=2.00, 95% CI [1.86-2.16], p=1.93e-72). This group showed the highest reclassification improvement, mainly driven by the up-classification of cases (NRI0.02=0.199, 95% CI [0.157-0.248] and NRI>0=0.602, 95% CI [0.525-0.683]). From 23,982 deaths, HR for mortality was 1.08 (95% CI [1.06-1.09], p=5.46e-30) per SD increase of PRSCAD, with a stronger association in men (interaction p=1.60e-6).ConclusionOur PRSCAD predicts MI incidence and all-cause mortality, especially in men aged between 40-51 years. PRS could optimize the identification and management of individuals at risk for CAD.

scholarly journals S24. PATHWAYS LINKING ENVIRONMENTAL RISKS, POLYGENIC RISK SCORE TO SCHIZOPHRENIA AND PSYCHOTIC EXPERIENCES IN A BRAZILIAN COHORT OF CHILDREN AND ADOLESCENTS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S39-S40
Author(s):  
Lais Fonseca ◽  
Gabrielle de Oliveira S V Navarro ◽  
Marcos Leite Santoro ◽  
Pedro M Pan ◽  
Rodrigo Bressan ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Risk Score ◽  
Moderated Mediation ◽  
Polygenic Risk Score ◽  
Psychotic Experiences ◽  
Mediation Model ◽  
Polygenic Risk ◽  
Moderated Mediation Model ◽  
P Factor

Abstract Background Polygenic risk score to schizophrenia (PRS-SZ) provides a liability measure summarizing each genetic risk variant and the polyenviromic risk score (PERS) proposes the same regarding exposure factors to psychosis, yet few studies addressed how both scopes interplay, especially in early developmental stages. Psychotic experiences (PE) rest on the lower range of psychosis spectrum, representing an important asset to study psychotic disorders, ie. schizophrenia. However, investigators failed to find significant associations between PRS-SZ and PE in children. We hypothesize that unspecific psychopathology – also previously linked to PE – can mediate the effects of higher risk load for psychosis during neurodevelopment. Thus, our aim is to test a moderated mediation model in which PERS and general psychopathology in youths can lead to PE, prospectively, through SZ genetic liability. Methods We analyzed data from the Brazilian High-Risk Cohort for Psychiatric Disorders, a youth community sample with 2 time-points: baseline (w0) and 3year follow-up (w1), from São Paulo and Porto Alegre, both urban centers. PRS-SZ was calculated using summary statistics from the PGC and corrected for the 10 principal components of the GWAS. PE were assessed at w0 and w1 with the Community Assessment Psychotic Experiences – CAPE and trained psychologists rated the reliability of students’ answers. The Development and Well-Being Assessment – DAWBA, a structured interview with a transdiagnostic approach, was used to extract a general factor for psychopathology (P-factor) on w0. Latent variables for PE and P-factor were generated through confirmatory factor analysis yielding good model fits. We calculated PERS on w0, as validated, with birth season, urbanicity, cannabis use, paternal age, obstetric/perinatal complications and physical/sexual abuse, neglect or parental loss/separation. Last, we built a moderated mediation diagram based on model 15 of Haye’s PROCESS builder on SPSS: (X) PERS > (M) P-factor > (Y) PE w1, with (V) PRS-SZ as a moderator for PERS > PE and P-factor > PE. Age, sex, site and PE w0 were covariates. Results 2,511 students (6–14 y/o, mean=10.2 ± 1.9, 53% male) completed the w0 assessment and 2,010 the follow-up (mean=13.5 y/o ± 1.9). In our moderated mediation model, P-factor emerged as a full mediator between PERS and PE w1 (B=.324, BootLL–UL CI=.138 to .553). We found PRS-SZ provided a significant moderation effect on the P-factor > PE relation (M*V=.053, R2-chng=.003, p=.037), with the moderator effects of the focal predictor rising considerably according to values of PRS-SZ: p16 (B=.047, p=.192), p50 (B=.099, p=.000) and p84 (B=.153, p=.000). PRS-SZ did not moderate PERS > PE separately (X*V=.016, R2-chng=.001, p=.974). However, conditional indirect coefficients for the complete model were also significant for higher PRS-SZ levels: p16 (B=.143, BootLL–UL CI=-.072 to .389), p50 (B=.304, BootLL–UL CI=.126 to .529) and p84 (B=.470, BootLL–UL CI=.197 to .814). Discussion Our findings suggest environmental risk factors and intermediate phenotypes – namely unspecific non-psychotic psychopathology – can play crucial and intertwined roles in children and adolescents with higher genetic liability to SZ. Moreover, the moderation effects of PRS-SZ imply the existence of thresholds for those relations. The non-clinical nature and age of our sample could explain the low effect sizes. Next steps would include additional phenotypic tracks, such as cognition and social functionality – both previously connected to PRS-SZ as well. We hope our results can help disentangle the genetic and environmental trajectories bonding SZ proneness and PE, and possibly contribute to risk assessment in youths, especially among vulnerable populations.

