Genetic-based prediction of disease traits: prediction is very difficult, especially about the future

Human height: a model common complex trait

CONTEXT

Like other complex phenotypes, human height reflects a combination of environmental and genetic factors, but is notable for being exceptionally easy to measure. Height has therefore been commonly used to make observations later generalised to other phenotypes though the appropriateness of such generalisations is not always considered.

OBJECTIVES

We aimed to assess height's suitability as a model for other complex phenotypes and review recent advances in height genetics with regard to their implications for complex phenotypes more broadly.

METHODS

We conducted a comprehensive literature search in PubMed and Google Scholar for articles relevant to the genetics of height and its comparatibility to other phenotypes.

RESULTS

Height is broadly similar to other phenotypes apart from its high heritability and ease of measurment. Recent genome-wide association studies (GWAS) have identified over 12,000 independent signals associated with height and saturated height's common single nucleotide polymorphism based heritability of height within a subset of the genome in individuals similar to European reference populations.

CONCLUSIONS

Given the similarity of height to other complex traits, the saturation of GWAS's ability to discover additional height-associated variants signals potential limitations to the omnigenic model of complex-phenotype inheritance, indicating the likely future power of polygenic scores and risk scores, and highlights the increasing need for large-scale variant-to-gene mapping efforts.

Migraine: interactions between brain's trait and state

It is well established that migraine is a multifactorial disorder. A deep understanding of migraine should be based upon both the underlying traits and the current states affected by different physiological, psychological, and environmental factors. At this point, there is no framework fully meeting these criteria. Here, we describe a broader view of the migraine disorder defined as a dysfunctional brain state and trait interaction. In this model, we consider events that may enhance or diminish migraine responsivity based on an individual's trait and state. This could provide an expanded view for considering how migraine attacks are sometimes precipitated by "triggers" and sometimes not, how these factors only lead to migraine attacks in migraine patients, or how individuals with an increased risk for migraine do not show any symptoms at all. Summarizing recent studies and evidence that support the concept of migraine as a brain state-trait interaction can also contribute to improving patient care by highlighting the importance of precision medicine and applying measures that are able to capture how different traits and states work together to determine migraine.

Association of the PROGINS PgR polymorphism with susceptibility to female reproductive cancer: A meta-analysis of 30 studies

Aims

The progesterone response of the nuclear progesterone receptor plays an important role in the female reproductive system. Changes in the function of the progesterone receptor gene may increase the risk of reproductive cancer. The present study performed a meta-analysis to examine whether the progesterone receptor gene PROGINS polymorphism was a susceptibility factor for female reproductive cancer.

Materials and methods

We searched the PubMed, Cochrane Library, Web of Science and EMBASE databases for literature on PROGINS polymorphisms and female reproductive cancer published before September 2020. We evaluated the risk using odds ratios [ORs] and 95% confidence intervals via fixed effects models and random-effects models, which were calculated for all five genetic models. We grouped the analyses by race, cancer, and HWE.

Results

Thirty studies comprised of 25405 controls and 19253 female reproductive cancer cases were included in this meta-analysis. We observed that the Alu insertion polymorphism and the V660L polymorphism were significantly associated with female reproductive cancer in the allele and dominant genetic models. The allele genetic model and (Alu-insertion polymorphism: OR = 1.22, 95% CI = 1.02–1.45; V660L polymorphism: OR = 1.02, 95% CI = 1.00–1.13) dominant genetic model (Alu-insertion polymorphism: OR = 1.27, 95% CI = 1.03–1.58; V660L polymorphism: OR = 1.10, 95% CI = 1.011.19) demonstrated a significantly increased risk of female reproductive cancer. A subgroup analysis according to ethnicity found that the Alu insertion was associated with female reproductive cancer incidence in white (Allele model: OR = 1.21, 95% CI = 1.00–1.45; Heterozygous model: OR = 3.44, 95% CI = 1.30–9.09) and Asian (Dominant model: OR = 3.12, 95% CI = 1.25–7.79) populations, but the association disappeared for African and mixed racial groups. However, the V660L polymorphism was significantly associated with female reproductive cancer in the African (Allele model: OR = 2.52, 95% CI = 1.14–5.56; Heterozygous model: OR = 2.83, 95% CI = 1.26–6.35) and mixed racial groups (Dominant model: OR = 1.28, 95% CI = 1.01–1.62). Subgroup analysis by cancer showed that the PROGINS polymorphism increased the risk of cancer in the allele model, dominant mode and heterozygous model, but the confidence interval for this result spanned 1 and was not statistically significant. This sensitivity was verified in studies with HWE greater than 0.5.

