Population genetic, association and zygosity testing on preamplified DNA

Abstract

New advances in genetic epidemiological research have lead to the establishment of collaborative biobanks that can be used in large-scale population genetic and association studies. A less explored approach is to use the existing biological repositories, like the Swedish newborn screening registry (SNSR) for similar purposes. The SNSR encompasses about 3 million individual samples in the form of dried blood spots on filter paper. These samples represent all newborns in Sweden since 1975 and the repository grows by 100000 samples each year.

In this thesis I use improved primer extension preamplification (I-PEP-L) and multiple displacement amplification (MDA) to preamplify DNA from dried blood spots and other templates. The methods are used to 1) explore the population genetic substructure in Sweden and Finland, 2) test if variants in the neuropeptide s receptor 1 (NPSR1) are associated with an increased risk of respiratory distress syndrome (RDS), and 3) validate a panel of autosomal SNPs for zygosity testing and population genetics.

I show that up to 25-year-old dried blood spots can be used for genetic studies (Study 1) and that haplotypes in NPSR1 associate with an increased risk of RDS (Study 2). Zygosity testing based on 47 unlinked autosomal SNPs is robust and reliable in the presence of population substructure and missing data (Study 3). Historic connections with neighbouring countries, Central Europe and recent immigration in the big cities are evident in Sweden, as well as the presence of regional differences in genetic diversity (studies 4 and 5). The CCR5 ð€‡»32 variant associated with HIV immunity is more common in the northern parts of Sweden (Study 1). The Finnish substructure is characteristic of an east-west duality congruent with historic political and anthropological borders, and the Swedish speaking part of Ostrobothnia clusters with Sweden (Study 5).

In conclusion, I demonstrate how preamplification can assist in gaining access to samples that would otherwise be incompatible with genetic epidemiological research. Also, due to the possibility of allele dropouts and missing data, quality control should be of very high priority when using these methods.