Genetic risks and clinical rewards

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Asthma and the Missing Heritability Problem: Necessity for Multiomics Approaches in Determining Accurate Risk Profiles

Asthma is ranked among the most common chronic conditions and has become a significant public health issue due to the recent and rapid increase in its prevalence. Investigations into the underlying genetic factors predict a heritable component for its incidence, estimated between 35% and 90% of causation. Despite the application of large-scale genome-wide association studies (GWAS) and admixture mapping approaches, the proportion of variants identified accounts for less than 15% of the observed heritability of the disease. The discrepancy between the predicted heritable component of disease and the proportion of heritability mapped to the currently identified susceptibility loci has been termed the ‘missing heritability problem.’ Here, we examine recent studies involving both the analysis of genetically encoded features that contribute to asthma and also the role of non-encoded heritable characteristics, including epigenetic, environmental, and developmental aspects of disease. The importance of vertical maternal microbiome transfer and the influence of maternal immune factors on fetal conditioning in the inheritance of disease are also discussed. In order to highlight the broad array of biological inputs that contribute to the sum of heritable risk factors associated with allergic disease incidence that, together, contribute to the induction of a pro-atopic state. Currently, there is a need to develop in-depth models of asthma risk factors to overcome the limitations encountered in the interpretation of GWAS results in isolation, which have resulted in the missing heritability problem. Hence, multiomics analyses need to be established considering genetic, epigenetic, and functional data to create a true systems biology-based approach for analyzing the regulatory pathways that underlie the inheritance of asthma and to develop accurate risk profiles for disease.

Holy Grail: the journey towards disease modification in asthma

At present, there is no cure for asthma, and treatment typically involves therapies that prevent or reduce asthma symptoms, without modifying the underlying disease. A “disease-modifying” treatment can be classed as able to address the pathogenesis of a disease, preventing progression or leading to a long-term reduction in symptoms. Such therapies have been investigated and approved in other indications, e.g. rheumatoid arthritis and immunoglobulin E-mediated allergic disease. Asthma's heterogeneous nature has made the discovery of similar therapies in asthma more difficult, although novel therapies (e.g. biologics) may have the potential to exhibit disease-modifying properties. To investigate the disease-modifying potential of a treatment, study design considerations can be made, including: appropriate end-point selection, length of trial, age of study population (key differences between adults/children in physiology, pathology and drug metabolism) and comorbidities in the patient population. Potential future focus areas for disease-modifying treatments in asthma include early assessments (e.g. to detect patterns of remodelling) and interventions for patients genetically susceptible to asthma, interventions to prevent virally induced asthma and therapies to promote a healthy microbiome. This review explores the pathophysiology of asthma, the disease-modifying potential of current asthma therapies and the direction future research may take to achieve full disease remission or prevention.

Asthma is a complex, heterogeneous disease, which currently has no cure; this review explores the disease-modifying potential of asthma therapies and the direction future research may take to achieve disease remission or prevention.https://bit.ly/31AxYou

The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits

Type 2 diabetes has a global prevalence, with epidemiological data suggesting that some populations have a higher risk of developing this disease. However, to date, most genetic studies of type 2 diabetes and related glycaemic traits have been performed in individuals of European ancestry. The same is true for most other complex diseases, largely due to use of 'convenience samples'. Rapid genotyping of large population cohorts and case-control studies from existing collections was performed when the genome-wide association study (GWAS) 'revolution' began, back in 2005. Although global representation has increased in the intervening 15 years, further expansion and inclusion of diverse populations in genetic and genomic studies is still needed. In this review, I discuss the progress made in incorporating multi-ancestry participants in genetic analyses of type 2 diabetes and related glycaemic traits, and associated opportunities and challenges. I also discuss how increased representation of global diversity in genetic and genomic studies is required to fulfil the promise of precision medicine for all.