Conclusion

Our meta-analysis showed that the progesterone receptor gene Alu insertion and the V660L polymorphism contained in the PROGINS polymorphism were susceptibility factors for female reproductive cancer.

Whole-exome Sequencing Identifies SLC52A1 and ZNF106 Variants as Novel Genetic Risk Factors for (Early) Multiple-organ Failure in Acute Pancreatitis

OBJECTIVE

The aim of this study was to identify genetic variants associated with early multiple organ failure (MOF) in acute pancreatitis.

SUMMARY BACKGROUND DATA

MOF is a life-threatening complication of acute pancreatitis, and risk factors are largely unknown, especially in early persistent MOF. Genetic risk factors are thought to enhance severity in complex diseases such as acute pancreatitis.

METHODS

A 2-phase study design was conducted. First, we exome sequenced 9 acute pancreatitis patients with early persistent MOF and 9 case-matched patients with mild edematous pancreatitis (phenotypic extremes) from our initial Dutch cohort of 387 patients. Secondly, 48 candidate variants that were overrepresented in MOF patients and 10 additional variants known from literature were genotyped in a replication cohort of 286 Dutch and German patients.

RESULTS

Exome sequencing resulted in 161,696 genetic variants, of which the 38,333 non-synonymous variants were selected for downstream analyses. Of these, 153 variants were overrepresented in patients with multiple-organ failure, as compared with patients with mild acute pancreatitis. In total, 58 candidate variants were genotyped in the joined Dutch and German replication cohort. We found the rs12440118 variant of ZNF106 to be overrepresented in patients with MOF (minor allele frequency 20.4% vs 11.6%, Padj=0.026). Additionally, SLC52A1 rs346821 was found to be overrepresented (minor allele frequency 48.0% vs 42.4%, Padj= 0.003) in early MOF. None of the variants known from literature were associated.Conclusions: This study indicates that SLC52A1, a riboflavin plasma membrane transporter, and ZNF106, a zinc finger protein, may be involved in disease progression toward (early) MOF in acute pancreatitis.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.

PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics

Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.

Genetic variants of CHEK1, PRIM2 and CDK6 in the mitotic phase-related pathway are associated with nonsmall cell lung cancer survival

The mitotic phase is a vital step in cell division and may be involved in cancer progression, but it remains unclear whether genetic variants in mitotic phase-related pathways genes impact the survival of these patients. Here, we investigated associations between 31 032 single nucleotide polymorphisms (SNPs) in 368 mitotic phase-related pathway genes and overall survival (OS) of patients with nonsmall cell lung cancer (NSCLC). We assessed the associations in a discovery data set of 1185 NSCLC patients from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and validated the findings in another data set of 984 patients from the Harvard Lung Cancer Susceptibility Study. As a result, we identified three independent SNPs (ie, CHEK1 rs76744140 T>C, PRIM2 rs6939623 G>T and CDK6 rs113181986 G>C) to be significantly associated with NSCLC OS with an adjusted hazard ratio of 1.29 (95% confidence interval = 1.11-1.49, P = 8.26 × 10^-4 ), 1.26 (1.12-1.42, 1.10 × 10^-4 ) and 0.73 (0.63-0.86, 1.63 × 10^-4 ), respectively. Moreover, the number of combined unfavorable genotypes of these three SNPs was significantly associated with NSCLC OS and disease-specific survival in the PLCO data set (P_trend  < .0001 and .0003, respectively). Further expression quantitative trait loci analysis showed that the rs76744140C allele predicted CHEK1 mRNA expression levels in normal lung tissues and that rs113181986C allele predicted CDK6 mRNA expression levels in whole blood tissues. Additional analyses indicated CHEK1, PRIM2 and CDK6 may impact NSCLC survival. Taken together, these findings suggested that these genetic variants may be prognostic biomarkers of patients with NSCLC.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

