12 research outputs found
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Common topics discerned in ethics in epidemiology and public health syllabi: in-depth review
This commentary reviews ethics in epidemiology and public health (PH) syllabi collected in 2011 and 2018. The syllabi repository was an American College of Epidemiology (ACE) Ethics Committee project to support institutions and faculty introducing, furthering or improving ethics in epidemiology and public health courses. Of 83 syllabi from 52 accredited public health schools and programs, 80 were reviewed to identify the most common ethics topics. The extracted information was categorized into eight main groups: (1) ethical/moral foundations and theories; (2) case studies in epidemiology/PH; (3) ethical issues in PH practice; (4) ethical issues in general epidemiologic/PH research; (5) ethical issues in specific research areas; (6) ethical issues in information technology; (7) ethical issues in other emerging topics in epidemiology/PH; and (8) others. The frequency of topics in each category was computed, and common topics were presented. Ethical issues absent from the syllabi were inferred. This commentary is intended to promote a dialog among those desiring to elevate epidemiology and public health ethics to an educational level commensurate with its importance
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African locus reduces the effect of ApoE ε4 allele in Alzheimer's disease.
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African‐ancestry based polygenic risk scores improve Alzheimer disease risk prediction in individuals of African Ancestry
Background
Polygenic risk scores (PRS) may be a useful approach to predict the risk of the complex disease and to be an important clinical tool for early intervention. PRS studies in Alzheimer Disease (AD) have focused on individuals of European Ancestry resulting in a >75% prediction accuracy. PRS generated from genome wide data in one population often provides reduced predictive accuracy in other populations. This is particularly problematic for underserved groups. In this study, we assessed and compared the PRS prediction accuracy of AD in individuals of African Ancestry (AA) using both AA and non‐Hispanic White (NHW) Genome Wide Association (GWAS) studies.
Method
As part of the Research in African American Alzheimer Disease Initiative (REAAADI) and ADGC, two TOPMED imputed AA datasets were generated (REAAADI:AD=234, cognitively unimpaired (CU)=676 and ADC9: AD cases=109, CU=224). We assessed the PRS using the effect sizes from summary statistics from the NHW (Kunkle et al. 2019) and the AA (Kunkle et al. 2021) studies. To model the effect of APOE we excluded APOE region in PRS constructing and included APOE alleles as separate terms in the prediction model. First, we generated PRS scores on the REAAADI dataset, and validated our model in ADC9 dataset. To assess the PRS performance, we employed the logistic regression modeling (covariates‐only (age, sex, and PC1:3),PRS‐only, and full (PRS+APOE+covariates) model) to construct receiver operator (ROC) curves.
Result
European ancestry‐derived PRS has the poor prediction power (AUC=0.53) in the REAAADI dataset whereas the AA‐derived PRS predicts better (AUC= 0.87). Further validation of the AA PRS in ADC9 dataset using covariates‐only, PRS‐only and full modelsvalidated that inclusion of African ancestry derived PRS significantly improves the accuracy of AD prediction in AA individuals (AUCcovariates‐only=0.59; AUCPRS‐only=0.74 and AUCfull=0.81).
Conclusion
Our results showed that AA‐derived PRS significantly improves AD risk prediction in AA individuals over European ancestry‐derived PRS. Our findings demonstrate the importance of increasing the diversity in genetic studies to improve precision medicine approaches. Moreover, the development of more accurate PRS models that can detect the risk of AD in all in all groups paves the way for more accurate prevention, early detection, and intervention of AD
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Admixture mapping identifies novel regions influencing Alzheimer disease in African Americans
Background
African Americans (AA) are substantially underrepresented in Alzheimer's disease (AD) genetic studies, yet their admixed genetic ancestry (African and European) provides a unique opportunity to identify novel genetic factors associated with AD related to genetic ancestry. Admixture mapping (AM) provides a more powerful approach than SNP‐based genome‐wide association studies (GWAS) in admixed populations in part due to the lower multiple testing burden. In this study we used AM to identify regions associated with AD in AA individuals.
Methods
Our analyses included 10,271 individuals from 17 AD Sequencing Project and AD Genetics Consortium cohorts. We estimated global ancestry (GA) using the GENESIS software. To infer local ancestry (LA), the target AA dataset was combined with appropriate reference‐population samples from HGDP reference panel, and LA was estimated using SHAPEIT followed by RFMix. Then, we performed AM using the GENESIS software separately on each cohort. We meta‐analyzed the AM results with the random effect approach (RE2). We calculated the significance threshold using STEAM software. Finally, we performed logistic regression of genotype on affection status for variants across the prioritized regions from AM for fine‐mapping. The regression model included LA and genotype as main effects and term for their interaction, along with GA, sex and age as covariates, and used permutation‐based testing approach for multiple test correction (N=10,000).
