This dissertation develops statistical methods in genetics and with their application answers both old and new questions related to genetics, income and inequality. Chapter 2 develops a new method to support identification of causal effects in nonexperimental data. Additionally, a new method for estimating heritability using two polygenic indices (PGI) from independent genome-wide association studies (GWAS) is developed. In Chapter 3 this new heritability method is explored further and compared to the established and widely used method, genome-based restricted maximum likelihood (GREML). Chapter 4 aims to remove several barriers for researchers wanting to use PGI in their study. In this chapter a broad array of PGI are constructed, covering a wide range of phenotypes for a number of datasets used by social scientists. Furthermore, in this chapter a theoretical framework is introduced for interpreting associations with PGI. In Chapter 5, the first large scale GWAS on personal income is conducted, using data from the UK Biobank. It is shown that a higher PGI is linked to higher education and better health. Chapter 6 builds upon the results of the previous chapter and further investigates the genetic and environmental factors underlying socioeconomic and health inequality. A lower bound is estimated for the relevance of genetic factors and early-childhood environment for differences in education, income and body mass index. Chapter 7 presents the first results of an ongoing research project where the first large-scale GWAS meta-analysis on personal income is performed. The meta-analysis has a total sample size of 1,161,574 observations from approximately 756,000 individuals.
|Award date||15 Dec 2021|
|Place of Publication||s.l.|
|Publication status||Published - 15 Dec 2021|
- Genetics, Economics, Income, Inequality, GWAS, polygenic score, polygenic index