Statistical Methods in Genome-Wide Association Studies

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

Polygenetic risk scores (pGRSs) consisting of adult body mass index (BMI) genetic variants have been widely associated with obesity in children populations. The implication of such obesity pGRSs in the development of cardio-metabolic alterations during childhood as well as their utility for the clinical prediction of pubertal obesity outcomes has been barely investigated otherwise. In the present study, we evaluated the utility of an adult BMI predisposing pGRS for the prediction and pharmacological management of obesity in Spanish children, further investigating its implication in the appearance of cardio-metabolic alterations. For that purpose, we counted on genetics data from three well-characterized children populations (composed of 574, 96 and 124 individuals), following both cross-sectional and longitudinal designs, expanding childhood and puberty. As a result, we demonstrated that the pGRS is strongly associated with childhood BMI Z-Score (B = 1.56, SE = 0.27 and p-value = 1.90 × 10⁻⁸), and that could be used as a good predictor of obesity longitudinal trajectories during puberty. On the other hand, we showed that the pGRS is not associated with cardio-metabolic comorbidities in children and that certain environmental factors interact with the genetic predisposition to the disease. Finally, according to the results derived from a weight-reduction metformin intervention in children with obesity, we discarded the utility of the pGRS as a pharmacogenetics marker of metformin response.

Potentially functional genetic variants in PLIN2, SULT2A1 and UGT1A9 genes of the ketone pathway and survival of nonsmall cell lung cancer

The ketone metabolism pathway is a principle procedure in physiological homeostasis and induces cancer cells to switch between glycolysis and oxidative phosphorylation for energy production. We conducted a two-phase analysis for associations between genetic variants in the ketone metabolism pathway genes and survival of nonsmall cell lung cancer (NSCLC) by analyzing genotyping data from two published genome-wide association studies (GWASs). In the discovery, we used a genotyping dataset from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial in the multivariable Cox proportional hazards regression analysis. We used Bayesian false discovery probability (≤0.80) for multiple testing correction to evaluate associations between 25,819 (2,176 genotyped and 23,643 imputed) single-nucleotide polymorphisms (SNPs) in 162 genes and survival of 1,185 NSCLC patients. Subsequently, we validated the identified significant SNPs with an additional 984 NSCLC patients from the Harvard Lung Cancer Susceptibility GWAS study. Finally, we found that three independent and potentially functional SNPs in three different genes (i.e., PLIN2 rs7867814 G>A, SULT2A1 rs2547235 C>T and UGT1A9 rs2011404 C>T) were independently associated with risk of death from NSCLC, with a combined hazards ratio of 1.22 [95% confidence interval = 1.09-1.36 and p = 0.0003], 0.82 (0.74-0.91 and p = 0.0002) and 1.21 (1.10-1.33 and p = 0.0001), respectively. Additional expression quantitative trait loci analysis found that the survival-associated PLIN2 rs7867814 GA + AA genotypes, but not the genotypes of other two SNPs, were significantly associated with increased mRNA expression levels (p = 0.005). These results indicated that PLIN2 variants may be potential predictors of NSCLC survival through regulating the PLIN2 expression.

