Biobank-scale methods and projections for sparse polygenic prediction from machine learning.

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

Polygenic Health Index, General Health, and Pleiotropy: Sibling Analysis and Disease Risk Reduction.

We construct a polygenic health index as a weighted sum of polygenic risk scores for 20 major disease conditions, including, e.g., coronary artery disease, type 1 and 2 diabetes, schizophrenia, etc. Individual weights are determined by population-level estimates of impact on life expectancy. We validate this index in odds ratios and selection experiments using unrelated individuals and siblings (pairs and trios) from the UK Biobank. Individuals with higher index scores have decreased disease risk across almost all 20 diseases (no significant risk increases), and longer calculated life expectancy. When estimated Disability Adjusted Life Years (DALYs) are used as the performance metric, the gain from selection among ten individuals (highest index score vs average) is found to be roughly 4 DALYs. We find no statistical evidence for antagonistic trade-offs in risk reduction across these diseases. Correlations between genetic disease risks are found to be mostly positive and generally mild. These results have important implications for public health and also for fundamental issues such as pleiotropy and genetic architecture of human disease conditions.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits.

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

Quantum Hair from Gravity.

We explore the relationship between the quantum state of a compact matter source and of its asymptotic graviton field. For a matter source in an energy eigenstate, the graviton state is determined at leading order by the energy eigenvalue. Insofar as there are no accidental energy degeneracies there is a one to one map between graviton states on the boundary of spacetime and the matter source states. Effective field theory allows us to compute a purely quantum gravitational effect which causes the subleading asymptotic behavior of the graviton state to depend on the internal structure of the source. This establishes the existence of ubiquitous quantum hair due to gravitational effects.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank.

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Lithography-defect-driven source-mask optimization solution for full-chip optical proximity correction.

In the domain of computational lithography, the performance of an optimized imaging solution is usually qualified with a full-chip posted-optical-proximity-correction lithography printing check to ensure the printing is defect free before committed for mask writing. It is thus highly preferable for the optimization process itself to be driven by the same defect detection mechanism towards a defect-free solution. On the other hand, the huge data size of chip layout poses great challenge to such optimization process, in terms of runtime and data storage. A gradient-based optimization scheme thus becomes necessary. To date, no successful engineering tool is capable of accommodating these two requirements at the same time. We demonstrate the technology of defect-driven gradient-based optimization to achieve a defect-free solution within practical runtime specification, using ASML's computational lithography product Tachyon SMO.

Sibling validation of polygenic risk scores and complex trait prediction.

We test 26 polygenic predictors using tens of thousands of genetic siblings from the UK Biobank (UKB), for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in between-sibling designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (< 84 percentile, < + 1 SD) and 1 sibling with high PRS score (top few percentiles, i.e. > + 2 SD), the predictors identify the affected sibling about 70-90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. 55-65% of the time the higher PRS sibling is the case. For quantitative traits such as height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.

Genetic architecture of complex traits and disease risk predictors.

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. Using data from the UK Biobank, predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. The state of these SNPs cannot be determined from exome-sequencing data. 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. We also study the fraction of SNPs and of variance that is in common between pairs of predictors. 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. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously.

Author Correction: Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.

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.

Accurate Genomic Prediction of Human Height.

We construct genomic predictors for heritable but extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). The constructed predictors explain, respectively, ∼40, 20, and 9% of total variance for the three traits, in data not used for training. For example, predicted heights correlate ∼0.65 with actual height; actual heights of most individuals in validation samples are within a few centimeters of the prediction. The proportion of variance explained for height is comparable to the estimated common SNP heritability from genome-wide complex trait analysis (GCTA), and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for SNPs. Thus, our results close the gap between prediction R-squared and common SNP heritability. The ∼20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common variants. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier genome-wide association studies (GWAS) for out-of-sample validation of our results.

Determination of nonlinear genetic architecture using compressed sensing.

BACKGROUND: One of the fundamental problems of modern genomics is to extract the genetic architecture of a complex trait from a data set of individual genotypes and trait values. Establishing this important connection between genotype and phenotype is complicated by the large number of candidate genes, the potentially large number of causal loci, and the likely presence of some nonlinear interactions between different genes. Compressed Sensing methods obtain solutions to under-constrained systems of linear equations. These methods can be applied to the problem of determining the best model relating genotype to phenotype, and generally deliver better performance than simply regressing the phenotype against each genetic variant, one at a time. We introduce a Compressed Sensing method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. Our method uses L1-penalized regression applied to nonlinear functions of the sensing matrix. RESULTS: The computational and data resource requirements for our method are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using simulated human genomes and the small amount of currently available real data. A phase transition (i.e., dramatic and qualitative change) in the behavior of the algorithm indicates when sufficient data is available for its successful application. CONCLUSION: Our results indicate that predictive models for many complex traits, including a variety of human disease susceptibilities (e.g., with additive heritability h (2)∼0.5), can be extracted from data sets comprised of n ⋆∼100s individuals, where s is the number of distinct causal variants influencing the trait. For example, given a trait controlled by ∼10 k loci, roughly a million individuals would be sufficient for application of the method.

