Large-scale genome-wide association studies are providing an unprecedented amount of information to understand the complex dynamics underlying genotype-phenotype associations. This session will showcase novel methods and results regarding the impact of ascertainment, phenotyping, and population structure on gene discovery and polygenic characterization of human traits and diseases. The first speaker will explain the impact of participation bias and non-response behaviors on the interpretation and the generalizability of large-scale genome-wide association studies. His analyses demonstrate that these biases are genetically correlated with education, health, and income although unique genetic effects were also observed. Next, the second speaker will show how assortative mating due to socioeconomic status affects the polygenic architecture of psychiatric traits and disorders assessed in the UK Biobank. Her research underlines that socioeconomic status should be carefully modeled when investigating the polygenic architecture of phenotypes related to mental health. Our third speaker will present the consistent correlation of year of birth with polygenic scores for different health conditions and quantitative traits in participants recruited by multiple biobanks within the PsycheMERGE network. Her findings highlight that this phenomenon is likely due to a combination of factors, such as differences in population structure by birth cohort and changing ascertainment bias within the medical system over time. Finally, our fourth speaker will introduce a novel method to assess ascertainment biases through the analysis of individual-level array-genotyped data and genome-wide association statistics. Specifically, he will show how comparing the empirical distribution (mainly statistical moments) of multivariate distributions of polygenic scores with that expected for a randomly ascertained sample can provide evidence of different types of ascertainment.