The Genetic Architecture of High Bone Mass

The phenotypic trait of high bone mass (HBM) is an excellent example of the nexus between common and rare disease genetics. HBM may arise from carriage of many 'high bone mineral density [BMD]'-associated alleles, and certainly the genetic architecture of individuals with HBM is enriched with high BMD variants identified through genome-wide association studies of BMD. HBM may also arise as a monogenic skeletal disorder, due to abnormalities in bone formation, bone resorption, and/or bone turnover. Individuals with monogenic disorders of HBM usually, though not invariably, have other skeletal abnormalities (such as mandible enlargement) and thus are best regarded as having a skeletal dysplasia rather than just isolated high BMD. A binary etiological division of HBM into polygenic vs. monogenic, however, would be excessively simplistic: the phenotype of individuals carrying rare variants of large effect can still be modified by their common variant polygenic background, and by the environment. HBM disorders-whether predominantly polygenic or monogenic in origin-are not only interesting clinically and genetically: they provide insights into bone processes that can be exploited therapeutically, with benefits both for individuals with these rare bone disorders and importantly for the many people affected by the commonest bone disease worldwide-i.e., osteoporosis. In this review we detail the genetic architecture of HBM; we provide a conceptual framework for considering HBM in the clinical context; and we discuss monogenic and polygenic causes of HBM with particular emphasis on anabolic causes of HBM.

Data integration and predictive modeling methods for multi-omics datasets

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

Clinical Genomics in Inflammatory Bowel Disease

Genomic technologies inform the complex genetic basis of polygenic inflammatory bowel disease (IBD) as well as Mendelian disease-associated IBD. Aiming to diagnose patients that present with extreme phenotypes due to monogenic forms of IBD, genomics has progressed from 'orphan disease' research towards an integrated standard of clinical care. Advances in diagnostic clinical genomics are increasingly complemented by pathway-specific therapies that aim to correct the consequences of genetic defects. This highlights the exceptional potential for personalized precision medicine. IBD is nevertheless a challenging example for genomic medicine because the overall fraction of patients with Mendelian defects is low, the number of potential candidate genes is high, and interventional evidence is still emerging. We discuss requirements and prospects of explanatory and predictive clinical genomics in IBD.

Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction

Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn’s disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

Genetic risk prediction plays a significant role in precision medicine. Accurate prediction models could have great impact on disease prevention and treatment strategies. However, prediction accuracies for most complex diseases remain moderate mainly due to the challenges in identifying and quantifying the effects of genetic variants from millions of markers, limited access to individual-level genotype data, and lack of efficient computational methods. Up to now, most methods have been focused on predicting disease risk using data from a single trait. With the discovery of genetic correlations among many complex diseases, incorporating data of genetically correlated diseases could have the potential to increase prediction accuracy. Current statistical methods are not able to fully exploit the richness of these kinds of data to take into account the shared genetic architecture. To make use of commonly available GWAS summary statistics, we propose a novel method to address these challenges by jointly modeling genetically correlated diseases and integrating genomic functional annotations. We demonstrate the substantial improvement in accuracy in both extensive simulation studies and real data analysis of Crohn’s disease, celiac disease and type-II diabetes. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

Leveraging functional annotations in genetic risk prediction for human complex diseases

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.

Genetics of hereditary nephrotic syndrome: a clinical review

Advances in podocytology and genetic techniques have expanded our understanding of the pathogenesis of hereditary steroid-resistant nephrotic syndrome (SRNS). In the past 20 years, over 45 genetic mutations have been identified in patients with hereditary SRNS. Genetic mutations on structural and functional molecules in podocytes can lead to serious injury in the podocytes themselves and in adjacent structures, causing sclerotic lesions such as focal segmental glomerulosclerosis or diffuse mesangial sclerosis. This paper provides an update on the current knowledge of podocyte genes involved in the development of hereditary nephrotic syndrome and, thereby, reviews genotype-phenotype correlations to propose an approach for appropriate mutational screening based on clinical aspects.

Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 68% (184% more phenotypic variance explained) over SNPs reported to be robustly associated with height in a previous GWAS meta-analysis of similar size. Across-population predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

Reflections on the Field of Human Genetics: A Call for Increased Disease Genetics Theory

Development of human genetics theoretical models and the integration of those models with experiment and statistical evaluation are critical for scientific progress. This perspective argues that increased effort in disease genetics theory, complementing experimental, and statistical efforts, will escalate the unraveling of molecular etiologies of complex diseases. In particular, the development of new, realistic disease genetics models will help elucidate complex disease pathogenesis, and the predicted patterns in genetic data made by these models will enable the concurrent, more comprehensive statistical testing of multiple aspects of disease genetics predictions, thereby better identifying disease loci. By theoretical human genetics, I intend to encompass all investigations devoted to modeling the heritable architecture underlying disease traits and studies of the resulting principles and dynamics of such models. Hence, the scope of theoretical disease genetics work includes construction and analysis of models describing how disease-predisposing alleles (1) arise, (2) are transmitted across families and populations, and (3) interact with other risk and protective alleles across both the genome and environmental factors to produce disease states. Theoretical work improves insight into viable genetic models of diseases consistent with empirical results from linkage, transmission, and association studies as well as population genetics. Furthermore, understanding the patterns of genetic data expected under realistic disease models will enable more powerful approaches to discover disease-predisposing alleles and additional heritable factors important in common diseases. In spite of the pivotal role of disease genetics theory, such investigation is not particularly vibrant.

Multikernel linear mixed models for complex phenotype prediction

Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy.

Opportunities and Challenges of Genotyping Patients With Nephrotic Syndrome in the Genomic Era

Both targeted and genome-wide linkage and association studies have identified a number of genes and genetic variants associated with nephrotic syndrome (NS). Genotype-phenotype studies of patients with these variants have identified correlations of clear clinical significance. Combined with improved genomic technologies, this has resulted in increasing, and justifiable, enthusiasm for incorporating our patients' genomic information into our clinical management decisions. Here, we summarize our understanding of NS-associated genetic factors, namely rare causal mutations or common risk alleles in apolipoprotein L1. We discuss the complexities inherent in trying to ascribe risk or causality to these variants, particularly as we seek to extend genetic testing to a broader group of patients, including many with sporadic disease. Overall, the thoughtful application and interpretation of these genetic tests will maximize the benefits to our patients with NS in the form of more precise clinical care.

Understanding the pathogenesis of abdominal aortic aneurysms

An aortic aneurysm is a dilatation in which the aortic diameter is ≥3.0 cm. If left untreated, the aortic wall continues to weaken and becomes unable to withstand the forces of the luminal blood pressure resulting in progressive dilatation and rupture, a catastrophic event associated with a mortality of 50-80%. Smoking and positive family history are important risk factors for the development of abdominal aortic aneurysms (AAA). Several genetic risk factors have also been identified. On the histological level, visible hallmarks of AAA pathogenesis include inflammation, smooth muscle cell apoptosis, extracellular matrix degradation and oxidative stress. We expect that large genetic, genomic, epigenetic, proteomic and metabolomic studies will be undertaken by international consortia to identify additional risk factors and biomarkers, and to enhance our understanding of the pathobiology of AAA. Collaboration between different research groups will be important in overcoming the challenges to develop pharmacological treatments for AAA.

Neuroimaging genetic risk for Alzheimer's disease in preclinical individuals: From candidate genes to polygenic approaches

Better characterization of the preclinical phase of Alzheimer's disease (AD) is needed in order to develop effective interventions. Neuropathological changes in AD, including neuronal loss and the formation of proteinaceous deposits, begin up to 20 years before the onset of clinical symptoms. As such, the emergence of cognitive impairment should not be the sole basis used to diagnose AD nor to evaluate individuals for enrollment in clinical trials for preventative AD treatments. Instead, early preclinical biomarkers of disease and genetic risk should be used to determine most likely prognosis and enroll individuals in appropriate clinical trials. Neuroimaging-based biomarkers and genetic analysis together present a powerful system for classifying preclinical pathology in patients. Disease modifying interventions are more likely to produce positive outcomes when administered early in the course of AD. In this review, we examine the utility of the neuroimaging genetics field as it applies to AD and early detection during the preclinical phase. Neuroimaging studies focused on single genetic risk factors are summarized. However, we particularly focus on the recent increased interest in polygenic methods and discuss the benefits and disadvantages of these approaches. We discuss challenges in the neuroimaging genetics field, including limitations of statistical power arising from small effect sizes and the over-use of cross-sectional designs. Despite the limitations, neuroimaging genetics has already begun to influence clinical trial design and will play a major role in the prevention of AD.