Applying compressed sensing to genome-wide association studies.

BACKGROUND: The aim of a genome-wide association study (GWAS) is to isolate DNA markers for variants affecting phenotypes of interest. This is constrained by the fact that the number of markers often far exceeds the number of samples. Compressed sensing (CS) is a body of theory regarding signal recovery when the number of predictor variables (i.e., genotyped markers) exceeds the sample size. Its applicability to GWAS has not been investigated. RESULTS: Using CS theory, we show that all markers with nonzero coefficients can be identified (selected) using an efficient algorithm, provided that they are sufficiently few in number (sparse) relative to sample size. For heritability equal to one (h (2) = 1), there is a sharp phase transition from poor performance to complete selection as the sample size is increased. For heritability below one, complete selection still occurs, but the transition is smoothed. We find for h (2) ∼ 0.5 that a sample size of approximately thirty times the number of markers with nonzero coefficients is sufficient for full selection. This boundary is only weakly dependent on the number of genotyped markers. CONCLUSION: Practical measures of signal recovery are robust to linkage disequilibrium between a true causal variant and markers residing in the same genomic region. Given a limited sample size, it is possible to discover a phase transition by increasing the penalization; in this case a subset of the support may be recovered. Applying this approach to the GWAS analysis of height, we show that 70-100% of the selected markers are strongly correlated with height-associated markers identified by the GIANT Consortium.

Inhibition of herpes simplex virus type 1 with the modified green tea polyphenol palmitoyl-epigallocatechin gallate.

Green tea polyphenol epigallocatechin gallate (EGCG) is a strong antioxidant that has previously been shown to reduce the number of plaques in HIV-infected cultured cells. Modified EGCG, palmitoyl-EGCG (p-EGCG), is of interest as a topical antiviral agent for herpes simplex virus (HSV-1) infections. This study evaluated the effect of p-EGCG on HSV-infected Vero cells. Results of cell viability and cell proliferation assays indicate that p-EGCG is not toxic to cultured Vero cells and show that modification of the green tea polyphenol epigallocatechin gallate (EGCG) with palmitate increases the effectiveness of EGCG as an antiviral agent. Furthermore, p-EGCG is a more potent inhibitor of herpes simplex virus 1 (HSV-1) than EGCG and can be topically applied to skin, one of the primary tissues infected by HSV. Viral binding assay, plaque forming assay, PCR, real-time PCR, and fluorescence microscopy were used to demonstrate that p-EGCG concentrations of 50 µM and higher block the production of infectious HSV-1 particles. p-EGCG was found to inhibit HSV-1 adsorption to Vero cells. Thus, p-EGCG may provide a novel treatment for HSV-1 infections.

Ni(II) ions dysregulate cytokine secretion from human monocytes.

Nickel-containing alloys are used in dentistry because of their low cost, but poor corrosion behavior increases their risk of causing adverse biological responses. Intraorally, nickel-containing alloys accumulate bacterial plaque that triggers periodontal inflammation via toxins such as lipopolysaccharide (LPS). Recent evidence suggests that in monocytes, Ni(II) amplifies LPS-induced secretion of several cytokines that mediate periodontal destruction. Thus, we investigated the effects of Ni(II), with or without LPS, on the secretion of a broader array of cytokines from monocytes. We then measured monocytic expression of two proteins, Nrf2 and thioredoxin-1 (Trx1), that influence the regulation of cytokine secretion. Cytokine arrays were used to measure the effects of 0-50 microM Ni(II) on cytokine secretion from human THP1 monocytes, with or without LPS activation. Immunoblots were used to estimate Nrf2 and Trx1 levels. Our results indicate that both Ni(II) alone and Ni(II) with LPS have broad-based effects on cytokine secretion. Ni(II) increased Nrf2 levels by threefold, and LPS amplified the effects of Ni(II) by 10-fold. Trx1 levels did not change under any condition tested. Our results suggest that Ni(II)-induced changes in cytokine secretion by monocytes are diverse and may be influenced by Nrf2 but are not likely influenced by changes in whole-cell Trx1 levels.

Grand unification and enhanced quantum gravitational effects.

In grand unified theories with large numbers of fields, renormalization effects significantly modify the scale at which quantum gravity becomes strong. This in turn can modify the boundary conditions for coupling constant unification, if higher dimensional operators induced by gravity are taken into consideration. We show that the generic size of, and the uncertainty in, these effects from gravity can be larger than the two-loop corrections typically considered in renormalization group analyses of unification. In some cases, gravitational effects of modest size can render unification impossible.