Results
AM identified two genome‐wide significant loci on chromosomes 17p13.2 (pv=2.2 x10‐5) and 18q21.33 (pv=1.22x10‐5). 17p13.2 region was identified as a genome‐wide significant in two previous studies in non‐Hispanic White population. To fine map this region we conducted ancestry‐aware regression analysis. LA x genotype interaction model found the MINK1 gene (rs72835013) on the 17p13.2 region significantly associated with AD (pv = 1x10‐4).
Conclusions
Our results confirmed an AD associated region on the chromosome 17p13.2 and showed that the 17p13.2 region increases AD risk in AA individuals with the European LA. This region includes genes previously indicated in AD such as SCIMP through association studies and the MINK1 and SLC25A11 genes through brain expression studies
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African Locus Reduces the Effect of ApoE ɛ4 Allele in Alzheimer’s Disease (S15.003)
Abstract onl
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A Novel Protective locus significantly reduces the ApoEε4 risk for Alzheimer’s Disease in individuals of African Ancestry
Background
African ancestry populations have a lower risk for developing Alzheimer disease (AD) from ApoEε4 compared to other populations. Understanding this mechanism of protection could lead new therapeutic insights for AD. Our goal is to identify areas of the genome that interact with ApoEε4 in African ancestry that result in the lowered risk for developing AD in this population.
Methods
We performed association analyses using a logistic regression model with ApoEε4 allele as an interaction term and adjusted for genome‐wide ancestry, age, and sex. Discovery analysis included imputed SNP data from 1,850 African American (AA) individuals with AD and 4,331 AA controls. We performed replication analysis on whole‐genome sequenced (WGS) data from 1) 63 Ibadan (Nigerian) AD individuals and 648 Ibadan controls; 2) WGS 273 Puerto Rican (PR) AD individuals and 275 PR controls; and 3) SNPs imputed from 8,463 non‐Hispanic White (NHW) AD individuals and 11,365 NHW controls.
Results
We identified a significant interaction with the ApoE ε4 allele and the SNP rs10423769_A allele, that reduces the odds ratio for AD risk from 7.2 for ApoEε4/ε4 without the A allele to 2.1 for allele ApoEε4/ε4 carriers with at least one A allele. rs10423769 (frequency = 0.11 in AA, NHW= 0.003) is located approximately 2 megabases distal to ApoE, in a large cluster of pregnancy specific beta‐1 glycoproteins on chromosome 19 and lies within a long noncoding RNA, ENSG00000282943. rs10423769 is reported to be a splicing QTL (sQTL) for TMEM145, whose highest brain expression is in the cerebellum. This interaction analysis was identified in the discovery AA dataset (β=‐0.54, SE=0.12,p‐value=7.50x10‐6), and this finding was replicated in both the Ibadan (β = ‐1.32,SE = 0.52,p‐value = 1.15x10‐2) and PR (β=‐1.27,SE=0.64,p‐value=4.91x10‐2) datasets while it trended but was not significant in the NHW dataset.
Conclusion
This study identified a new African‐ancestry specific locus that reduces the risk effect of ApoEε4 for developing AD by approximately 75%. The genes lying near this protective locus suggest potential new protective mechanisms for AD development
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Multi‐Ancestry Genome‐wide Association Analysis of Late‐Onset Alzheimer’s Disease (LOAD) in 60,941 Individuals Identifies a Novel Cross‐Ancestry Association in LRRC4C
Background
Increasing diversity in genomic studies is critical for defining the genetic architecture of LOAD by improving power to identify variants more prevalent in or specific to a given ancestry. In this study, we constructed and analyzed a multi‐ancestry collection of GWAS datasets in the Alzheimer’s Disease Genetics Consortium (ADGC) to identify novel LOAD susceptibility loci and characterize shared and unique features of LOAD genomic risk profiles between ancestry groups.
