| dc.date.accessioned |
2020-05-06T08:38:52Z |
|
| dc.date.available |
2020-05-06T08:38:52Z |
|
| dc.date.issued |
2020-05-06 |
|
| dc.identifier.uri |
http://hdl.handle.net/123456789/28250 |
|
| dc.title |
Comparing the performance of the gene prioritization methods DEPICT and MAGMA on genome-wide association studies of schizophrenia using the Benchmarker framework |
en |
| ethesis.discipline |
none |
und |
| ethesis.department |
none |
und |
| ethesis.faculty |
Matemaattis-luonnontieteellinen tiedekunta |
fi |
| ethesis.faculty |
Faculty of Science |
en |
| ethesis.faculty |
Matematisk-naturvetenskapliga fakulteten |
sv |
| ethesis.faculty.URI |
http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca |
|
| ethesis.university.URI |
http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97 |
|
| ethesis.university |
Helsingin yliopisto |
fi |
| ethesis.university |
University of Helsinki |
en |
| ethesis.university |
Helsingfors universitet |
sv |
| dct.creator |
Ottensmann, Linda |
|
| dct.issued |
2020 |
|
| dct.language.ISO639-2 |
eng |
|
| dct.abstract |
It is challenging to identify causal genes and pathways explaining the associations with diseases and traits found by genome-wide association studies (GWASs). To solve this problem, a variety of methods that prioritize genes based on the variants identified by GWASs have been developed. In this thesis, the methods Data-driven Expression Prioritized Integration for Complex Traits (DEPICT) and Multi-marker Analysis of GenoMic Annotation (MAGMA) are used to prioritize causal genes based on the most recently published publicly available schizophrenia GWAS summary statistics. The two methods are compared using the Benchmarker framework, which allows an unbiased comparison of gene prioritization methods.
The study has four aims. Firstly, to explain what are the differences between the gene prioritization methods DEPICT and MAGMA and how the two methods work. Secondly, to explain how the Benchmarker framework can be used to compare gene prioritization methods in an unbiased way. Thirdly, to compare the performance of DEPICT and MAGMA in prioritizing genes based on the latest schizophrenia summary statistics from 2018 using the Benchmarker framework. Lastly, to compare the performance of DEPICT and MAGMA on a schizophrenia GWAS with a smaller sample size by using Benchmarker.
Firstly, the published results of the Benchmarker analyses using schizophrenia GWAS from 2014 were replicated to make sure that the framework is run correctly. The results were very similar and both the original and the replicated results show that DEPICT and MAGMA do not perform significantly differently. Furthermore, they show that the intersection of genes prioritized by DEPICT and MAGMA outperforms the outersection, which is defined as genes prioritized by only one of these methods. Secondly, Benchmarker was used to compare the performance of DEPICT and MAGMA on prioritizing genes using the schizophrenia GWAS from 2018. The results of the Benchmarker analyses suggest that DEPICT and MAGMA perform similarly with the GWAS from 2018 compared to the GWAS from 2014. Furthermore, an earlier schizophrenia GWAS from 2011 was used to check if the performance of DEPICT and MAGMA differs when a GWAS with lower statistical power is used. The results of the Benchmarker analyses make clear that MAGMA performs better than DEPICT in prioritizing genes using this smaller data set. Furthermore, for the schizophrenia GWAS from 2011 the outersection of genes prioritized by DEPICT and MAGMA outperforms the intersection.
To conclude, the Benchmarker framework is a useful tool for comparing gene prioritization methods in an unbiased way. For the most recently published schizophrenia GWAS from 2018 there is no significant difference between the performance of DEPICT and MAGMA in prioritizing genes according to Benchmarker. For the smaller schizophrenia GWAS from 2011, however, MAGMA outperformed DEPICT. |
en |
| dct.subject |
Genome-wide association study |
|
| dct.subject |
Schizophrenia |
|
| dct.subject |
Cross-validation |
|
| dct.subject |
Linkage disequilibrium |
|
| dct.subject |
Heritability |
|
| dct.language |
en |
|
| ethesis.isPublicationLicenseAccepted |
true |
|
| ethesis.language.URI |
http://data.hulib.helsinki.fi/id/languages/eng |
|
| ethesis.language |
English |
en |
| ethesis.language |
englanti |
fi |
| ethesis.language |
engelska |
sv |
| ethesis.thesistype |
pro gradu -tutkielmat |
fi |
| ethesis.thesistype |
master's thesis |
en |
| ethesis.thesistype |
pro gradu-avhandlingar |
sv |
| ethesis.thesistype.URI |
http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis |
|
| dct.identifier.ethesis |
E-thesisID:67bbb54c-5ece-449f-823c-92c5fd5144cf |
|
| dct.identifier.urn |
URN:NBN:fi:hulib-202005062017 |
|
| dc.type.dcmitype |
Text |
|
| ethesis.facultystudyline |
Biostatistics and Bioinformatics |
fi |
| ethesis.facultystudyline |
Biostatistics and Bioinformatics |
en |
| ethesis.facultystudyline |
Biostatistics and Bioinformatics |
sv |
| ethesis.facultystudyline.URI |
http://data.hulib.helsinki.fi/id/SH50_134 |
|
| ethesis.mastersdegreeprogram |
Life Science Informatics -maisteriohjelma |
fi |
| ethesis.mastersdegreeprogram |
Master's Programme in Life Science Informatics |
en |
| ethesis.mastersdegreeprogram |
Magisterprogrammet i Life Science Informatics |
sv |
| ethesis.mastersdegreeprogram.URI |
http://data.hulib.helsinki.fi/id/MH50_002 |
|
