netCRS: Network-based comorbidity risk score for prediction of myocardial infarction using biobank-scaled PheWAS data

The polygenic risk score (PRS) can help to identify individuals' genetic susceptibility for various diseases by combining patient genetic profiles and identified single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Although multiple diseases will usually afflict patients at once or in succession, conventional PRSs fail to consider genetic relationships across multiple diseases. Even multi-trait PRSs, which take into account genetic effects for more than one disease at a time, fail to consider a sufficient number of phenotypes to accurately reflect the state of disease comorbidity in a patient, or are biased in terms of the traits that are selected. Thus, we developed novel network-based comorbidity risk scores to quantify associations among multiple phenotypes from phenome-wide association studies (PheWAS). We first constructed a disease-SNP heterogeneous multi-layered network (DS-Net), which consists of a disease network (disease-layer) and SNP network (SNP-layer). The disease-layer describes the population-level interactome from PheWAS data. The SNP-layer was constructed according to linkage disequilibrium. Both layers were attached to transform the information from a population-level interactome to individual-level inferences. Then, graph-based semi-supervised learning was applied to predict possible comorbidity scores on disease-layer for each subject. The SNP-layer serves as receiving individual genotyping data in the scoring process, and the disease-layer serves as the propagated output for an individual's multiple disease comorbidity scores. The possible comorbidity scores were combined by logistic regression, and it is denoted as netCRS. The DS-Net was constructed from UK Biobank PheWAS data, and the individual genetic profiles were collected from the Penn Medicine Biobank. As a proof-of-concept study, myocardial infarction (MI) was selected to compare netCRS with the PRS with pruning and thresholding (PRS-PT). The combined model (netCRS + PRS-PT + covariates) achieved an AUC improvement of 6.26% compared to the (PRS-PT + covariates) model. In terms of risk stratification, the combined model was able to capture the risk of MI up to approximately eight-fold higher than that of the low-risk group. The netCRS and PRS-PT complement each other in predicting high-risk groups of patients with MI. We expect that using these risk prediction models will allow for the development of prevention strategies and reduction of MI morbidity and mortality.

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Pharmacogenetic Polygenic Risk Score for Bronchodilator Response in Children and Adolescents with Asthma: Proof-of-Concept

Journal of Personalized Medicine ◽

10.3390/jpm11040319 ◽

2021 ◽

Vol 11 (4) ◽

pp. 319

Author(s):

Joanne E. Sordillo ◽

Sharon M. Lutz ◽

Michael J. McGeachie ◽

Jessica Lasky-Su ◽

Scott T. Weiss ◽

...

Keyword(s):

Standard Deviation ◽

Risk Score ◽

Association Studies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Linear Regression Models ◽

Standard Deviation Increase ◽

Polygenic Risk ◽

Bronchodilator Response ◽

Children With Asthma

Genome-wide association studies (GWAS) of response to asthma medications have primarily focused on Caucasian populations, with findings that may not be generalizable to minority populations. We derived a polygenic risk score (PRS) for response to albuterol as measured by bronchodilator response (BDR), and examined the PRS in a cohort of Hispanic school-aged children with asthma. We leveraged a published GWAS of BDR to identify relevant genetic variants, and ranked the top variants according to their Combined Annotation Dependent Depletion (CADD) scores. Variants with CADD scores greater than 10 were used to compute the PRS. Once we derived the PRS, we determined the association of the PRS with BDR in a cohort of Hispanic children with asthma (the Genetics of Asthma in Costa Rica Study (GACRS)) in adjusted linear regression models. Mean BDR in GACRS participants was5.6% with a standard deviation of 10.2%. We observed a 0.63% decrease in BDR in response to albuterol for a standard deviation increase in the PRS (p = 0.05). We also observed decreased odds of a BDR response at or above the 12% threshold for a one standard deviation increase in the PRS (OR = 0.80 (95% CI 0.67 to 0.95)). Our findings show that combining variants from a pharmacogenetic GWAS into a PRS may be useful for predicting medication response in asthma.

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Distinct Genetic Profiles in Postpartum Depression With Different Trajectory of Illness

10.21203/rs.3.rs-29168/v1 ◽

2020 ◽

Author(s):

Nagahide Takahashi ◽

Hanae Tainaka ◽

Tomoko Nishimura ◽

Taeko Harada ◽

Akemi Okumura ◽

...

