Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author

Polygenic Risk Score Analysis of Alzheimer’s Disease in Cases without APOE4 or APOE2 Alleles

The We and others have previously shown that polygenic risk score analysis (PRS) has considerable predictive utility for identifying those at high risk of developing Alzheimer’s disease (AD) with an area under the curve (AUC) of >0.8. However, by far the greatest determinant of this risk is the apolipoprotein E locus with the E4 allele alone giving an AUC of ∼0.68 and the inclusion of the protective E2 allele increasing this to ∼0.69 in a clinical cohort. An important question is to determine how good PRS is at predicting risk in those who do not carry the E4 allele (E3 homozygotes, E3E2 and E2E2) and in those who carry neither the E4 or E2 allele (i.e. E3 homozygotes). Previous studies have shown that PRS remains a significant predictor of AD risk in clinical cohorts after controlling for APOE ε4 carrier status. In this study we assess the accuracy of PRS prediction in a cohort of pathologically confirmed AD cases and controls. The exclusion of APOE4 carriers has surprisingly little effect on the PRS prediction accuracy (AUC ∼0.83 [95% CI: 0.80-0.86]), and the accuracy remained higher than that in clinical cohorts with APOE included as a predictor. From a practical perspective this suggests that PRS analysis will have predictive utility even in E4 negative individuals and may be useful in clinical trial design

Serum Neurofilament Light is elevated in COVID-19 Positive Adults in the ICU and is associated with Co-Morbid Cardiovascular Disease, Neurological Complications, and Acuity of Illness

In critically ill COVID-19 patients, the risk of long-term neurological consequences is just beginning to be appreciated. While recent studies have identified that there is an increase in structural injury to the nervous system in critically ill COVID-19 patients, there is little known about the relationship of COVID-19 neurological damage to the systemic inflammatory diseases also observed in COVID-19 patients. The purpose of this pilot observational study was to examine the relationships between serum neurofilament light protein (NfL, a measure of neuronal injury) and co-morbid cardiovascular disease (CVD) and neurological complications in COVID-19 positive patients admitted to the intensive care unit (ICU). In this observational study of one-hundred patients who were admitted to the ICU in Tucson, Arizona between April and August 2020, 89 were positive for COVID-19 (COVID-pos) and 11 was COVID-negative (COVID-neg). A healthy control group (n=8) was examined for comparison. The primary outcomes and measures were subject demographics, serum NfL, presence and extent of CVD, diabetes, sequential organ failure assessment score (SOFA), presence of neurological complications, and blood chemistry panel data. COVID-pos patients in the ICU had significantly higher mean levels of Nfl (229.6 ± 163 pg/ml) compared to COVID-neg ICU patients (19.3 ± 5.6 pg/ml), Welch's t-test, p =.01 and healthy controls (12.3 ± 3.1 pg/ml), Welch's t-test p =.005. Levels of Nfl in COVID-pos ICU patients were significantly higher in patients with concomitant CVD and diabetes (n=35, log Nfl 1.6±.09), and correlated with higher SOFA scores (r=.5, p =.001). These findings suggest that in severe COVID-19 disease, the central neuronal and axonal damage in these patients may be driven, in part, by the level of systemic cardiovascular disease and peripheral inflammation. Understanding the contributions of systemic inflammatory disease to central neurological degeneration in these COVID-19 survivors will be important to the design of interventional therapies to prevent long-term neurological and cognitive dysfunction

Common BACE2 Polymorphisms are Associated with Altered Risk for Alzheimer's Disease and CSF Amyloid Biomarkers in APOE ε4 Non-Carriers