A new tool called DISSECT for analysing large genomic data sets using a Big Data approach

Large-scale genetic and genomic data are increasingly available and the major bottleneck in their analysis is a lack of sufficiently scalable computational tools. To address this problem in the context of complex traits analysis, we present DISSECT. DISSECT is a new and freely available software that is able to exploit the distributed-memory parallel computational architectures of compute clusters, to perform a wide range of genomic and epidemiologic analyses, which currently can only be carried out on reduced sample sizes or under restricted conditions. We demonstrate the usefulness of our new tool by addressing the challenge of predicting phenotypes from genotype data in human populations using mixed-linear model analysis. We analyse simulated traits from 470,000 individuals genotyped for 590,004 SNPs in ∼4 h using the combined computational power of 8,400 processor cores. We find that prediction accuracies in excess of 80% of the theoretical maximum could be achieved with large sample sizes.

Availability of computing power can limit computational analysis of large genetic and genomic datasets. Here, Canela-Xandri, et al. describe a software called DISSECT that is capable of analyzing large-scale genetic data by distributing the work across thousands of networked computers.

Privacy Risks from Genomic Data-Sharing Beacons

The human genetics community needs robust protocols that enable secure sharing of genomic data from participants in genetic research. Beacons are web servers that answer allele-presence queries—such as “Do you have a genome that has a specific nucleotide (e.g., A) at a specific genomic position (e.g., position 11,272 on chromosome 1)?”—with either “yes” or “no.” Here, we show that individuals in a beacon are susceptible to re-identification even if the only data shared include presence or absence information about alleles in a beacon. Specifically, we propose a likelihood-ratio test of whether a given individual is present in a given genetic beacon. Our test is not dependent on allele frequencies and is the most powerful test for a specified false-positive rate. Through simulations, we showed that in a beacon with 1,000 individuals, re-identification is possible with just 5,000 queries. Relatives can also be identified in the beacon. Re-identification is possible even in the presence of sequencing errors and variant-calling differences. In a beacon constructed with 65 European individuals from the 1000 Genomes Project, we demonstrated that it is possible to detect membership in the beacon with just 250 SNPs. With just 1,000 SNP queries, we were able to detect the presence of an individual genome from the Personal Genome Project in an existing beacon. Our results show that beacons can disclose membership and implied phenotypic information about participants and do not protect privacy a priori. We discuss risk mitigation through policies and standards such as not allowing anonymous pings of genetic beacons and requiring minimum beacon sizes.

Boosting for high-dimensional two-class prediction

Background

In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly exceeds the number of samples) the choice of an appropriate classifier is crucial as it was observed that no single classification algorithm performs optimally for all types of data. Boosting was proposed as a method that combines the classification results obtained using base classifiers, where the sample weights are sequentially adjusted based on the performance in previous iterations. Generally boosting outperforms any individual classifier, but studies with high-dimensional data showed that the most standard boosting algorithm, AdaBoost.M1, cannot significantly improve the performance of its base classier. Recently other boosting algorithms were proposed (Gradient boosting, Stochastic Gradient boosting, LogitBoost); they were shown to perform better than AdaBoost.M1 but their performance was not evaluated for high-dimensional data.

Results

In this paper we use simulation studies and real gene-expression data sets to evaluate the performance of boosting algorithms when data are high-dimensional. Our results confirm that AdaBoost.M1 can perform poorly in this setting, often failing to improve the performance of its base classifier. We provide the explanation for this and propose a modification, AdaBoost.M1.ICV, which uses cross-validated estimates of the prediction errors and outperforms the original algorithm when data are high-dimensional. The use of AdaBoost.M1.ICV is advisable when the base classifier overfits the training data: the number of variables is large, the number of samples is small, and/or the difference between the classes is large. To a lesser extent also Gradient boosting suffers from similar problems. Contrary to the findings for the low-dimensional data, shrinkage does not improve the performance of Gradient boosting when data are high-dimensional, however it is beneficial for Stochastic Gradient boosting, which outperformed the other boosting algorithms in our analyses. LogitBoost suffers from overfitting and generally performs poorly.

