Genetic scoring analysis: a way forward in genome wide association studies?

Machine Learning SNP Based Prediction for Precision Medicine

In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.

Integrative omics analysis identifies macrophage migration inhibitory factor signaling pathways underlying human hepatic fibrogenesis and fibrosis

The genetic basis underlying liver fibrosis remains largely unknown. We conducted a study to identify genetic alleles and underlying pathways associated with hepatic fibrogenesis and fibrosis at the genome-wide level in 121 human livers. By accepting a liberal significance level of P<1e-4, we identified 73 and 71 candidate loci respectively affecting the variability in alpha-smooth muscle actin (α-SMA) levels (fibrogenesis) and total collagen content (fibrosis). The top genetic loci associated with the two markers were BAZA1 and NOL10 for α-SMA expression and FAM46A for total collagen content (P<1e-6). We further investigated the relationship between the candidate loci and the nearby gene transcription levels (cis-expression quantitative trait loci) in the same liver samples. We found that 44 candidate loci for α-SMA expression and 44 for total collagen content were also associated with the transcription of the nearby genes (P<0.05). Pathway analyses of these genes indicated that macrophage migration inhibitory factor (MIF) related pathway is significantly associated with fibrogenesis and fibrosis, though different genes were enriched for each marker. The association between the single nucleotide polymorphisms, MIF and α-SMA showed that decreased MIF expression is correlated with increased α-SMA expression, suggesting that variations in MIF locus might affect the susceptibility of fibrogenesis through controlling MIF gene expression. In summary, our study identified candidate alleles and pathways underlying both fibrogenesis and fibrosis in human livers. Our bioinformatics analyses suggested MIF pathway as a strong candidate involved in liver fibrosis, thus further investigation for the role of the MIF pathway in liver fibrosis is warranted. The study was reviewed and approved by the Institutional Review Board (IRB) of Wayne State University (approval No. 201842) on May 17, 2018.

Missing single nucleotide polymorphisms in Genetic Risk Scores: A simulation study

Using a genetic risk score (GRS) to predict a phenotype in a target sample can be complicated by missing data on the single nucleotide polymorphisms (SNPs) that comprise the GRS. This is usually addressed by imputation, omission of the SNPs or by replacing the missing SNPs with proxy SNPs. To assess the impact of the omission and proxy approaches on effect size estimation and predictive ability of weighted and unweighted GRS with small numbers of SNPs, we simulated a dichotomous phenotype conditional on real genotype data. We considered scenarios in which the proportion of missing SNPs ranged from 20-70%. We assessed the impact of omitting or replacing missing SNPs on the association between the GRS and phenotype, the corresponding statistical power and the area under the receiver operating curve. Omission resulted in a larger bias towards the null value of the effect size, a smaller predictive ability and greater loss of statistical power than proxy approaches. The predictive ability of a weighted GRS that includes SNPs with large weights depends of the availability of these large-weight SNPs.

Assessing individual risk for AMD with genetic counseling, family history, and genetic testing

PurposeThe goal was to develop a simple model for predicting the individual risk profile for age-related macular degeneration (AMD) on the basis of genetic information, disease family history, and smoking habits.Patients and methodsThe study enrolled 151 AMD patients following specific clinical and environmental inclusion criteria: age >55 years, positive family history for AMD, presence of at least one first-degree relative affected by AMD, and smoking habits. All of the samples were genotyped for rs1061170 (CFH) and rs10490924 (ARMS2) with a TaqMan assay, using a 7500 Fast Real Time PCR device. Statistical analysis was subsequently employed to calculate the real individual risk (OR) based on the genetic data (ORgn), family history (ORf), and smoking habits (ORsm).Results and conclusionThe combination of ORgn, ORf, and ORsm allowed the calculation of the Ort that represented the realistic individual risk for developing AMD. In this report, we present a computational model for the estimation of the individual risk for AMD. Moreover, we show that the average distribution of risk alleles in the general population and the knowledge of parents' genotype can be decisive to assess the real disease risk. In this contest, genetic counseling is crucial to provide the patients with an understanding of their individual risk and the availability for preventive actions.

Contemporary Considerations for Constructing a Genetic Risk Score: An Empirical Approach

Genetic risk scores are an increasingly popular tool for summarizing the cumulative risk of a set of Single Nucleotide Polymorphisms (SNPs) with disease. Typically only the set of the SNPs that have reached genome-wide significance compose these scores. However recent work suggests that including additional SNPs may aid risk assessment. In this paper, we used the Atherosclerosis Risk in Communities (ARIC) Study cohort to illustrate how one can choose the optimal set of SNPs for a genetic risk score (GRS). In addition to P-value threshold, we also examined linkage disequilibrium, imputation quality, and imputation type. We provide a variety of evaluation metrics. Results suggest that P-value threshold had the greatest impact on GRS quality for the outcome of coronary heart disease, with an optimal threshold around 0.001. However, GRSs are relatively robust to both linkage disequilibrium and imputation quality. We also show that the optimal GRS partially depends on the evaluation metric and consequently the way one intends to use the GRS. Overall the implications highlight both the robustness of GRS and a means to empirically choose the best set of GRSs.

The effects of the interplay of genetics and early environmental risk on the course of internalizing symptoms from late childhood through adolescence

Internalizing symptoms during adolescence and beyond is a major public health concern, particularly because severe symptoms can lead to the diagnosis of a number of serious psychiatric conditions. This study utilizes a unique sample with a complex statistical method in order to explore Gene × Environment interactions found in internalizing symptoms during adolescence. Data for this study were drawn from a longitudinal prevention intervention study (n = 798) of Baltimore city school children. Internalizing symptom data were collected using self-report and blood or saliva samples genotyped using Affymetrix 6.0 microarrays. A major depression polygenic score was created for each individual using information from the major depressive disorder Psychiatric Genetics Consortium and used as a predictor in a latent trait-state-occasion model. The major depressive disorder polygenic score was a significant predictor of the stable latent trait variable, which captures time-independent phenotypic variability. In addition, an early childhood stressor of death or divorce was a significant predictor of occasion-specific variables. A Gene × Environment interaction was not a significant predictor of the latent trait or occasion variables. These findings support the importance of genetics on the stable latent trait portion of internalizing symptoms across adolescence.

A discovery study of daunorubicin induced cardiotoxicity in a sample of acute myeloid leukemia patients prioritizes P450 oxidoreductase polymorphisms as a potential risk factor

Anthracyclines are very effective chemotherapeutic agents; however, their use is hampered by the treatment-induced cardiotoxicity. Genetic variants that help define patient's sensitivity to anthracyclines will greatly improve the design of optimal chemotherapeutic regimens. However, identification of such variants is hampered by the lack of analytical approaches that address the complex, multi-genic character of anthracycline induced cardiotoxicity (AIC). Here, using a multi-SNP based approach, we examined 60 genes coding for proteins involved in drug metabolism and efflux and identified the P450 oxidoreductase (POR) gene to be most strongly associated with daunorubicin induced cardiotoxicity in a population of acute myeloid leukemia (AML) patients (FDR adjusted p-value of 0.15). In this sample of cancer patients, variation in the POR gene is estimated to account for some 11.6% of the variability in the drop of left ventricular ejection fraction (LVEF) after daunorubicin treatment, compared to the estimated 13.2% accounted for by the cumulative dose and ethnicity. In post-hoc analysis, this association was driven by 3 SNPs-the rs2868177, rs13240755, and rs4732513-through their linear interaction with cumulative daunorubicin dose. The unadjusted odds ratios (ORs) and confidence intervals (CIs) for rs2868177 and rs13240755 were estimated to be 1.89 (95% CI: 0.7435-4.819; p = 0.1756) and 3.18 (95% CI: 1.223-8.27; p = 0.01376), respectively. Although the contribution of POR variants is expected to be overestimated due to the multiple testing performed in this small pilot study, given that cumulative anthracycline dose is virtually the only factor used clinically to predict the risk of cardiotoxicity, the contribution that genetic analyses of POR can make to the assessment of this risk is worthy of follow up in future investigations.

The aggregate effect of dopamine genes on dependence symptoms among cocaine users: cross-validation of a candidate system scoring approach

Genome-wide studies of psychiatric conditions frequently fail to explain a substantial proportion of variance, and replication of individual SNP effects is rare. We demonstrate a selective scoring approach, in which variants from several genes known to directly affect the dopamine system are considered concurrently to explain individual differences in cocaine dependence symptoms. 273 SNPs from eight dopamine-related genes were tested for association with cocaine dependence symptoms in an initial training sample. We identified a four-SNP score that accounted for 0.55% of the variance in a separate testing sample (p = 0.037). These findings suggest that (1) limiting investigated SNPs to those located in genes of theoretical importance improves the chances of identifying replicable effects by reducing statistical penalties for multiple testing, and (2) considering top-associated SNPs in the aggregate can reveal replicable effects that are too small to be identified at the level of individual SNPs.

Challenges for epidemiologic research on the verge of a new era

Although risk factor epidemiology has achieved much, it has its limitations (e.g., a failure to reveal causal mechanisms at multiple levels). To illustrate contemporary challenges for epidemiological research, we present a dialog with examples and argue for incorporating a "systems thinking through a life course" paradigm in epidemiological research. There is an increasing interest in moving part of public health from a discipline concerned primarily with risk factors at the individual level toward one concerned with complex causal patterns which often operate across different levels in time and space (e.g., from the molecular to the population, from the past to the future, and from the distal to the proximal). However, the methodology for discovering these complex and dynamic relationships remains to be improved. We propose strategies for taking up this challenge.

Genetic architecture of circulating lipid levels

Serum concentrations of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs) and total cholesterol (TC) are important heritable risk factors for cardiovascular disease. Although genome-wide association studies (GWASs) of circulating lipid levels have identified numerous loci, a substantial portion of the heritability of these traits remains unexplained. Evidence of unexplained genetic variance can be detected by combining multiple independent markers into additive genetic risk scores. Such polygenic scores, constructed using results from the ENGAGE Consortium GWAS on serum lipids, were applied to predict lipid levels in an independent population-based study, the Rotterdam Study-II (RS-II). We additionally tested for evidence of a shared genetic basis for different lipid phenotypes. Finally, the polygenic score approach was used to identify an alternative genome-wide significance threshold before pathway analysis and those results were compared with those based on the classical genome-wide significance threshold. Our study provides evidence suggesting that many loci influencing circulating lipid levels remain undiscovered. Cross-prediction models suggested a small overlap between the polygenic backgrounds involved in determining LDL-C, HDL-C and TG levels. Pathway analysis utilizing the best polygenic score for TC uncovered extra information compared with using only genome-wide significant loci. These results suggest that the genetic architecture of circulating lipids involves a number of undiscovered variants with very small effects, and that increasing GWAS sample sizes will enable the identification of novel variants that regulate lipid levels.

The Shanghai Changfeng Study: a community-based prospective cohort study of chronic diseases among middle-aged and elderly: objectives and design

The Shanghai Changfeng Study is a community-based prospective cohort study of chronic diseases ongoing since February 2009 in Shanghai, China. The study focuses on multiple chronic diseases, including obesity and metabolic syndrome, diabetes, osteoporosis, liver diseases, cardiovascular diseases and neurologic diseases. 15,000 subjects of 40 years or over are planned to be recruited. The rationale, objectives and design of this study are described in this paper.

The Generation R Study: design and cohort update 2010

The Generation R Study is a population-based prospective cohort study from fetal life until young adulthood. The study is designed to identify early environmental and genetic causes of normal and abnormal growth, development and health during fetal life, childhood and adulthood. The study focuses on four primary areas of research: (1) growth and physical development; (2) behavioural and cognitive development; (3) diseases in childhood; and (4) health and healthcare for pregnant women and children. In total, 9,778 mothers with a delivery date from April 2002 until January 2006 were enrolled in the study. General follow-up rates until the age of 4 years exceed 75%. Data collection in mothers, fathers and preschool children included questionnaires, detailed physical and ultrasound examinations, behavioural observations, and biological samples. A genome wide association screen is available in the participating children. Regular detailed hands on assessment are performed from the age of 5 years onwards. Eventually, results forthcoming from the Generation R Study have to contribute to the development of strategies for optimizing health and healthcare for pregnant women and children.