<i>genipe</i>: an automated genome-wide imputation pipeline with automatic reporting and statistical tools

Perreault, Louis‐Philippe Lemieux; Legault, Marc-André; Asselin, Géraldine; Dubé, Marie‐Pierre

doi:10.1093/bioinformatics/btw487

Cited by 25 publications

(13 citation statements)

References 7 publications

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“…The top 10 principal components of each of the 5 clusters of the SPARK cohort were calculated separately to be used as covariates in heritability, polygenic risk score, and genome-wide association analyses. These remaining individuals and SNPs were imputed to the 1,000 Genomes Phase 3 reference of their respective cluster using the Genipe pipeline ( 46 ). Genipe performed LD calculation and pruning with PLINK ( 43 ), genotype phasing with SHAPEIT ( 47 ), and genotype imputation by IMPUTE2 ( 48 ) using default parameters.…”

Section: Methodsmentioning

confidence: 99%

Estimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort

et al. 2021

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This study is the first genetically-informed investigation of avoidant/restrictive food intake disorder (ARFID), an eating disorder that profoundly impacts quality of life for those affected. ARFID is highly comorbid with autism, and we provide the first estimate of its prevalence in a large and phenotypically diverse autism cohort (a subsample of the SPARK study, N = 5,157 probands). This estimate, 21% (at a balanced accuracy 80%), is at the upper end of previous estimates from studies based on clinical samples, suggesting under-diagnosis and potentially lack of awareness among caretakers and clinicians. Although some studies suggest a decrease of disordered eating symptoms by age 6, our estimates indicate that up to 17% (at a balanced accuracy 87%) of parents of autistic children are also at heightened risk for ARFID, suggesting a lifelong risk for disordered eating. We were also able to provide the first estimates of narrow-sense heritability (h2) for ARFID risk, at 0.45. Genome-wide association revealed a single hit near ZSWIM6, a gene previously implicated in neurodevelopmental conditions. While, the current sample was not well-powered for GWAS, effect size and heritability estimates allowed us to project the sample sizes necessary to more robustly discover ARFID-linked loci via common variants. Further genetic analysis using polygenic risk scores (PRS) affirmed genetic links to autism as well as neuroticism and metabolic syndrome.

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Section: Methodsmentioning

confidence: 99%

Estimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort

et al. 2021

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“…Imputer makes it easy for Members to augment their existing genetic data sources using techniques that were previously difficult to access. The Imputer imputation pipeline was built using genipe [80] and uses the 1000 Genomes Project [81] genotype data as the reference population.…”

Section: Resultsmentioning

confidence: 99%

Open Humans: A platform for participant-centered research and personal data exploration

Tzovaras

Angrist

Arvai³

et al. 2019

GigaScience

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Background Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals). Results To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects. Conclusions Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.

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“…Imputer makes it easy for members to augment their existing genetic data sources using techniques that were previously di cult to access. The Imputer imputation pipeline was built using genipe [77] and uses the 1000 Genomes Project [78] genotype data as the reference population.…”

Section: Genetic Data Augmentationmentioning

confidence: 99%

Open Humans: A platform for participant-centered research and personal data exploration

Tzovaras

Arvai

Dulaney

et al. 2018

Preprint

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Background: Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This o ers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data come practical problems (such as how to merge data streams from various sources),as well as ethical problems (such as how to best balance risks and bene ts when enabling personal data sharing by individuals). Results: To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g. personal genetic data, wearable activity monitors, GPS location records and continuous glucose monitor data), along with use cases of how the data facilitates various projects. Conclusions: Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy. BackgroundResearch involving human participants, from biomedical and health research to social sciences studies, is experiencing rapid changes. The rise of electronic records, online platforms, and data from devices contribute to a sense that these collected data can change how research in these elds is performed [1,2,3,4] Among the impacted disciplines is precision medicine -

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genipe: an automated genome-wide imputation pipeline with automatic reporting and statistical tools

Cited by 25 publications

References 7 publications

Estimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort

Estimating the Prevalence and Genetic Risk Mechanisms of ARFID in a Large Autism Cohort

Open Humans: A platform for participant-centered research and personal data exploration

Open Humans: A platform for participant-centered research and personal data exploration

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