Genetics of coronary artery disease in the post-GWAS era

During the past decade, genome-wide association studies (GWAS) have transformed our understanding of many heritable traits. Three recent large-scale GWAS meta-analyses now further markedly expand the knowledge on coronary artery disease (CAD) genetics in doubling the number of loci with genome-wide significant signals. Here, we review the unprecedented discoveries of CAD GWAS on low-frequency variants, underrepresented populations, sex differences and integrated polygenic risk. We present the milestones of CAD GWAS and post-GWAS studies from 2007 to 2021, and the trend in identification of variants with smaller odds ratio by year due to the increasing sample size. We compile the 321 CAD loci discovered thus far and classify candidate genes as well as distinct functional pathways on the road to indepth biological investigation and identification of novel treatment targets. We draw attention to systems genetics in integrating these loci into gene regulatory networks within and across tissues. We review the traits, biomarkers and diseases scrutinized by Mendelian randomization studies for CAD. Finally, we discuss the potentials and concerns of polygenic scores in predicting CAD risk in patient care as well as future directions of GWAS and post-GWAS studies in the field of precision medicine.

Technological readiness and implementation of genomic-driven precision medicine for complex diseases

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Polygenic Risk Scores

As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.

Risk of Early-Onset Depression Associated With Polygenic Liability, Parental Psychiatric History, and Socioeconomic Status

IMPORTANCE

Combining information on polygenic risk scores (PRSs) with other known risk factors could potentially improve the identification of risk of depression in the general population. However, to our knowledge, no study has estimated the association of PRS with the absolute risk of depression, and few have examined combinations of the PRS and other important risk factors, including parental history of psychiatric disorders and socioeconomic status (SES), in the identification of depression risk.

OBJECTIVE

To assess the individual and joint associations of PRS, parental history, and SES with relative and absolute risk of early-onset depression.

DESIGN, SETTING, AND PARTICIPANTS

This case-cohort study included participants from the iPSYCH2012 sample, a case-cohort sample of all singletons born in Denmark between May 1, 1981, and December 31, 2005. Hazard ratios (HRs) and absolute risks were estimated using Cox proportional hazards regression for case-cohort designs.

EXPOSURES

The PRS for depression; SES measured using maternal educational level, maternal marital status, and paternal employment; and parental history of psychiatric disorders (major depression, bipolar disorder, other mood or psychotic disorders, and other psychiatric diagnoses).

MAIN OUTCOMES AND MEASURES

Hospital-based diagnosis of depression from inpatient, outpatient, or emergency settings.

RESULTS

Participants included 17 098 patients with depression (11 748 [68.7%] female) and 18 582 (9429 [50.7%] male) individuals randomly selected from the base population. The PRS, parental history, and lower SES were all significantly associated with increased risk of depression, with HRs ranging from 1.32 (95% CI, 1.29-1.35) per 1-SD increase in PRS to 2.23 (95% CI, 1.81-2.64) for maternal history of mood or psychotic disorders. Fully adjusted models had similar effect sizes, suggesting that these risk factors do not confound one another. Absolute risk of depression by the age of 30 years differed substantially, depending on an individual's combination of risk factors, ranging from 1.0% (95% CI, 0.1%-2.0%) among men with high SES in the bottom 2% of the PRS distribution to 23.7% (95% CI, 16.6%-30.2%) among women in the top 2% of PRS distribution with a parental history of psychiatric disorders.

CONCLUSIONS AND RELEVANCE

This study suggests that current PRSs for depression are not more likely to be associated with major depressive disorder than are other known risk factors; however, they may be useful for the identification of risk in conjunction with other risk factors.

Genomic Risk Score impact on susceptibility to systemic sclerosis

OBJECTIVES

Genomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those individuals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time.

METHODS

Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model.

RESULTS

The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren's syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693.

CONCLUSIONS

GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.

Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk

Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.

Strategies for Testing Intervention Matching Schemes in Cancer

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogeneity of pathogenic processes underlying most diseases. However, testing and ultimately proving the benefit of strategies or algorithms connecting the mechanisms of action of specific interventions to patient pathophysiological profiles (referred to here as "intervention matching schemes" (IMS)) is complex for many reasons. We argue that IMS are likely to be pervasive, if not ubiquitous, in future health care, but raise important questions about their broad deployment and the contexts within which their utility can be proven. For example, one could question the need to, the efficiency associated with, and the reliability of, strategies for comparing competing or perhaps complementary IMS. We briefly summarize some of the more salient issues surrounding the vetting of IMS in cancer contexts and argue that IMS are at the foundation of many modern clinical trials and intervention strategies, such as basket, umbrella, and adaptive trials. In addition, IMS are at the heart of proposed "rapid learning systems" in hospitals, and implicit in cell replacement strategies, such as cytotoxic T-cell therapies targeting patient-specific neo-antigen profiles. We also consider the need for sensitivity to issues surrounding the deployment of IMS and comment on directions for future research.

