@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 = {

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.

}, 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 {38, title = {Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Apr 24}, pages = {1613}, abstract = {

Less than 3\% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5\%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.

}, keywords = {Databases, Genetic, Diabetes Mellitus, Type 2, Disease, Gene Frequency, Genetic Testing, Genetic Variation, Humans, Obesity, Phenotype, Proteins, Respiratory Hypersensitivity, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-018-03911-8}, author = {Emdin, Connor A and Khera, Amit V and Chaffin, Mark and Klarin, Derek and Natarajan, Pradeep and Aragam, Krishna and Haas, Mary and Bick, Alexander and Zekavat, Seyedeh M and Nomura, Akihiro and Ardissino, Diego and Wilson, James G and Schunkert, Heribert and McPherson, Ruth and Watkins, Hugh and Elosua, Roberto and Bown, Matthew J and Samani, Nilesh J and Baber, Usman and Erdmann, Jeanette and Gupta, Namrata and Danesh, John and Chasman, Daniel and Ridker, Paul and Denny, Joshua and Bastarache, Lisa and Lichtman, Judith H and D{\textquoteright}Onofrio, Gail and Mattera, Jennifer and Spertus, John A and Sheu, Wayne H-H and Taylor, Kent D and Psaty, Bruce M and Rich, Stephen S and Post, Wendy and Rotter, Jerome I and Chen, Yii-Der Ida and Krumholz, Harlan and Saleheen, Danish and Gabriel, Stacey and Kathiresan, Sekar} } @article {27, title = {Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms.}, journal = {Cell}, volume = {170}, year = {2017}, month = {2017 Jun 29}, pages = {199-212.e20}, abstract = {

Type 2 diabetes (T2D) affects Latinos at twice the~rate~seen in populations of European descent. We~recently identified a risk haplotype spanning SLC16A11 that explains \~{}20\% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at~this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. VIDEO ABSTRACT.

}, keywords = {Basigin, Cell Membrane, Chromosomes, Human, Pair 17, Diabetes Mellitus, Type 2, Gene Knockdown Techniques, Haplotypes, Hepatocytes, Heterozygote, Histone Code, Humans, Liver, Models, Molecular, Monocarboxylic Acid Transporters}, issn = {1097-4172}, doi = {10.1016/j.cell.2017.06.011}, author = {Rusu, Victor and Hoch, Eitan and Mercader, Josep M and Tenen, Danielle E and Gymrek, Melissa and Hartigan, Christina R and DeRan, Michael and von Grotthuss, Marcin and Fontanillas, Pierre and Spooner, Alexandra and Guzman, Gaelen and Deik, Amy A and Pierce, Kerry A and Dennis, Courtney and Clish, Clary B and Carr, Steven A and Wagner, Bridget K and Schenone, Monica and Ng, Maggie C Y and Chen, Brian H and Centeno-Cruz, Federico and Zerrweck, Carlos and Orozco, Lorena and Altshuler, David M and Schreiber, Stuart L and Florez, Jose C and Jacobs, Suzanne B R and Lander, Eric S} }