GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis

Vitale, Dan; Koretsky, Mathew; Kuznetsov, Nicole; Hong, Samantha; Martin, Jessica; James, Mikayla; Makarious, Mary B; Leonard, Hampton; Iwaki, Hirotaka; Faghri, Faraz; Blauwendraat, Cornelis; Singleton, Andrew B.; Song, Yeajin; Levine, Kristin; Sreelatha, Ashwin Ashok Kumar; Fang, Zih-Hua; Nalls, Mike

doi:10.1101/2024.03.26.586362

Cited by 9 publications

(3 citation statements)

References 17 publications

(25 reference statements)

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“…Variants flagged by GCAD for removal were excluded, with additional variant and sample QC conducted using GenoTools 21 . Variants were excluded if the call rate <0.95, not in Hardy–Weinberg equilibrium (p < 1×10–4); samples were excluded if the call rate was <0.95, discordant sex was reported based on X chromosome heterozygosity, cryptic relatedness, and either insufficient or excessive heterozygosity.…”

Section: Methodsmentioning

confidence: 99%

The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes

Andrews,

Jonson,

Fulton-Howard

et al. 2024

Preprint

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Importance: By integrating genetic and clinical risk factors into genomic-informed dementia risk reports, healthcare providers can offer patients detailed risk profiles to facilitate understanding of individual risk and support the implementation of personalized strategies for promoting brain health. Objective: To develop a genomic-informed risk assessment composed of family history, genetic, and clinical risk factors and, in turn, evaluate how the risk assessment predicted incident dementia. Design: This longitudinal study included data from two clinical case-control cohorts with an average of 6.6 visits. Secondary analyses were conducted from July 2023 - March 2024. Setting: Data were previously collected across multiple US locations from 1994 to 2023. Participants: Older adults aged 55+ with whole-genome sequencing and dementia-free at baseline. Exposures: An additive score comprising the modified Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (mCAIDE), family history of dementia, APOE genotype, and an AD polygenic risk score. Main Outcome(s) and Measure(s): The risk of progression to all-cause dementia was evaluated using Cox-proportional hazard models (hazard ratios with 95% confidence intervals [OR 9%CI]). Results: A total of 3,429 older adults were included (aged 75 +/- 7 years; 59% female; 75% non-Latino White, 15% Black, 5.2% Latino, 3.6% other, and 0.4% Asian; 27% MCI), with 751 participants progressing to dementia. The most common high-risk indicator was a family history of dementia (56%), followed by APOE*e4 genotype (36%), high mCAIDE score (34%), and high AD-PRS (11%). Most participants had at least one high-risk indicator, with 39% having one, 32% two, 9.8% three, and 1% four. The presence of 1, 2, 3, or 4 risk indicators was associated with a doubling (HR = 1.72, CI: 1.34-2.22, p = 2.5e-05), tripling (HR = 3.09, CI: 2.41-3.95, p = 4.4e-19), quadrupling (HR = 4.46, CI: 3.34-5.94, p = 2.2e-24), and a twelvefold increase (HR = 12.15, CI: 7.33-20.14, p = 3.2e-22) in dementia risk. Conclusion & Relevance: We found that most participants in memory and aging clinics had at least one high-risk indicator for dementia. Furthermore, we observed a dose-response relationship where a greater number of risk indicators was associated with an increased risk of incident dementia.

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Section: Methodsmentioning

confidence: 99%

The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes

Andrews,

Jonson,

Fulton-Howard

et al. 2024

Preprint

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“…These classifier models were then applied to the dataset to generate ancestry estimates for all samples. Detailed methodologies for the cloud-based and scalable pipeline employed for genotype calling, QC, and ancestry estimation are documented in the GenoTools 23 GitHub repository (https://doi.org/10.5281/zenodo.10719034) 27 .…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the array includes 96,517 customized variants. Automated genotype data processing was conducted on GenoTools 23 , a Python pipeline built for quality control and ancestry estimation of data. Additional details can be found at https://pypi.org/project/the-real-genotools/ 23 .…”

Section: Methodsmentioning

confidence: 99%

Insights into Ancestral Diversity in Parkinson’s Disease Risk: A Comparative Assessment of Polygenic Risk Scores