It was recently suggested that beta-site amyloid precursor protein (APP)-cleaving enzyme 2 (BACE2) functions as an amyloid beta (A beta)-degrading enzyme; in addition to its better understood role as an APP secretase. Due to this finding we sought to understand the possible genetic risk contributed by the BACE2 locus to the development of late-onset Alzheimer's disease (AD). In this study, we report that common single nucleotide polymorphism (SNP) variation in BACE2 is associated with altered AD risk in apolipoprotein E gene (APOE) epsilon 4 variant (e4) non-carriers. In addition, in e4 non-carriers diagnosed with AD or mild cognitive impairment (MCI), SNPs within the BACE2 locus are associated with cerebrospinal fluid (CSF) levels of A beta 1-42. Further, SNP variants in BACE2 are also associated with BACE2 RNA expression levels suggesting a potential mechanism for the CSF A beta 1-42 findings. Lastly, overexpression of BACE2 in vitro resulted in decreased A beta 1-40 and A beta 1-42 fragments in a cell line model of A beta production. These findings suggest that genetic variation at the BACE2 locus modifies AD risk for those individuals who don't carry the e4 variant of APOE. Further, our data indicate that the biological mechanism associated with this altered risk is linked to amyloid generation or clearance possibly through BACE2 expression changes.National Institute on Aging (NIA); National Alzheimer's Coordinating Center (NACC) [U01 AG016976]; National Institute on Aging: Ruth Seemann, John Hopkins Alzheimer's Disease Research Center (NIA) [AG05146, P50 AG16570, AG05128]; NINDS [NS39764]; Glaxo Smith Kline [P50-AG053760, AG05144, P50AG05681, P50 AG05136, P30-AG13846, 211002]; Arizona Biomedical Research Commission [4001, 0011, 05_ 901]; Michael J. Fox Foundation [AG10161, HHSN-271-2013-00030C]; McGowan Endowment; Medical Research Council, local NHS trusts and Newcastle University; Medical Research Council; Safa Al-Sarraj; Netherlands Brain Bank; Stichting MS Research, Brain Net Europe; Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds; Internationale Stiching Alzheimer Onderzoek; NIH-NIA [R01-AG041232]; State of Arizona DHS (Arizona Alzheimer's Consortium) - NIH EUREKA [R01-AG034504]; NIH intramural funds; UK Dementia Research Institute; DRI Ltd - UK Medical Research Council; Alzheimer's Society; Alzheimer's Research UK - Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]; DOD ADNI (Department of Defense) [W81XWH-12-2-0012]; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Alzheimer'sAssociation; Alzheimer's Drug Discovery Foundation; Araclon Biotech; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd; Fujirebio; Johnson & Johnson Pharmaceutical Research & Development LLC.; Merck Co., Inc.; Meso Scale Diagnostics; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Takeda Pharmaceutical Company; Canadian Institutes of Health Research isproviding funds; ADNI clinical sites in Canada; Foundation for the National Institutes of Health; Northern California Institute for Research and Education; Laboratory for Neuro Imaging at the University of Southern CaliforniaOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Identification of disease causing loci using an array-based genotyping approach on pooled DNA

BACKGROUND: Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs. RESULTS: We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided. CONCLUSION: Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs

Extracellular microRNAs in blood differentiate between ischaemic and haemorrhagic stroke subtypes.

Rapid identification of patients suffering from cerebral ischaemia, while excluding intracerebral haemorrhage, can assist with patient triage and expand patient access to chemical and mechanical revascularization. We sought to identify blood-based, extracellular microRNAs 15 (ex-miRNAs) derived from extracellular vesicles associated with major stroke subtypes using clinical samples from subjects with spontaneous intraparenchymal haemorrhage (IPH), aneurysmal subarachnoid haemorrhage (SAH) and ischaemic stroke due to cerebral vessel occlusion. We collected blood from patients presenting with IPH (n = 19), SAH (n = 17) and ischaemic stroke (n = 21). We isolated extracellular vesicles from plasma, extracted RNA cargo, 20 sequenced the small RNAs and performed bioinformatic analyses to identify ex-miRNA biomarkers predictive of the stroke subtypes. Sixty-seven miRNAs were significantly variant across the stroke subtypes. A subset of exmiRNAs differed between haemorrhagic and ischaemic strokes, and LASSO analysis could distinguish SAH from the other subtypes with an accuracy of 0.972 ± 0.002. Further analyses predicted 25 miRNA classifiers that stratify IPH from ischaemic stroke with an accuracy of 0.811 ± 0.004 and distinguish haemorrhagic from ischaemic stroke with an accuracy of 0.813 ± 0.003. Blood-based, ex-miRNAs have predictive value, and could be capable of distinguishing between major stroke subtypes with refinement and validation. Such a biomarker could one day aid in the triage of patients to expand the pool eligible for effective treatment

Extracellular microRNAs in blood differentiate between ischaemic and haemorrhagic stroke subtypes