scholarly journals Using an Alzheimer Disease Polygenic Risk Score to Predict Memory Decline in Black and White Americans Over 14 Years of Follow-up

2016 ◽  
Vol 30 (3) ◽  
pp. 195-202 ◽  
Author(s):  
Jessica R. Marden ◽  
Elizabeth R. Mayeda ◽  
Stefan Walter ◽  
Alexandre Vivot ◽  
Eric J. Tchetgen Tchetgen ◽  
...  
Keyword(s):  
Alzheimer Disease ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
White Americans ◽  
Memory Decline ◽  
Polygenic Risk ◽  
Black And White

scholarly journals Association between a Polygenic Risk Score for Multiple Myeloma Risk and Overall Survival

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4366-4366
Author(s):  
Alyssa I. Clay-Gilmour ◽  
Michelle Hildebrandt ◽  
Yan Asmann ◽  
Elizabeth E. Brown ◽  
Jonathan N Hofmann ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Overall Survival ◽  
Quality Control ◽  
Risk Score ◽  
Genetic Variants ◽  
Research Funding ◽  
Stage I ◽  
Polygenic Risk Score ◽  
Polygenic Risk

Background Genome-wide association studies (GWAS) conducted in populations of European ancestry (EA) have identified and confirmed 23 germline susceptibility loci for multiple myeloma (MM). The effect sizes of single nucleotide polymorphisms (SNPs) at these loci are small, therefore combining them into a single summary measure, known as a polygenic risk score (PRS), may provide a more meaningful risk factor. We have previously shown a PRS comprised of the 23 SNPs for MM contributes to increased risk of MM, with a 2.7-fold increase for highest vs. lowest PRS quintiles. Whether the MM-PRS is also associated with overall survival (OS) in MM cases has not been evaluated. We examined the association between MM-PRS and OS in two EA studies. Methods The first study consisted of 2,179 EA MM cases from ten studies included in the Multiple Myeloma Working Group within the International Lymphoma Consortium (InterLymph). Cases were diagnosed between 1970 and 2015 and genotyped using multiple platforms (Oncoarray, Affymetrix, Human660W-quad Beadchip, and Illumina arrays); 885 cases also had stage [based on International Staging System (ISS)] available. Each of the GWAS was subjected to rigorous standard quality control independently (prior to imputation via the Michigan imputation server based on the Haplotype Reference Consortium (HRC). The second study consisted of 515 newly diagnosed EA MM cases from CoMMpass (Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile), diagnosed from 2011-2013, who had whole genome sequencing (WGS) performed on germline DNA. The WGS data was used to call common germline genetic variants through the Mayo Clinic bioinformatics pipeline. Briefly, genetic variants were detected with GenomeGPS, aligned to the hg19 reference genome, called using the GATK (V3.6) Haplotype Caller, and merged for multiple-sample joint calling. To reduce the false positive variants, variant quality score recalibration (VQSR) was applied for both SNPs and INDELs. After quality control, 458 EA samples remained. Follow-up was available for both studies and consisted of time from MM diagnosis date until death or date of last known follow-up. The PRS was constructed from the 23 MM SNPs using the published per allele odds ratio associated with MM risk. The published log odds ratios for each SNP were multiplied by the number of risk alleles (0, 1, 2) for the corresponding SNP, and summed, resulting in a unique score per person. Kaplan-Meier curves and Cox proportional hazard models were used to assess the association between PRS with MM OS considering