We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.
Compressed sensing and high-dimensional statistical methods in complex trait genomics
Our goal is to test new computational methods for determining the genetic architecture of complex traits, including highly heritable conditions such as Type 1 Diabetes, Alzheimer's, and others. The techniques we plan to use have been the subject of intense recent activity in fields such as optimization, signal processing and machine learning, but so far have just begun to be applied in genomics. The research will produce improved predictive models which, based on individual genomics, identify individuals at high risk for certain diseases. It will also identify the many alleles associated with this risk. Early intervention with high risk individuals may decrease rates of incidence and reduce health care costs. Elaboration of underlying genetic architecture is important basic science and may lead to improved treatments (e.g., drug development). We wish to obtain access to genomic data and phenotype data relevant to highly heritable disease conditions (e.g., Type 1 Diabetes) as well as complex traits such as height, BMI, cognitive ability. Advanced computational algorithms will be used to study the genetic architecture of these traits. The techniques we plan to use have been the subject of intense recent activity in fields such as optimization, signal processing and machine learning, but so far have just begun to be applied in genomics. Analysis will be performed on high-performance computing clusters. We would like access to the full cohort (SNP genotypes), and several relevant phenotypes.
|Lead investigator:||Stephen Hsu|
|Lead institution:||Michigan State University|