@article {141,
	title = {Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.},
	journal = {Am J Hum Genet},
	volume = {107},
	year = {2020},
	month = {2020 07 02},
	pages = {46-59},
	abstract = {<p>In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait{\textquoteright}s heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.</p>},
	keywords = {Aged, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci},
	issn = {1537-6605},
	doi = {10.1016/j.ajhg.2020.05.004},
	author = {Chun, Sung and Imakaev, Maxim and Hui, Daniel and Patsopoulos, Nikolaos A and Neale, Benjamin M and Kathiresan, Sekar and Stitziel, Nathan O and Sunyaev, Shamil R}
}
@article {29,
	title = {Genetic association studies in cardiovascular diseases: Do we have enough power?},
	journal = {Trends Cardiovasc Med},
	volume = {27},
	year = {2017},
	month = {2017 Aug},
	pages = {397-404},
	abstract = {<p>Genetic association studies have a long history of delivering insightful results for cardiovascular disease (CVD) research. Beginning with early candidate gene studies, to genome-wide association studies, and now on to newer whole-genome sequencing studies, research in human genetics has enriched our understanding of the pathobiology of CVD. As these studies continue to expand, the issue of statistical power plays an important role in study design as well as the interpretation of results. We provide an overview of the component parts that determine statistical power and preview the future of CVD genetic association studies through this lens.</p>},
	keywords = {Cardiovascular Diseases, Data Accuracy, Data Interpretation, Statistical, Genetic Association Studies, Genetic Markers, Genetic Predisposition to Disease, Genetic Variation, Humans, Phenotype, Reproducibility of Results, Research Design, Risk Assessment, Risk Factors},
	issn = {1873-2615},
	doi = {10.1016/j.tcm.2017.03.005},
	author = {Auer, Paul L and Stitziel, Nathan O}
}