two models: 1) adjusted for age, sex, study and 2) additional adjustment by stage (ISS). Hazard ratios (OR) and 95% confidence intervals (CI) were estimated. The PRS was evaluated both as a continuous variable, per standard deviation (SD), and as a categorical variable (quintiles). Results MM cases (N=2,179) in the InterLymph study were 59% male and 41% female and the median age was 61.0 years (26-90 years). Median follow-up time was 57.2 months (1.0-509.0 months) with 868 reported deaths. MM cases with stage information available consisted of 20% stage I (n=178), 53% stage II (n=466), and 27% stage III (n=241). No association was observed between PRS and OS in MM patients regardless of adjustment for stage (continuous PRS (HR: 1.03, 95% CI: 0.83-1.28, P=0.80) or by quintile PRS (p>0.05)) (Table). The CoMMpass EA MM cases (n=458) had similar distributions for sex (61% male and 39% females) but were slightly older 65 years (27-93 years) and had shorter follow-up time (median=39.75 months (0.13-77.2)) with 117 deaths. Stage was available for 96% of CoMMpass cases including 36% stage I (n=159), 33% stage II (n=146), and 31% stage III (n=134). We also observed no association of PRS and OS in the CoMMpass study (HR=1.02, 95% CI: 0.72 -1.46, P= 0.89), adjusted for age, sex, and stage (Table). Discussion A PRS score for MM risk is not associated with OS for MM cases in two EA populations. Given that prior studies have shown association of genetic variation with MM survival, efforts to identify additional loci associated with OS or MM specific survival are warranted. Future studies should also consider germline variants impact on molecular subtypes, specific therapies, and outcomes. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

A Methodological Perspective on the Longitudinal Cognitive Change after Stroke

2017 ◽  
Vol 44 (5-6) ◽  
pp. 311-319 ◽  
Author(s):  
Jae-Sung Lim ◽  
Maengseok Noh ◽  
Beom Joon Kim ◽  
Moon-Ku Han ◽  
SangYun Kim ◽  
...  
Keyword(s):  
Cognitive Change ◽  
Multiple Outcomes ◽  
Cognitive Changes ◽  
Cognitive Domains ◽  
Domain Specific ◽  
Cognitive Levels ◽  
Methodological Considerations ◽  
Changes Over Time ◽  
Over Time

Background/Aims: Most studies of poststroke cognitive impairment (PSCI) have analyzed cognitive levels at specific time points rather than their changes over time. Furthermore, they seldom consider correlations between cognitive domains. We aimed to investigate the effects of these methodological considerations on determining significant PSCI predictors in a longitudinal stroke cohort. Methods: In patients who underwent neuropsychological tests at least twice after stroke, we adopted a multilevel hierarchical mixed-effects model with domain-specific cognitive changes and a multivariate model for multiple outcomes to reflect their correlations. Results: We enrolled 375 patients (median follow-up of 34.1 months). Known predictors of PSCI were generally associated with cognitive levels; however, most of the statistical significances disappeared when cognitive changes were set as outcomes, except age for memory, prior stroke and baseline cognition for executive/attention domain, and baseline cognition for visuospatial function. The multivariate analysis which considered multiple outcomes simultaneously further altered these associations. Conclusions: This study shows that defining outcomes as changes over time and reflecting correlations between outcomes may affect the identification of predictors of PSCI.

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