Genetic risk profiling for prediction of type 2 diabetes

GMStool: GWAS-based marker selection tool for genomic prediction from genomic data

The increased accessibility to genomic data in recent years has laid the foundation for studies to predict various phenotypes of organisms based on the genome. Genomic prediction collectively refers to these studies, and it estimates an individual's phenotypes mainly using single nucleotide polymorphism markers. Typically, the accuracy of these genomic prediction studies is highly dependent on the markers used; however, in practice, choosing optimal markers with high accuracy for the phenotype to be used is a challenging task. Therefore, we present a new tool called GMStool for selecting optimal marker sets and predicting quantitative phenotypes. The GMStool is based on a genome-wide association study (GWAS) and heuristically searches for optimal markers using statistical and machine-learning methods. The GMStool performs the genomic prediction using statistical and machine/deep-learning models and presents the best prediction model with the optimal marker-set. For the evaluation, the GMStool was tested on real datasets with four phenotypes. The prediction results showed higher performance than using the entire markers or the GWAS-top markers, which have been used frequently in prediction studies. Although the GMStool has several limitations, it is expected to contribute to various studies for predicting quantitative phenotypes. The GMStool written in R is available at www.github.com/JaeYoonKim72/GMStool .

Precision Health and Nursing: Seeing the Familiar in the Foreign

Precision health is the integration of personal genomic data with biological, environmental, behavioral, and other information relevant to the care of a patient. Genetics and genomics are essential components of precision health. Genetics is the study of the effects of individual genes, and genomics is the study of all the components of the genome and interactions between genes, environmental factors, and other psychosocial and cultural factors. Knowledge about the role of genetics and genomics on health outcomes has increased substantially since the completion of the human genome project in 2003. Insights about genetics and genomics obtained from bench science are now having positive clinical implications on patient health outcomes. Nurses have the potential to make distinct contributions to precision health due to their unique role in the health care system. In this article, we discuss gaps in the development of precision health in nursing and how nursing can expand the definition of precision health to actualize its potential. Precision health plays a role in nursing practice. Understanding this connection positions nurses to incorporate genetic and genomic knowledge into their nursing practice.

Premorbid risk perception, lifestyle, adherence and coping strategies of people with diabetes mellitus: A phenomenological study in the Brong Ahafo Region of Ghana

Background

One of the non-communicable diseases which is on the rise is type 2 diabetes (T2D). T2D is largely preventable with healthy lifestyle. We therefore conducted this study to explore premorbid perception of risk, behavioural practices and the coping strategies of patients with T2D.

Methods

Using descriptive phenomenology approach to qualitative enquiry, we conducted eight focus group discussions (N = 73) with diabetic patients; four among males (N = 36) and four among females (N = 37). In addition, we conducted in-depth interviews with 15 patients, seven caretakers and three physicians. We adopted Colaizzi’s descriptive phenomenology approach to analyse the data with the aid of NVivo 11.

Results

We found that respondents believed diabetes was a condition for the aged and rich and this served as a premorbid risk attenuator. Majority of them engaged in diabetes-related high risk behaviours such as lack of exercise, sedentary lifestyle and unhealthy eating despite their foreknowledge about the role of lifestyle in diabetes pathogenesis. We also found that patients used moringa, noni, prekese, and garlic concurrently with orthodox medications. Adherence to dietary changes and exercises was a challenge with females reporting better adherence than males. The study also revealed that patients believed biomedical health facilities paid little attention to psychosocial aspects of care despite its essential role in coping with the condition.

Conclusion

Diabetic patients had low premorbid perception of risk and engaged in diabetes-related risky behaviours. Diabetic patients had challenges adhering to lifestyle changes and use both biomedical and local remedies in the management of the condition. Psychosocial support is necessary to enhance coping with the condition.

Association of type 2 diabetes mellitus with the interaction between low-density lipoprotein receptor-related protein 5 (LRP5) polymorphisms and overweight and obesity in rural Chinese adults

BACKGROUND

Low-density lipoprotein receptor-related protein 5 (LRP5) plays an important role in glucose and cholesterol metabolism. The present cohort study evaluated associations of LRP5 variants with the incidence of type 2 diabetes mellitus (T2DM) in a rural adult Chinese population.

METHODS

In all, 7751 subjects aged ≥18 years without T2DM underwent genotyping at baseline; 6326 subjects (81.62%) were followed-up, and 5511 with a clear disease outcome were eligible for analysis. The same questionnaire was administered and the same anthropometric and blood biochemical examinations were performed at baseline and follow-up. Association analysis was performed for five single nucleotide polymorphisms and haplotypes of LRP5.

RESULTS

Cox proportional hazards testing of three different genetic models found no significant association between T2DM and LRP5 after adjusting for potential risk factors (P > 0.05). However, the incidence of T2DM in subjects with LRP5 mutational genotypes was higher in the overweight/obese than normal weight population. Under the dominant model, the risk of T2DM was increased with an interaction between rs11228303 and the waist-to-height ratio adjusted for baseline age, sex, and family history of T2DM (synergy index [SI] = 4.172; 95% confidence interval [CI] 1.014-17.166)], and body mass index (SI = 3.237; 95% CI 1.102-9.509). Furthermore, the A allele of rs3758644 was related to decreased fasting plasma insulin and homeostatic model assessment of β-cell function levels, whereas the T allele of rs12363572 was related to increased high-density lipoprotein cholesterol levels in new-onset diabetes patients (P < 0.05).

