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Browsing by Subject "Heritability"

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  • Comparing the performance of the gene prioritization methods DEPICT and MAGMA on genome-wide association studies of schizophrenia using the Benchmarker framework 
    Ottensmann, Linda (2020)
    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.
  • Sex-specific autosomal genetic architecture of human complex traits 
    Lehtonen, Leevi (2021)
    Sex differences can be found in most human phenotypes, and they play an important role in human health and disease. Females and males have different sex chromosomes, which are known to cause sex differences, as are differences in the concentration of sex hormones such as testosterone, estradiol and progesterone. However, the role of the autosomes has remained more debated. The primary aim of this thesis is to assess the magnitude and relevance of human sex-specific genetic architecture in the autosomes. This is done by calculating sex-specific heritability estimates and genetic correlation estimates between females and males, as well as comparing these to sex differences on the phenotype level. Additionally, the heritability and genetic correlation estimates are compared between two populations, in order to assess the magnitude of sex differences compared to differences between populations. The analyses in this thesis are based on sex-stratified genome-wide association study (GWAS) data from 48 phenotypes in the UK Biobank (UKB), which contains genotype data from approximately 500 000 individuals as well as thousands of phenotype measurements. A replication of the analyses using three phenotypes was also made on data from the FinnGen project, with a dataset from approximately 175 000 individuals. The 48 phenotypes used in this study range from biomarkers such as serum testosterone and albumin levels to general traits such as height and blood pressure. The heritability and genetic correlation estimates were calculated using linkage disequilibrium score regression (LDSC). LDSC fits a linear regression model between test statistic values of GWAS variants and linkage disequilibrium (LD) scores calculated from a reference population. For most phenotypes, the heritability and genetic correlation results show little evidence of sex differences. Serum testosterone level and waist-to-hip ratio are exceptions to this, showing strong evidence of sex differences both on the genetic and the phenotype level. However, the overall correlation between phenotype level sex differences and sex differences in heritability or genetic correlation estimates is low. The replication in the FinnGen dataset for height, weight and body mass index (BMI), showed that for these traits the differences in heritability estimates and genetic correlations between the Finnish and UK populations are comparable or larger than the differences found between males and females.

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