Genetic Risk Scores

Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well-established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome-wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.

Towards a Better Classification and Novel Therapies Based on the Genetics of Systemic Sclerosis

PURPOSE OF THE REVIEW

Nowadays, important advances have occurred in our understanding of the pathogenesis of systemic sclerosis (SSc), which is a rare immune-mediated inflammatory disease (IMID) characterized by vascular damage, immune imbalance, and fibrosis. Its etiology remains unknown; nevertheless, both environmental and genetic factors play a major role in the disease. This review will focus on the main advances made in the field of genetics of SSc.

RECENT FINDINGS

The assessment of how interindividual genetic variability affects disease onset and progression has enhanced our knowledge of disease biology, and this will eventually translate in the development of new diagnostic and therapeutic tools, which is the final goal of personalized medicine. We will provide an overview of the most relevant achievements in the genetics of SSc, its shared genetics among IMIDs with special attention on drug repurposing, current challenges for the functional characterization of risk variants, and future directions.

Does big data require a methodological change in medical research?

Background

Use of big data is becoming increasingly popular in medical research. Since big data-based projects differ notably from classical research studies, both in terms of scope and quality, a debate is apt as to whether big data require new approaches to scientific reasoning different from those established in statistics and philosophy of science.

Main text

The progressing digitalization of our societies generates vast amounts of data that also become available for medical research. Here, the big promise of big data is to facilitate major improvements in the treatment, diagnosis and prevention of diseases. An ongoing examination of the idiosyncrasies of big data is therefore essential to ensure that the field stays congruent with the principles of evidence-based medicine. We discuss the inherent challenges and opportunities of big data in medicine from a methodological point of view, particularly highlighting the relative importance of causality and correlation in commercial and medical research settings. We make a strong case for upholding the distinction between exploratory data analysis facilitating hypothesis generation and confirmatory approaches involving hypothesis validation. An independent verification of research results will be ever more important in the context of big data, where data quality is often hampered by a lack of standardization and structuring.

Conclusions

We argue that it would be both unnecessary and dangerous to discard long-established principles of data generation, analysis and interpretation in the age of big data. While many medical research areas may reasonably benefit from big data analyses, they should nevertheless be complemented by carefully designed (prospective) studies.

Artificial Intelligence and Personalized Medicine

The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate 'big data,'  experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition,  the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or 'personalize,' medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for  treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.

Genomic Predictors of Asthma Phenotypes and Treatment Response

Asthma is a complex respiratory disease considered as the most common chronic condition in children. A large genetic contribution to asthma susceptibility is predicted by the clustering of asthma and allergy symptoms among relatives and the large disease heritability estimated from twin studies, ranging from 55 to 90%. Genetic basis of asthma has been extensively investigated in the past 40 years using linkage analysis and candidate-gene association studies. However, the development of dense arrays for polymorphism genotyping has enabled the transition toward genome-wide association studies (GWAS), which have led the discovery of several unanticipated asthma genes in the last 11 years. Despite this, currently known risk variants identified using many thousand samples from distinct ethnicities only explain a small proportion of asthma heritability. This review examines the main findings of the last 2 years in genomic studies of asthma using GWAS and admixture mapping studies, as well as the direction of studies fostering integrative perspectives involving omics data. Additionally, we discuss the need for assessing the whole spectrum of genetic variation in association studies of asthma susceptibility, severity, and treatment response in order to further improve our knowledge of asthma genes and predictive biomarkers. Leveraging the individual's genetic information will allow a better understanding of asthma pathogenesis and will facilitate the transition toward a more precise diagnosis and treatment.