Effects of oral consumption of the green tea polyphenol EGCG in a murine model for human Sjogren's syndrome, an autoimmune disease.

SIGNIFICANCE: Protection of glandular cells from autoimmune-induced damage would be of significant clinical benefit to Sjogren's syndrome (SS) patients. Epigallocatechin-3-gallate (EGCG) possesses anti-apoptotic, anti-inflammatory, and autoantigen-inhibitory properties. AIMS: To investigate if EGCG protects against certain autoimmune-induced pathological changes in the salivary glands of the non-obese diabetic (NOD) mouse model for SS. MAIN METHODS: Animals were provided with either water or water containing 0.2% EGCG. At the age of 8, 16 and 22 weeks, submandibular salivary gland tissue and serum samples were collected for pathological and serological analysis. KEY FINDINGS: Significant lymphocyte infiltration was observed in the salivary glands of the water-fed group at the age of 16 weeks, while the EGCG group showed reduced lymphocyte infiltration. By 22 weeks of age, water-fed animals demonstrated elevated levels of apoptotic activity within the lymphocytic infiltrates, and high levels of serum total anti-nuclear antibody, compared to EGCG-fed animals. Remarkably, proliferating cell nuclear antigen (PCNA) and Ki-67 levels in the salivary glands of water-fed NOD mice were significantly elevated in comparison to BALB/c control mice; in contrast, PCNA and Ki-67 levels in EGCG-fed NOD animals were similar to BALB/c mice. These results indicate that EGCG protects the NOD mouse submandibular glands from autoimmune-induced inflammation, and reduces serum autoantibody levels. Abnormal proliferation, rather than apoptosis, appears to be a characteristic of the NOD mouse gland that is normalized by EGCG. The evidence suggests that EGCG could be useful in delaying or managing SS-like autoimmune disorders.

Green tea polyphenols reduce autoimmune symptoms in a murine model for human Sjogren's syndrome and protect human salivary acinar cells from TNF-alpha-induced cytotoxicity.

Sjogren's syndrome (SS) is a relatively common autoimmune disorder. A key feature of SS is lymphocytic infiltration of the salivary and lacrimal glands, associated with the destruction of secretory functions of these glands. Current treatment of SS targets the symptoms but is unable to reduce or prevent the damage to the glands. We reported previously that the major green tea polyphenol (GTP) epigallocatechin-3-gallate (EGCG) inhibits autoantigen expression in normal human keratinocytes and immortalized normal human salivary acinar cells (Hsu et al. 2005). However, it is not known whether GTPs have this effect in vivo, if they can reduce lymphocytic infiltration, or protect salivary acinar cells from tumor necrosis factor-alpha (TNF-alpha)-induced cytotoxicity. Here, we demonstrate that in the NOD mouse, a model for human SS, oral administration of green tea extract reduced the serum total autoantibody levels and the autoimmune-induced lymphocytic infiltration of the submandibular glands. Further, we show that EGCG protected normal human salivary acinar cells from TNF-alpha-induced cytotoxicity. This protection was associated with specific phosphorylation of p38 MAPK, and inhibitors of the p38 MAPK pathway blocked the protective effect. In conclusion, GTPs may provide a degree of protection against autoimmune-induced tissue damage in SS, mediated in part through activation of MAPK elements.

Ni(II) activates the Nrf2 signaling pathway in human monocytic cells.

Nickel is a component of biomedical alloys that is released during corrosion and causes inflammation in tissues by as yet unknown mechanisms. Recent data show that Ni(II) at concentrations of 10-50 microM amplifies lipopolysaccharide-triggered, NFkappaB-mediated cytokine secretion from monocytes. In the current study, we tested the hypothesis that Ni(II) amplifies cytokine secretion by activating the Nrf2 antioxidant pathway rather than by enhancing activity of the NFkappaB signaling pathway. Human THP1 monocytes were exposed to Ni(II) concentrations of 10-30 microM for 6-72 h, then immunoblots of whole-cell lysates or cytosolic and nuclear proteins were used to detect changes in Nrf2 or NFkappaB signaling. Our results show that Ni(II) increased (by 1-2 fold) whole-cell Nrf2 levels and nuclear translocation of Nrf2, and amplified lipopolysaccharide (LPS)-induction of Nrf2 (by 3-5 fold), but had no detectable effect on the initial activation or nuclear translocation of NFkappaB. Because Nrf2 target gene products are known regulators of NFkappaB nuclear activity, our results suggest that Ni(II) may affect cytokine secretion indirectly via modulation of the Nrf2 pathway.