Method
The ADGC multi‐ancestry dataset collection includes GWAS genotype and phenotype data on 38,774 non‐Hispanic White (NHW), 7,454 African American (AA), 11,436 Hispanic (HI), and 3,277 East Asian (EAS) subjects, all imputed to the NHLBI TOPMed v5 haplotype reference panel. We performed a two‐stage analysis: (1) single‐variant association analyses using score‐based logistic regression for case‐control and cohort studies and generalized linear mix‐model for family‐based datasets with covariate adjustment for onset/exam age, sex, principal components for population substructure, and APOE ε2/ε3/ε4 genotype, followed by within‐ancestry fixed‐effects meta‐analysis using METAL; and (2) cross‐ancestry meta‐analysis of within‐ancestry summary statistics using the random‐effects model (RE2) in METASOFT.
Result
In addition to APOE region associations, we identified five loci with cross‐ancestry genome‐wide significant associations (P≤5×10−8) including chromosomes 2q14 (BIN1; P = 2.4×10−19), 7q22 (NYAP1; P = 4.9×10−8), 11p2 (LRRC4C; P = 4.6×10−8), 11q12 (MS4A6A; P = 2.6×10−8), and 11q14 (PICALM; P = 2.3×10−10). 21 loci reached suggestive significance (P<10−5), including 10 associations driven by AA ancestry in or near ABCA7, APP, TAF1B, and CEP44); five by NHW (CR1, CD2AP, SORL1, ECHDC3, and ABCA1); four by HI (TREM2, IQCK, DIRC3, and FAR1); and one by EAS (VPS41), some with highly heterogeneous cross‐ancestry associations. Follow‐up analyses including cross‐ancestry fine‐mapping, gene‐based analyses, eQTL (expression) analyses, and functional analyses are in progress.
Conclusion
Cross‐ancestry GWAS meta‐analyses identified a novel LOAD susceptibility locus, LRRC4C, as well as a number of suggestive loci driven by individual ancestry groups. LRRC4C (Leucine‐Rich Repeat Containing 4C; MIM:608817) encodes NGL1, a ligand of Netrin‐G1 in the netrin family of axon guidance molecules, and has been shown to regulate development and function of thalamocortical axons. Multi‐ancestry studies with even larger sample sizes will provide even more powerful for further elucidating the genomic underpinnings of LOAD
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Exploring effect of known Alzheimer disease genetic loci in the Peruvian population
Background
Native American populations are substantially underrepresented in Alzheimer disease (AD) genetic studies. The Peruvian (PE) population with up to ∼80 of Amerindian ancestry (AI) provides a unique opportunity to assess the role of AI ancestry in AD. We performed whole‐genome sequencing in PE case‐control study to assess the effect of the known AD loci in PE population.
Methods
Whole‐genome sequencing was performed in 96 AD cases and 145 unrelated cognitive healthy controls from PE population. We calculated the global ancestry (principal components) using the EIGENSTRAT approach. We tested 21 AD lead variants from the recent large non‐Hispanic White (NHW) GWAS of AD (Kunkle et al. 2019). We performed association analyses using logistic regression model with accounting for age, gender, and population substructure (first three principal components). We used Bonferroni approach for multiple test correction.
Results
Logistic regression analysis confirmed association of APOE with AD (rs429358, OR=3.6, CI:1.9‐7.0; pv < 8.4e‐05) in PE population. CLU loci (rs9331896, pv=9.3e‐04) passed the significance threshold after Bonferroni multiple test correction. Two AD loci demonstrated nominal associations (pv<0.05), which were EPHA1 (rs10808026, pv = 0.028), and FERMT1 (rs17125924, pv=0.022) loci.