Saffie-Awad,

Makarious,

Elsayed

et al. 2023

Preprint

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ObjectiveThis study aims to address disparities in risk prediction by evaluating the performance of polygenic risk score (PRS) models using the 90 risk variants across 78 independent loci previously linked to Parkinson’s disease (PD) risk across seven diverse ancestry populations.MethodsWe conducted a multi-stage study, testing PRS models in predicting PD status across seven different ancestries applying three approaches: 1) PRS adjusted by gender and age; 2) PRS adjusted by gender, age and principal components (PCs); and 3) PRS adjusted by gender, age and percentage of population admixture. These models were built using the largest four population-specific summary statistics of PD risk to date (base data) and individual level data obtained from the Global Parkinson’s Genetics Program (target data). We performed power calculations to estimate the minimum sample size required to conduct these analyses. A total of 91 PRS models were developed to investigate cumulative known genetic variation associated with PD risk and age of onset in a global context.ResultsWe observed marked heterogeneity in risk estimates across non-European ancestries, including East Asians, Central Asians, Latino/Admixed Americans, Africans, African admixed, and Ashkenazi Jewish populations. Risk allele patterns for the 90 risk variants yielded significant differences in directionality, frequency, and magnitude of effect. PRS did not improve in performance when predicting disease status using similar base and target data across multiple ancestries, demonstrating that cumulative PRS models based on current known risk are inherently biased towards European populations. We found that PRS models adjusted by percentage of admixture outperformed models that adjusted for conventional PCs in highly admixed populations. Overall, the clinical utility of our models in individually predicting PD status is limited in concordance with the estimates observed in European populations.InterpretationThis study represents the first comprehensive assessment of how PRS models predict PD risk and age at onset in a multi-ancestry fashion. Given the heterogeneity and distinct genetic architecture of PD across different populations, our assessment emphasizes the need for larger and diverse study cohorts of individual-level target data and well-powered ancestry-specific summary statistics. Our current understanding of PD status unraveled through GWAS in European populations is not generally applicable to other ancestries. Future studies should integrate clinical and *omics level data to enhance the accuracy and predictive power of PRS across diverse populations.

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Parkinson’s families project: a UK-wide study of early onset and familial Parkinson’s disease

Towns,

Fang,

Tan

et al. 2024

npj Parkinsons Dis.

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The Parkinson’s Families Project is a UK-wide study aimed at identifying genetic variation associated with familial and early-onset Parkinson’s disease (PD). We recruited individuals with a clinical diagnosis of PD and age at motor symptom onset ≤45 years and/or a family history of PD in up to third-degree relatives. Where possible, we also recruited affected and unaffected relatives. We analysed DNA samples with a combination of single nucleotide polymorphism (SNP) array genotyping, multiplex ligation-dependent probe amplification (MLPA), and whole-genome sequencing (WGS). We investigated the association between identified pathogenic mutations and demographic and clinical factors such as age at motor symptom onset, family history, motor symptoms (MDS-UPDRS) and cognitive performance (MoCA). We performed baseline genetic analysis in 718 families, of which 205 had sporadic early-onset PD (sEOPD), 113 had familial early-onset PD (fEOPD), and 400 had late-onset familial PD (fLOPD). 69 (9.6%) of these families carried pathogenic variants in known monogenic PD-related genes. The rate of a molecular diagnosis increased to 28.1% in PD with motor onset ≤35 years. We identified pathogenic variants in LRRK2 in 4.2% of families, and biallelic pathogenic variants in PRKN in 3.6% of families. We also identified two families with SNCA duplications and three families with a pathogenic repeat expansion in ATXN2, as well as single families with pathogenic variants in VCP, PINK1, PNPLA6, PLA2G6, SPG7, GCH1, and RAB32. An additional 73 (10.2%) families were carriers of at least one pathogenic or risk GBA1 variant. Most early-onset and familial PD cases do not have a known genetic cause, indicating that there are likely to be further monogenic causes for PD.

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GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis

Cited by 9 publications

References 17 publications

The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes

The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes

Insights into Ancestral Diversity in Parkinson’s Disease Risk: A Comparative Assessment of Polygenic Risk Scores

Parkinson’s families project: a UK-wide study of early onset and familial Parkinson’s disease

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