Conclusions

The results show that boosting can substantially improve the performance of its base classifier also when data are high-dimensional. However, not all boosting algorithms perform equally well. LogitBoost, AdaBoost.M1 and Gradient boosting seem less useful for this type of data. Overall, Stochastic Gradient boosting with shrinkage and AdaBoost.M1.ICV seem to be the preferable choices for high-dimensional class-prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0723-9) contains supplementary material, which is available to authorized users.

Actualizing the Benefits of Genomic Discovery in Pediatric Nephrology

The discovery of genetic variation associated with pediatric kidney disease has shed light on the biology underlying these conditions and, in some cases, has improved our clinical management of patients. We are challenged to continue the momentum of the genomic era in pediatric nephrology by identifying novel disease-associated genetic variation and translating these discoveries into clinical applications. This article reviews the diverse forms of genetic architecture that have been found to be associated with kidney diseases and traits. These include rare, fully penetrant variants responsible for Mendelian forms of disease, copy number variants, and more common variants associated with increased risk of disease. These discoveries have provided us with a greater understanding of the molecular mechanisms underlying these conditions and highlighted key pathways for potential intervention. In a number of areas, the identification of rare, fully penetrant variants is immediately clinically relevant, whether in regard to diagnostic testing, prediction of outcomes, or choice of therapies and interventions. This article discusses limitations in the deterministic view of rare, putatively causal mutations, a challenge increasing in importance as sequencing expands to many more genes and patients. This article also focusses on common genetic variants, using those found to be associated with focal segmental glomerulosclerosis in African-Americans, IgA nephropathy, chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR) as examples. Identifying common genetic variants associated with disease will complement other areas of genomic inquiry, lead to a greater biological understanding of disease, and will benefit pediatric nephrology patients.

Evaluation of an ensemble of genetic models for prediction of a quantitative trait

Many genetic markers have been shown to be associated with common quantitative traits in genome-wide association studies. Typically these associated genetic markers have small to modest effect sizes and individually they explain only a small amount of the variability of the phenotype. In order to build a genetic prediction model without fitting a multiple linear regression model with possibly hundreds of genetic markers as predictors, researchers often summarize the joint effect of risk alleles into a genetic score that is used as a covariate in the genetic prediction model. However, the prediction accuracy can be highly variable and selecting the optimal number of markers to be included in the genetic score is challenging. In this manuscript we present a strategy to build an ensemble of genetic prediction models from data and we show that the ensemble-based method makes the challenge of choosing the number of genetic markers more amenable. Using simulated data with varying heritability and number of genetic markers, we compare the predictive accuracy and inclusion of true positive and false positive markers of a single genetic prediction model and our proposed ensemble method. The results show that the ensemble of genetic models tends to include a larger number of genetic variants than a single genetic model and it is more likely to include all of the true genetic markers. This increased sensitivity is obtained at the price of a lower specificity that appears to minimally affect the predictive accuracy of the ensemble.

Improving medical students' knowledge of genetic disease: a review of current and emerging pedagogical practices

Genetics is an essential subject to be mastered by health professional students of all types. However, technological advances in genomics and recent pedagogical research have changed the way in which many medical training programs teach genetics to their students. These advances favor a more experience-based education focused primarily on developing student's critical thinking skills. In this review, we examine the current state of genetics education at both the preclinical and clinical levels and the ways in which medical and pedagogical research have guided reforms to current and emerging teaching practices in genetics. We discover exciting trends taking place in which genetics is integrated with other scientific disciplines both horizontally and vertically across medical curricula to emphasize training in scientific critical thinking skills among students via the evaluation of clinical evidence and consultation of online databases. These trends will produce future health professionals with the skills and confidence necessary to embrace the new tools of medical practice that have emerged from scientific advances in genetics, genomics, and bioinformatics.

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers.

Materials and Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.

Results: The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider.

Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment.