What Is a GWAS?

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GWAS 1: What is a GWAS? Matti Pirinen, University of Helsinki Last updated: March 5, 2025 This document is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The slide set referred to in this document is “GWAS 1”. This course is about statistical/computational ideas and methods that are applied in genome-wide association studies (GWAS). (Note that on these notes ‘GWAS’ is both singular and plural while other texts may use ‘GWASs’ or ‘GWASes’ for plural.) More generally, these same methods are useful for analyzing large data sets of many other types but, on this course, we will approach these methods from the angle of GWAS. A GWAS quantifies statistical association between genetic variation and phenotypes. A phenotype, also called a trait, can be any measured or observed property of an individual. Examples of phenotypes are quantitative traits like standing height or concentration of cholesterol particles in plasma, or binary traits like diagnoses of psoriasis or schizophrenia. Why do we do GWAS? (slides 2-7) We do GWAS because a statistical association between a particular physical region of the genome and the phenotype of interest • can point to biological mechanisms affecting the phenotype, • can allow prediction of the phenotype from genomic information. These results may further benefit • medicine by leading to molecular or environmental interventions against harmful phenotypes, • biotechnology by improving the ways we utilize microbes, plants or animals, • forensics by more accurate identification of an individual from a DNA sample, • biogeographic ancestry inference of individuals, populations and species, • our understanding of the role of natural selection and other evolutionary forces in the living world. The genome of an individual remains (nearly) constant across all the cells of the individual throughout the individual’s lifetime. This is a truly remarkable property compared to, for example, other molecular sources of information, such as metabolomics, metagenomics, transcriptomics, proteomics or epigenomics, or environmental factors that may vary widely across time. Therefore, the genome seems an ideal starting point for scientific research: It needs to be measured only once per individual and there is no reverse causation from the phenotype to genome, (with cancer cells giving an important exception to this rule). Ethical aspects As with any powerful technique, the utilization of results from GWAS also raises many new and challenging ethical questions and the legislation of utilization of genome information is under active development around the world. For example, we need concrete answers to 1

Original publication: opening pages

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Section 1 — What is a GWAS?

Section 2 — Statistics of GWAS (regression coefficients, P-values, statistical power, Bayes factors)

Section 3 — Genetic relatedness and population structure

Section 4 — Confounding and covariates in GWAS

Section 5 — Haplotypes, linkage disequilibrum, imputation, fine-mapping

Section 6 — Linear mixed models and heritability

Section 7 — Summary statistics and meta-analysis

Section 8 — Polygenic scores

Section 9 — Success and critisism of GWAS

Section 10 — Human genetics research at FIMM

• who can access genetic information of a particular individual and for which purpose: individual them- self?, researchers?, medical professionals?, insurance companies?, employers?, school system?, every- one? • what kind of information should be returned back to the individual: genetic risk for a disease for which some preventive measures exist vs. a disease with no actionable measures known?, genetic prediction of sensitive traits such as IQ?, genetic ancestry or family information that does not match prior expectations of the individual, for example, due to false paternity? • what kind of information, if any, should be returned to a relative of a tested individual given that the relative may also have some of the same genetic variants? • when is gene editing allowed: to cure severe disease?, to prevent a severe mutation to be passed on to next generation?, to design the offspring to have some favorable genetic variants? When working with human genome data, we should always keep it clear in mind that there are such profound questions related to these data, and that the data we handle will likely turn out to be more powerful than most of us can imagine today. Human genome data are never ‘just data’ but include highly personal information, and they need to be handled with the highest respect and care. Access to genetic data requires a written agreement between the researcher and the data provider about how and for which purpose the data can be used. Contents of this course (slides 32-33) The plan is to discuss the following topics (in varying level of detail): 1. What is a GWAS? 2. Statistics of GWAS (regression coefficients, P-values, statistical power, Bayes factors) 3. Genetic relatedness and population structure 4. Confounding and covariates in GWAS 5. Haplotypes, linkage disequilibrum, imputation, fine-mapping 6. Linear mixed models and heritability 7. Summary statistics and meta-analysis 8. Polygenic scores 9. Success and critisism of GWAS 10. Human genetics research at FIMM 1.1 Genetic variation (slides 8-13) We all carry two nuclear genomes, i.e. two copies of the human genome located in the cell nucleus, one copy inherited from each of our two parents. Additionally, we have a small mitochondrial genome inherited from the mother, but on this course the term ‘genome’ refers to the nuclear genome. Human genome is 3.2 billion nucleotides (or base pairs or DNA letters A,C,G,T) long sequence (see yourgenome.org), that is divided into separate physical pieces called chromosomes (see yourgenome.org). There are 22 autosomal (non-sex related) chromosomes and two sex chromosomes (X chromosome and Y chromosome). Normally, humans have two copies of each autosome and individuals with one copy of X and one of Y are biological males whereas individuals who have two copies of X are biological females. Abnormal number of autosomal chromosomes (called autosomal aneuploidies) typically cause severe consequences or an early death if present in all cells of an individual. The most common non-lethal exception is the Down syndrome (3 copies of chr 21). While autosomal aneuploidies are often lethal, there are several non-lethal sex 2