Conclusion
Our results showed that known AD APOE and CLU loci are significantly associated with AD in PE population. Some of the genes demonstrated suggestive associations, but further analysis with a larger sample size is on‐going to determine if these reflect true associations
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Multi‐Ancestry Genome‐wide Association Analysis of Late‐Onset Alzheimer’s Disease (LOAD) in 60,941 Individuals Identifies Multiple Novel Cross‐Ancestry Associations and Implicates Amyloid and Complement Pathways
Abstract Background Lack of diversity in expanding genomic studies has limited the identification of LOAD risk variants that may be more prevalent in non‐European ancestry groups with more modest power requirements for detection. To help reverse this trend, we constructed and analyzed a multi‐ancestry collection of GWAS datasets in the Alzheimer’s Disease Genetics Consortium (ADGC) to identify novel shared and ancestry‐unique LOAD susceptibility loci and related biological pathways. Method The multi‐ancestry data collection of the ADGC includes GWAS genotype and phenotype data on 38,774 non‐Hispanic White (NHW), 7,454 African American (AA), 11,436 Hispanic (HI), and 3,277 East Asian (EAS) subjects imputed to the NHLBI TOPMed v5 reference panel. We performed a two‐stage analysis: (1) single‐variant association using score‐based logistic regression for population‐based datasets and generalized linear mix‐models for family studies with covariate adjustment for onset/exam age, sex, principal components for population substructure, and APOE ε2/ε3/ε4 genotype, followed by within‐ancestry fixed‐effects meta‐analysis using METAL; and (2) cross‐ancestry meta‐analysis of within‐ancestry associations using random‐effects modeling (RE2) in METASOFT. Secondary analyses include pathway analysis using EnrichR and gene‐based association testing using SKAT‐O (on‐going). Result In addition to APOE region associations, we identified 13 loci with genome‐wide significant (GWS; P≤5×10 −8 ) cross‐ancestry associations including chromosomes 1q32.2 (CR1), 2q14 (BIN1), 6p21.1 (TREM2), 6p12.3 (CD2AP), 8p21.2 (PTK2B), 8p21.1 (CLU), 8q24.3 (SHARPIN), 11q12 (MS4A6A), 11q14 (PICALM), 19p13.3 (ABCA7), and novel loci at 11p2 (LRRC4C) and 12q24.13 (LHX5‐AS1). An additional 13 loci reached suggestive significance (P<10 −5 ), including loci with ancestry‐specific associations attaining GWS such as PALM2AKAP2 (P = 2.5×10 −8 in EAS), GRB14 (P = 1.7×10 −8 in HI), and KIAA0825 (P = 2.9×10 −8 in NHW). Follow‐up pathway analysis implicated multiple amyloid regulation pathways (strongest: negative regulation of APP catabolism, adjusted P = 1.6×10 −4 ) and the classical complement pathway (adjusted P = 1.3×10 −3 ). Follow‐up analyses including fine‐mapping and gene‐based analyses are on‐going. Conclusion Cross‐ancestry GWAS meta‐analyses identified novel LOAD susceptibility loci in/near LRRC4C and LHX5‐AS1, both with known roles in neuronal development, as well as several novel ancestry‐unique loci, showing their tremendous value for gene discovery with smaller sample sizes than current European ancestry LOAD GWAS. Even larger multi‐ancestry studies will provide even more power for further elucidating the genomics of LOAD
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Admixed ancestral composition with Amerindian predominance at the Peruvian Alzheimer Disease Initiative (PeADI)
Background
Current genetic studies for AD and other dementias are making efforts to incorporate underrepresented populations including admixed Latinos. Peruvian population is characterized by admixed ancestry with a significant Amerindian component, which varies according to specific regions across Peru. The Peruvian Alzheimer Disease initiative (PeADI) was developed to ascertain a cohort for AD and other related dementias for genetic studies in Peru. We aim to determine the patterns of continental ancestry by regions across Peru.
Methods
Over the last 3 years, The PeADI study has recruited 212 unrelated cognitive participants through collaborative health centers and community outreach ascertainment strategies. Cases were assessed by neurologists following NINDS‐ADRDA criteria. Controls were screened using MMSE, Clock drawing test and Pfeffer functional activities questionnaire. Genome‐wide genotyping was performed by Illumina screening array. PC‐AiR and model‐based. The cohort was divided into five regions (Northern, Southern, Lima, Central Highlands, and Amazonian) based on place of birth of the participant or the ancient known ancestor.
Results
The global Admixture analysis showed that Peruvians have a substantial Amerindian component (63.6%), followed by European (35.9%), African (2.5%) and East Asian (2.1%) components. When analyzed by regions, we found that the Central region concentrates the highest Amerindian ancestry (72.7%), followed by the Northern region (58.2%), Lima and Callao (57%), the Amazonian region (55.6%), and the Southern region (55.1%). The highest European ancestry is located in the Amazonian region (42.1%), while the Central region has the lowest (26.6%). African and East Asian ancestry has little influence in Peru, being the Northern with the higher African component (4.1%), and Lima and Callao the region with the higher East Asian component (3.5%). There is no significant for Amerindian component across five regions (p=0.054); however, Central highlands region has higher Amerindian component compared to Lima (p=0.005), the Northern (p=0.005) and Southern(p=0.017) regions.
Conclusion
Our results confirmed the ancestry admixture of the Peruvian population with predominance of the Amerindian component. The Central region concentrates the highest Amerindian ancestry compared with other regions across Peru