Rapid identification of patients suffering from cerebral ischaemia, while excluding intracerebral haemorrhage, can assist with patient triage and expand patient access to chemical and mechanical revascularization. We sought to identify blood-based, extracellular microRNAs 15 (ex-miRNAs) derived from extracellular vesicles associated with major stroke subtypes using clinical samples from subjects with spontaneous intraparenchymal haemorrhage (IPH), aneurysmal subarachnoid haemorrhage (SAH) and ischaemic stroke due to cerebral vessel occlusion. We collected blood from patients presenting with IPH (n = 19), SAH (n = 17) and ischaemic stroke (n = 21). We isolated extracellular vesicles from plasma, extracted RNA cargo, 20 sequenced the small RNAs and performed bioinformatic analyses to identify ex-miRNA biomarkers predictive of the stroke subtypes. Sixty-seven miRNAs were significantly variant across the stroke subtypes. A subset of exmiRNAs differed between haemorrhagic and ischaemic strokes, and LASSO analysis could distinguish SAH from the other subtypes with an accuracy of 0.972 +/- 0.002. Further analyses predicted 25 miRNA classifiers that stratify IPH from ischaemic stroke with an accuracy of 0.811 +/- 0.004 and distinguish haemorrhagic from ischaemic stroke with an accuracy of 0.813 +/- 0.003. Blood-based, ex-miRNAs have predictive value, and could be capable of distinguishing between major stroke subtypes with refinement and validation. Such a biomarker could one day aid in the triage of patients to expand the pool eligible for effective treatment.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Common BACE2 Polymorphisms are Associated with Altered Risk for Alzheimer's Disease and CSF Amyloid Biomarkers in APOE ε4 Non-Carriers

It was recently suggested that beta-site amyloid precursor protein (APP)-cleaving enzyme 2 (BACE2) functions as an amyloid beta (Aβ)-degrading enzyme; in addition to its better understood role as an APP secretase. Due to this finding we sought to understand the possible genetic risk contributed by the BACE2 locus to the development of late-onset Alzheimer's disease (AD). In this study, we report that common single nucleotide polymorphism (SNP) variation in BACE2 is associated with altered AD risk in apolipoprotein E gene (APOE) epsilon 4 variant (ε4) non-carriers. In addition, in ε4 non-carriers diagnosed with AD or mild cognitive impairment (MCI), SNPs within the BACE2 locus are associated with cerebrospinal fluid (CSF) levels of Aβ1-42. Further, SNP variants in BACE2 are also associated with BACE2 RNA expression levels suggesting a potential mechanism for the CSF Aβ1-42 findings. Lastly, overexpression of BACE2 in vitro resulted in decreased Aβ1-40 and Aβ1-42 fragments in a cell line model of Aβ production. These findings suggest that genetic variation at the BACE2 locus modifies AD risk for those individuals who don't carry the ε4 variant of APOE. Further, our data indicate that the biological mechanism associated with this altered risk is linked to amyloid generation or clearance possibly through BACE2 expression changes

Calmodulin-binding transcription activator 1 (CAMTA1) alleles predispose human episodic memory performance

Little is known about the genes and proteins involved in the process of human memory. To identify genetic factors related to human episodic memory performance, we conducted an ultra-high-density genome-wide screen at > 500000 single nucleotide polymorphisms (SNPs) in a sample of normal young adults stratified for performance on an episodic recall memory test. Analysis of this data identified SNPs within the calmodulin-binding transcription activator 1 (CAMTA1) gene that were significantly associated with memory performance. A follow up study, focused on the CAMTA1 locus in an independent cohort consisting of cognitively normal young adults, singled out SNP rs4908449 with a P-value of 0.0002 as the most significant associated SNP in the region. These validated genetic findings were further supported by the identification of CAMTA1 transcript enrichment in memory-related human brain regions and through a functional magnetic resonance imaging experiment on individuals matched for memory performance that identified CAMTA1 allele-specific upregulation of medial temporal lobe brain activity in those individuals harboring the ‘at-risk' allele for poorer memory performance. The CAMTA1 locus encodes a purported transcription factor that interfaces with the calcium-calmodulin system of the cell to alter gene expression patterns. Our validated genomic and functional biological findings described herein suggest a role for CAMTA1 in human episodic memor

Resistance to autosomal dominant Alzheimer's disease in an APOE3 Christchurch homozygote: a case report.

We identified a PSEN1 (presenilin 1) mutation carrier from the world's largest autosomal dominant Alzheimer's disease kindred, who did not develop mild cognitive impairment until her seventies, three decades after the expected age of clinical onset. The individual had two copies of the APOE3 Christchurch (R136S) mutation, unusually high brain amyloid levels and limited tau and neurodegenerative measurements. Our findings have implications for the role of APOE in the pathogenesis, treatment and prevention of Alzheimer's disease