On the other hand, we might uncover overlap regions of DNA which affect multiple disease risks simultaneously. How much genetic overlap exists between different disease architectures? With millions of SNPs in the genome it is entirely possible that different diseases have nearly disjoint genetic architectures – i.e., risk is mostly controlled by distinct regions of DNA. Is exome sequencing data sufficient for computation of Polygenic Risk Scores (PRS)? How much of the total risk is controlled by loci in coding vs non-coding regions? What is the (qualitative) genetic architecture of specific disease risks? How many SNPs, where are they, how many genes? In this paper we address the following questions: Adult height prediction from DNA (with 95 percent confidence interval roughly ☒ inches) will allow physicians to avoid expensive HGH treatment (with significant potential side-effects) for children who are merely short for their age (late-developing) and are likely to be in the normal range in adulthood.įor the first time, we can begin to address some general questions concerning the genetic architectures of complex traits. However, it is difficult for pediatric endocrinologists, whose responsibility it is to prescribe HGH for these children, to know whether the child is simply passing through a temporary phase of slow growth (and will, by adulthood, reach normal height). Typically, these would be children in the bottom percentiles for height within their age group. Growth hormone treatment is sometimes prescribed for children who are at risk for this condition, at a cost in the $100k range. Idiopathic Short Stature (ISS) refers to extreme short stature that does not have a diagnostic explanation (e.g., height below 5 foot 2 inches in adult males). There are many clinical applications for such predictors (although there is still much work to be done to overcome sampling and algorithmic biases and disparity ). That is, individuals with unusually high (or low) risk of a specific condition. In the case of disease risk, the predictors are already good enough to identify risk outliers. In contrast, traditional Genome Wide Association studies (GWAS) can implicate the entire genome, making them unwieldy to analyze. The genetic architectures uncovered vary significantly: the number of SNPs required to capture most of the predictor variance ranges from a few dozen to many thousands. Predictors (PGS or PRS) now exist for a number of important traits and risks, many of which have undergone out of sample testing (i.e., validation in groups of individuals not used in training and from other data sets or from separate ancestries.). They produce Polygenic Risk Scores (PRS) or Polygenic Scores (PGS): functions that map the state of an individual’s DNA at specific locations (SNPs), to a risk score or predicted quantitative trait value. These algorithms range from simple regression, applied to one SNP at a time to estimate statistical significance and effect size (e.g., as used in GWAS), to high dimensional optimization methods such as compressed sensing or sparse learning. Genomic prediction of complex traits and disease risks has advanced considerably thanks to the recent advent of large data sets and improved algorithms. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. We also study the fraction of SNPs and of variance that is in common between pairs of predictors. This suggests that exome data alone will miss much of the heritability for these traits – i.e., existing PRS cannot be computed from exome data alone. The state of these SNPs cannot be determined from exome-sequencing data. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. Predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. Genomic prediction of complex human traits (e.g., height, cognitive ability, bone density) and disease risks (e.g., breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years.
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