ApoE gene polymorphisms and metals and their interactions with cognitive function

Ye, Zeyan; Tan, Dechan; Luo, Tingyu; Gou, Ruoyu; Cai, Jianshen; Wei, Yanfei; He, Kailian; Xiao, Song; Mai, Tingyu; Tang, Xu; Liu, Qiumei; Mo, Xiaoting; Lin, Yinxia; Huang, Shenxiang; Li, You; Qin, Jian; Zhang, Zhiyong

doi:10.1186/s12920-023-01632-6

Cited by 3 publications

(1 citation statement)

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“…The top variant was rs405509, which is known to be located at the APOE promoter and is a widely reported risk factor for AD (Ma et al ., 2016). It is linked to cognitive impairment of the elderly and can affect brain structure as well (Ye et al ., 2023; Ma et al ., 2016). Another variant linked to NECTIN2 was rs57537848.…”

Section: Resultsmentioning

confidence: 99%

New genetic loci discovery for Alzheimer’s disease using explainable deep neural networks

Gkertsou,

Parameshwarappa,

Shiyanbola

et al. 2023

Preprint

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BackgroundGenome-wide association studies (GWAS) have shed light on various complex diseases and traits, by detecting more than 400,000 associated genetic loci. This number is expected to drastically increase because of the use of novel artificial intelligence methods offering new ways to study the effects of variants. Deep learning using artificial neural networks (ANN) is a sub-field of artificial intelligence, which simulates how the human brain learns. We aimed at assessing the potential of deep learning in human genetic studies of Alzheimer’s Disease (AD) and how these compare to the traditional statistical methods used in GWAS, by simultaneously testing the two approaches on the same dataset, while discovering new genetic loci associated to AD.MethodsTo address this aim, phenotypic and genome-wide SNP data from the UK Biobank was analysed on a binary outcome, AD diagnosis, in two different data balance options, of one-to-one and one-to-two case-control datasets, using 2,764 cases vs 2,764 controls and 5,528 controls respectively matched on gender, age, ethnicity, PC1-20 and genotyping array. Genetic data handling and GWAS were performed using PLINK, whereas neural networks were trained using GenNet, a new ANN tool, with the same datasets, separated into training (60%), validation (20%) and test (20%) sets. Neural network layers were determined using biological knowledge, by annotating SNPs to genes and genes to AD related pathways, using ANNOVAR annotations followed by GeneSCF and KEGG.ResultsSignificant associations were detected between four SNPs linked to two different genes and AD for the 1 to 1 case-control study design and six SNPs linked to four different genes for the 1 to 2 case-control study design by using PLINK. All identified regions have been previously associated to AD. GenNet identified twelve SNPs on seven genes to be associated with AD, all with biological plausibility, achieving an AUC of 0.80 when using three biologically determined layers and 0.73 when using two layers at the neural networks. No common top SNPs were identified between the machine learning and GWAS models.ConclusionThis is one of the first studies attempting to compare the traditional GWAS to more sophisticated state-of-art methods for understanding the genetic architecture of complex phenotypes using the same dataset. More systematic comparisons with such approaches on real data are needed to enable best practises for machine learning in the analysis of genome-wide genetic data.

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Section: Resultsmentioning

confidence: 99%

New genetic loci discovery for Alzheimer’s disease using explainable deep neural networks

Gkertsou,

Parameshwarappa,

Shiyanbola

et al. 2023

Preprint

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Urinary Metal Levels, Cognitive Test Performance, and Dementia in the Multi-Ethnic Study of Atherosclerosis

Domingo-Relloso,

McGraw,

Heckbert

et al. 2024

JAMA Netw Open

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ImportanceMetals are established neurotoxicants, but evidence of their association with cognitive performance at low chronic exposure levels is limited.ObjectiveTo investigate the association of urinary metal levels, individually and as a mixture, with cognitive tests and dementia diagnosis, including effect modification by apolipoprotein ε4 allele (APOE4).Design, Setting, and ParticipantsThe multicenter prospective cohort Multi-Ethnic Study of Atherosclerosis (MESA) was started from July 2000 to August 2002, with follow-up through 2018. A total of 6303 MESA participants were included. Data analysis was performed from October 12, 2023, to June 13, 2024.ExposureUrine samples were collected at baseline (2000-2002), and arsenic, cadmium, cobalt, copper, lead, manganese, tungsten, uranium, and zinc levels were measured in 2020-2022.Main Outcomes and MeasuresDigit Symbol Coding (DSC) (n = 3819) (possible score range, 0-133), Cognitive Abilities Screening Instrument (CASI) (n = 3918) (possible score range, 0-100), and Digit Span (DS) (n = 4176) (possible score range, 0-30) cognitive tests were administered in 2010-2012; higher scores of each test indicate increasing levels of positive response.ResultsA total of 6303 participants were followed up for dementia diagnosis through 2018. The median age at baseline was 60 (IQR, 53-70) years, and 3303 participants (52.4%) were female. The median cognitive scores were 51 (IQR, 38-64) for DSC, 90 (IQR, 84-95) for CASI, and 15 (IQR, 12-18) for DS. There were 559 cases of dementia through the follow-up period. Inverse associations with DSC were identified: mean differences in z scores per IQR increase in metal levels were −0.03 (95% CI, −0.07 to 0.00) for arsenic, −0.05 (95% CI, −0.09 to −0.004) for cobalt, −0.05 (95% CI, −0.07 to −0.02) for copper, −0.04 (95% CI, −0.08 to −0.001) for uranium, and −0.03 (95% CI, −0.06 to −0.01) for zinc. Among 1058 APOE4 carriers, manganese was also inversely associated with DSC. The joint mean difference of DSC comparing percentile 95th with the 25th of the 9-metal mixture was −0.30 (95% CI, −0.47 to −0.14) for APOE4 carriers and −0.10 (95% CI, −0.19 to −0.01) for noncarriers. Arsenic, cadmium, cobalt, copper, tungsten, uranium, and zinc were individually associated with dementia, with hazard ratios per IQR of metal ranging from 1.15 (95% CI, 1.03-1.29) for tungsten to 1.46 (95% CI, 1.06-2.02) for uranium. The joint hazard ratio of dementia comparing percentiles 95th with the 25th of the 9-metal mixture was 1.71 (95% CI, 1.24-3.89), with no significant difference by APOE4 status.Conclusions and RelevanceIn this study, participants with higher concentrations of metals in their urine, compared with those with lower concentrations, had worse performance on cognitive tests and greater likelihood of developing dementia. The findings of this multicenter multiethnic cohort study might inform screening and potential interventions for prevention of dementia based on individuals’ metal exposure levels and genetic profiles.

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Mapping the APOE structurally on missense variants in elderly Brazilians

de Lima Pizzico,

Beatriz Máximo,

Hirata

et al. 2024

Journal of Biomolecular Structure and Dynamics

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ApoE gene polymorphisms and metals and their interactions with cognitive function

Cited by 3 publications

References 40 publications

New genetic loci discovery for Alzheimer’s disease using explainable deep neural networks

New genetic loci discovery for Alzheimer’s disease using explainable deep neural networks

Urinary Metal Levels, Cognitive Test Performance, and Dementia in the Multi-Ethnic Study of Atherosclerosis

Mapping the APOE structurally on missense variants in elderly Brazilians

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