Keyword(s):

Bipolar Disorder ◽

Postpartum Depression ◽

Risk Score ◽

Association Studies ◽

Depression Scale ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Gene Set Enrichment ◽

Polygenic Risk ◽

Gene Set

Abstract BackgroundPostpartum depression (PPD) is a common and highly heritabledisorder in the postnatal period of new mothers. The development of PPD is shown to affectneurodevelopment in children and recent evidence suggests thatthe trajectory of PPDisalso associated with children’s neurodevelopment and mental conditions. Thus, early identification and intervention for individuals at high risk of PPD are urgently needed.Additionally, it is not clear whether genetic factors affect thetrajectory of PPD. Therefore, using a polygenic risk score (PRS) approach, we investigated if PRS for depression (Depression-PRS) and bipolar disorder (Bipolar-PRS) are associated with the development and clinical course of PPD.Methods Usingrecent large genome-wide association studies(GWAS) of depression and bipolar disorder as discovery cohorts, we calculatedDepression-PRS and Bipolar-PRS in each individual. Then, we investigated the possible association between Depression-PRS and Bipolar-PRS with the development andtrajectory of PPD insubjects from the Hamamatsu Birth Cohort for mothers and children (n = 136). Depressive symptoms were assessed using the Edinburgh Postpartum Depression Scale. Gene-set enrichment analyses were used to identify pathways underlying these conditions. ResultsDepression-PRS was significantly higher in subjects with PPD than in those without PPD(t = -3.283, P = 0.002)and logistic analysis showed that Depression-PRS significantly increases therisk of developing PPD(OR [SE] = 2.274 [0.585], P = 0.002). Furthermore, Depression-PRS was positively associated with continuity of PPD (β [SE]=1.621 [0.672]; P = 0.032).Gene-set enrichment analyses revealed that pathways such as“response to hormone”(β[SE] -2.285[1.002], P < 0.001) and “epigenetic regulation”(β[SE] 2.831 [1.317], P < 0.001) were involved in the continuity of PPD. ConclusionThese preliminary findings indicate that the genetic component plays an important role not only in the development but also inthe continuity of PPD. A polygenic risk score approach could be useful to identify subjects at risk for PPD, especially for persistent PPD,who needcareful monitoring and intervention after delivery.

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Performance of polygenic risk scores for cancer prediction in an academic biobank.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.1528 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 1528-1528

Author(s):

Heena Desai ◽

Anh Le ◽

Ryan Hausler ◽

Shefali Verma ◽

Anurag Verma ◽

...

Keyword(s):

Risk Score ◽

Genetic Variants ◽

Association Studies ◽

Risk Scores ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

European Americans ◽

Genome Wide ◽

Common Genetic Variants

1528 Background: The discovery of rare genetic variants associated with cancer have a tremendous impact on reducing cancer morbidity and mortality when identified; however, rare variants are found in less than 5% of cancer patients. Genome wide association studies (GWAS) have identified hundreds of common genetic variants significantly associated with a number of cancers, but the clinical utility of individual variants or a polygenic risk score (PRS) derived from multiple variants is still unclear. Methods: We tested the ability of polygenic risk score (PRS) models developed from genome-wide significant variants to differentiate cases versus controls in the Penn Medicine Biobank. Cases for 15 different cancers and cancer-free controls were identified using electronic health record billing codes for 11,524 European American and 5,994 African American individuals from the Penn Medicine Biobank. Results: The discriminatory ability of the 15 PRS models to distinguish their respective cancer cases versus controls ranged from 0.68-0.79 in European Americans and 0.74-0.93 in African Americans. Seven of the 15 cancer PRS trended towards an association with their cancer at a p<0.05 (Table), and PRS for prostate, thyroid and melanoma were significantly associated with their cancers at a bonferroni corrected p<0.003 with OR 1.3-1.6 in European Americans. Conclusions: Our data demonstrate that common variants with significant associations from GWAS studies can distinguish cancer cases versus controls for some cancers in an unselected biobank population. Given the small effects, future studies are needed to determine how best to incorporate PRS with other risk factors in the precision prediction of cancer risk. [Table: see text]

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Genomic Prediction of Depression Risk and Resilience Under Stress

