Thesis

Comparison of GWAS analysis pipelines using Hail/VariantSpark and PLINK on Late-Onset Alzheimer’s Dataset

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Genome Wide Association Studies (GWAS) aims to find genomic variants that correlates with the
occurrence of certain traits within a population. Various tools and algorithms can be utilized to conduct
GWAS, among them are PLINK, Hail, and VariantSpark. In this study, a GWAS analysis pipeline was
developed using PLINK, Hail, and VariantSpark to assess their performance in conduction GWAS
association analysis. The Late-onset Alzheimer’s (LOAD) GWAS dataset from a previous study by
Webster et al. (2009) was used on these softwares. PLINK ran Simple Association, Linear Regression,
and Logistic Regression algorithms, while Hail ran Linear regression and Logistic regression. The novel
Random Forest algorithm in VariantSpark was ran and compared with the previous algorithms. The
softwares was evaluated based on SNP calling accuracy, RAM usage, and processing time. Overall,
PLINK’s algorithms performed the best in terms of SNP calling accuracy, RAM usage, and processing
time. Hail was comparable with PLINK in terms of RAM and processing time, but PLINK was more
accurate in detecting variants. VariantSpark used a lot more RAM and processing time for the same
performance as PLINK’s Simple Association algorithm. In conclusion, PLINK and Hail are great tools to
conduct GWAS Association Testing. More testing with different datasets is needed for more accurate
assessments of these tools.

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Availability

Detail Information

Series Title

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Call Number

BM 23-001

Publisher

i3L, Jakarta : i3L, Jakarta., 2023

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

GWAS
Late-onset Alzheimer’s (LOAD)
PLINK
Hail
VariantSpark

Specific Detail Info

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Statement of Responsibility

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