10.1101/599456 ◽

2019 ◽

Author(s):

Yu Fang ◽

Laura Scott ◽

Peter Song ◽

Margit Burmeister ◽

Srijan Sen

Keyword(s):

Risk Score ◽

Large Scale ◽

Association Studies ◽

Health Study ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Risk And Resilience ◽

Polygenic Risk ◽

Response To Stress ◽

Training Stress

AbstractAdvancing our ability to predict who is likely to develop depression in response to stress holds great potential in reducing the burden of the disorder. Large-scale genome-wide association studies (GWAS) of depression have, for the first time, provided a basis for meaningful depression polygenic risk score construction (MDD-PRS). The Intern Health Study utilizes the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the MDD-PRS derived from the PGC2/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.082, p=2.1×10−12) and that MDD-PRS was significantly more strongly associated with depression under stress than at baseline (MDD-PRS × stress interaction - beta=0.029, p=0.02). While known risk factors accounted for 85.6% of the association between MDD-PRS and depression at baseline, they only accounted for 55.4% of the association between MDD-PRS and depression under stress, suggesting that MDD-PRS can add unique predictive power to existing models of depression under stress. Further, we found that low MDD-PRS may have particular utility in identifying individuals with high resilience. Together, these findings suggest that polygenic risk score holds promise in furthering our ability to predict vulnerability and resilience under stress.

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Polygenic risk score based on weight gain trajectories is predictive of childhood obesity

10.1101/606277 ◽

2019 ◽

Author(s):

Sarah J. C. Craig ◽

Ana M. Kenney ◽

Junli Lin ◽

Ian M. Paul ◽

Leann L. Birch ◽

...

Keyword(s):

Weight Gain ◽

Childhood Obesity ◽

Risk Score ◽

Genetic Variants ◽

Association Studies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Genome Wide

AbstractObesity is highly heritable, yet only a small fraction of its heritability has been attributed to specific genetic variants. These variants are traditionally ascertained from genome-wide association studies (GWAS), which utilize samples with tens or hundreds of thousands of individuals for whom a single summary measurement (e.g., BMI) is collected. An alternative approach is to focus on a smaller, more deeply characterized sample in conjunction with advanced statistical models that leverage detailed phenotypes. Here we use novel functional data analysis (FDA) techniques to capitalize on longitudinal growth information and construct a polygenic risk score (PRS) for obesity in children followed from birth to three years of age. This score, comprised of 24 single nucleotide polymorphisms (SNPs), is significantly higher in children with (vs. without) rapid infant weight gain—a predictor of obesity later in life. Using two independent cohorts, we show that genetic variants identified in early childhood are also informative in older children and in adults, consistent with early childhood obesity being predictive of obesity later in life. In contrast, PRSs based on SNPs identified by adult obesity GWAS are not predictive of weight gain in our cohort of children. Our research provides an example of a successful application of FDA to GWAS. We demonstrate that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes. This study shows how FDA approaches can be used as an alternative to the traditional GWAS.Author SummaryFinding genetic variants that confer an increased risk of developing a particular disease has long been a focus of modern genetics. Genome wide association studies (GWAS) have catalogued single nucleotide polymorphisms (SNPs) associated with a variety of complex diseases in humans, including obesity, but by and large have done so using increasingly large samples-- tens or even hundreds of thousands of individuals, whose phenotypes are thus often only superficially characterized. This, in turn, may hide the intricacies of the genetic influence on disease. GWAS findings are also usually study-population dependent. We found that genetic risk scores based on SNPs from large adult obesity studies are not predictive of the propensity to gain weight in very young children. However, using a small cohort of a few hundred children deeply characterized with growth trajectories between birth and two years, and leveraging such trajectories through novel functional data analysis (FDA) techniques, we were able to produce a strong childhood obesity genetic risk score.

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Analysis of the Interaction between Polygenic Risk Score and Calorie Intake in Obesity in the Korean Population

Lifestyle Genomics ◽

10.1159/000511333 ◽

2020 ◽

pp. 1-10

Author(s):

Won-Jun Lee ◽

Ji Eun Lim ◽

Hae Un Jung ◽

Ji-One Kang ◽

Taesung Park ◽

...

Keyword(s):

Risk Score ◽

Significant Interaction ◽

Association Studies ◽

Calorie Intake ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Polygenic Risk ◽

Total Calorie ◽

Total Calorie Intake

Introduction: Obesity results from an imbalance in the intake and expenditure of calories that leads to lifestyle-related diseases. Although genome-wide association studies (GWAS) have revealed many obesity-related genetic factors, the interactions of these factors and calorie intake remain unknown. This study aimed to investigate interactions between calorie intake and the polygenic risk score (PRS) of BMI. Methods: Three cohorts, i.e., from the Korea Association REsource (KARE; n = 8,736), CArdioVAscular Disease Association Study (CAVAS; n = 9,334), and Health EXAminee (HEXA; n = 28,445), were used for this study. BMI-related genetic loci were selected from previous GWAS. Two scores, PRS, and association (a)PRS, were used; the former was determined from 193 single-nucleotide polymorphisms (SNPs) from 5 GWAS datasets, and the latter from 62 SNPs (potentially associated) from 3 Korean cohorts (meta-analysis, p < 0.01). Results: PRS and aPRS were significantly associated with BMI in all 3 cohorts but did not exhibit a significant interaction with total calorie intake. Similar results were obtained for obesity. PRS and aPRS were significantly associated with obesity but did not show a significant interaction with total calorie intake. We further analyzed the interaction with protein, fat, and carbohydrate intake. The results were similar to those for total calorie intake, with PRS and aPRS found to not be associated with the interaction of any of the 3 nutrition components for either BMI or obesity. Discussion: The interaction of BMI PRS with calorie intake was investigated in 3 independent Korean cohorts (total n = 35,094) and no interactions were found between PRS and calorie intake for obesity.

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A polygenic risk score for multiple myeloma risk prediction

European Journal of Human Genetics ◽

10.1038/s41431-021-00986-8 ◽

2021 ◽

Author(s):

Federico Canzian ◽

Chiara Piredda ◽

Angelica Macauda ◽

Daria Zawirska ◽

Niels Frost Andersen ◽

...

Keyword(s):

Multiple Myeloma ◽

Risk Score ◽

Association Studies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Epidemiologic Evidence ◽

Risk Variants ◽

Genome Wide ◽

Weighted Score

AbstractThere is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53–4.69, p = 3.55 × 10−15 for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34–4.33, p = 1.62 × 10−13 for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.

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Precision Colorectal Cancer Screening with Polygenic Risk Score

10.1101/2020.08.19.20177931 ◽

2020 ◽

Author(s):

Tonis Tasa ◽

Mikk Puustusmaa ◽

Neeme Tonisson ◽

Berit Kolk ◽

Peeter Padrik

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Association Studies ◽

Absolute Risk ◽

Specific Model ◽

Background Information ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Common Cancer

Colorectal cancer (CRC) is the second most common cancer in women and third most common cancer in men. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with CRC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of individuals according to PRS could be introduced to primary and secondary prevention. Our aim was to combine risk stratification of a sex-specific PRS model with recommendations for individualized CRC screening. Previously published PRS models for predicting the risk of CRC were collected from the literature. These were validated on the UK Biobank (UKBB) consisting of a total of 458 696 quality-controlled genotypes with 1810 and 1348 prevalent male cases, and 2410 and 1810 incident male and female cases. The best performing sex-specific model was selected based on the AUC in prevalent data and independently validated in the incident dataset. Using Estonian CRC background information, we performed absolute risk simulations and examined the ability of PRS in risk stratifying individual screening recommendations. The best-performing model included 91 SNPs. The C-index of the best performing model in the dataset was 0.613 (SE = 0.007) and hazard ratio (HR) per unit of PRS was 1.53 (1.47 - 1.59) for males. Respective metrics for females were 0.617 (SE = 0.006) and 1.50 (1.44 - 1.58). PRS risk simulations showed that a genetically average 50-year-old female doubles her risk by age 58 (55 in males) and triples it by age 63 (59 in males). In addition, the best performing PRS model was able to identify individuals in one of seven groups proposed by Naber et al. for different coloscopy screening recommendation regimens. We have combined PRS-based recommendations for individual screening attendance. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.

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Genetic Background of Mesalamine-induced Fever and Diarrhea in Japanese Patients with Inflammatory Bowel Disease

Inflammatory Bowel Diseases ◽

10.1093/ibd/izab004 ◽

2021 ◽

Author(s):

Kaoru Suzuki ◽

Yoichi Kakuta ◽

Takeo Naito ◽

Tetsuya Takagawa ◽

Hiroyuki Hanai ◽

...

Keyword(s):

Risk Score ◽

Genetic Background ◽

Association Studies ◽

Meta Analysis ◽

Area Under The Curve ◽

Genome Wide Association ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Genome Wide

Abstract Background Some patients with inflammatory bowel disease (IBD) who were under mesalamine treatment develop adverse reactions called “mesalamine allergy,” which includes high fever and worsening diarrhea. Currently, there is no method to predict mesalamine allergy. Pharmacogenomic approaches may help identify these patients. Here we analyzed the genetic background of mesalamine intolerance in the first genome-wide association study of Japanese patients with IBD. Methods Two independent pharmacogenetic IBD cohorts were analyzed: the MENDEL (n = 1523; as a discovery set) and the Tohoku (n = 788; as a replication set) cohorts. Genome-wide association studies were performed in each population, followed by a meta-analysis. In addition, we constructed a polygenic risk score model and combined genetic and clinical factors to model mesalamine intolerance. Results In the combined cohort, mesalamine-induced fever and/or diarrhea was significantly more frequent in ulcerative colitis vs Crohn’s disease. The genome-wide association studies and meta-analysis identified one significant association between rs144384547 (upstream of RGS17) and mesalamine-induced fever and diarrhea (P = 7.21e-09; odds ratio = 11.2). The estimated heritability of mesalamine allergy was 25.4%, suggesting a significant correlation with the genetic background. Furthermore, a polygenic risk score model was built to predict mesalamine allergy (P = 2.95e-2). The combined genetic/clinical prediction model yielded a higher area under the curve than did the polygenic risk score or clinical model alone (area under the curve, 0.89; sensitivity, 71.4%; specificity, 90.8%). Conclusions Mesalamine allergy was more common in ulcerative colitis than in Crohn’s disease. We identified a novel genetic association with and developed a combined clinical/genetic model for this adverse event.

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A/T/N polygenic risk score for cognitive decline in old age

10.1101/838847 ◽

2019 ◽

Author(s):

Annah M. Moore ◽

Teresa J. Filshtein ◽

Logan Dumitrescu ◽

Amal Harrati ◽

Fanny Elahi ◽

...

Keyword(s):

Executive Function ◽

Cognitive Decline ◽

Risk Score ◽

Genetic Risk ◽

Clinical Utility ◽

Association Studies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Phosphorylated Tau ◽

Polygenic Risk

AbstractINTRODUCTIONWe developed a novel polygenic risk score (PRS) based on the A/T/N (amyloid plaques (A), phosphorylated tau tangles (T), and neurodegeneration (N)) framework and compared a PRS based on clinical AD diagnosis to assess which was a better predictor of cognitive decline.METHODSWe used summary statistics from genome wide association studies of cerebrospinal fluid amyloid-β (Aβ42) and phosphorylated-tau (ptau181), left hippocampal volume (LHIPV), and late-onset AD dementia to calculate PRS for 1181 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Individual PRS were averaged to generate a composite A/T/N PRS. We assessed the association of PRS with baseline and longitudinal cognitive composites of executive function and memory.RESULTSThe A/T/N PRS showed superior predictive performance on AD biomarkers and executive function decline compared to the clinical AD PRS.DISCUSSIONResults suggest that integration of genetic risk across AD biomarkers may improve prediction of disease progression.Research in ContextSystematic ReviewAuthors reviewed relevant literature using PubMed and Google Scholar. Key studies that generated and validated polygenic risk scores (PRS) for clinical and pathologic AD were cited. PRS scores have been increasingly used in the literature but clinical utility continues to be questioned.InterpretationIn the current research landscape concerning PRS clinical utility in the AD space, there is room for model improvement and our hypothesis was that a PRS with integrated risk for AD biomarkers could yield a better model for cognitive decline.Future DirectionsThis study serves as proof-of-concept that encourages future study of integrated PRS across disease markers and utility in taking an A/T/N (amyloidosis, tauopathy and neurodegeneration) focused approach to genetic risk for cognitive decline and AD.

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