CONCLUSIONS

The risk of T2DM may be associated with interactions between the LRP5 gene and overweight and obesity. Polymorphisms of LRP5 are related to β-cell function and lipid metabolism.

Lifestyle Advice Combined with Personalized Estimates of Genetic or Phenotypic Risk of Type 2 Diabetes, and Objectively Measured Physical Activity: A Randomized Controlled Trial

BACKGROUND

Information about genetic and phenotypic risk of type 2 diabetes is now widely available and is being incorporated into disease prevention programs. Whether such information motivates behavior change or has adverse effects is uncertain. We examined the effect of communicating an estimate of genetic or phenotypic risk of type 2 diabetes in a parallel group, open, randomized controlled trial.

METHODS AND FINDINGS

We recruited 569 healthy middle-aged adults from the Fenland Study, an ongoing population-based, observational study in the east of England (Cambridgeshire, UK). We used a computer-generated random list to assign participants in blocks of six to receive either standard lifestyle advice alone (control group, n = 190) or in combination with a genetic (n = 189) or a phenotypic (n = 190) risk estimate for type 2 diabetes (intervention groups). After 8 wk, we measured the primary outcome, objectively measured physical activity (kJ/kg/day), and also measured several secondary outcomes (including self-reported diet, self-reported weight, worry, anxiety, and perceived risk). The study was powered to detect a between-group difference of 4.1 kJ/kg/d at follow-up. 557 (98%) participants completed the trial. There were no significant intervention effects on physical activity (difference in adjusted mean change from baseline: genetic risk group versus control group 0.85 kJ/kg/d (95% CI -2.07 to 3.77, p = 0.57); phenotypic risk group versus control group 1.32 (95% CI -1.61 to 4.25, p = 0.38); and genetic risk group versus phenotypic risk group -0.47 (95% CI -3.40 to 2.46, p = 0.75). No significant differences in self-reported diet, self-reported weight, worry, and anxiety were observed between trial groups. Estimates of perceived risk were significantly more accurate among those who received risk information than among those who did not. Key limitations include the recruitment of a sample that may not be representative of the UK population, use of self-reported secondary outcome measures, and a short follow-up period.

CONCLUSIONS

In this study, we did not observe short-term changes in behavior associated with the communication of an estimate of genetic or phenotypic risk of type 2 diabetes. We also did not observe changes in worry or anxiety in the study population. Additional research is needed to investigate the conditions under which risk information might enhance preventive strategies. (Current Controlled Trials ISRCTN09650496; Date applied: April 4, 2011; Date assigned: June 10, 2011).

TRIAL REGISTRATION

The trial is registered with Current Controlled Trials, ISRCTN09650496.

Application of high-dimensional feature selection: evaluation for genomic prediction in man

In this study, we investigated the effect of five feature selection approaches on the performance of a mixed model (G-BLUP) and a Bayesian (Bayes C) prediction method. We predicted height, high density lipoprotein cholesterol (HDL) and body mass index (BMI) within 2,186 Croatian and into 810 UK individuals using genome-wide SNP data. Using all SNP information Bayes C and G-BLUP had similar predictive performance across all traits within the Croatian data, and for the highly polygenic traits height and BMI when predicting into the UK data. Bayes C outperformed G-BLUP in the prediction of HDL, which is influenced by loci of moderate size, in the UK data. Supervised feature selection of a SNP subset in the G-BLUP framework provided a flexible, generalisable and computationally efficient alternative to Bayes C; but careful evaluation of predictive performance is required when supervised feature selection has been used.

Sequence and analysis of a whole genome from Kuwaiti population subgroup of Persian ancestry

Background

The 1000 Genome project paved the way for sequencing diverse human populations. New genome projects are being established to sequence underrepresented populations helping in understanding human genetic diversity. The Kuwait Genome Project an initiative to sequence individual genomes from the three subgroups of Kuwaiti population namely, Saudi Arabian tribe; “tent-dwelling” Bedouin; and Persian, attributing their ancestry to different regions in Arabian Peninsula and to modern-day Iran (West Asia). These subgroups were in line with settlement history and are confirmed by genetic studies. In this work, we report whole genome sequence of a Kuwaiti native from Persian subgroup at >37X coverage.

Results

We document 3,573,824 SNPs, 404,090 insertions/deletions, and 11,138 structural variations. Out of the reported SNPs and indels, 85,939 are novel. We identify 295 ‘loss-of-function’ and 2,314 ’deleterious’ coding variants, some of which carry homozygous genotypes in the sequenced genome; the associated phenotypes include pharmacogenomic traits such as greater triglyceride lowering ability with fenofibrate treatment, and requirement of high warfarin dosage to elicit anticoagulation response. 6,328 non-coding SNPs associate with 811 phenotype traits: in congruence with medical history of the participant for Type 2 diabetes and β-Thalassemia, and of participant’s family for migraine, 72 (of 159 known) Type 2 diabetes, 3 (of 4) β-Thalassemia, and 76 (of 169) migraine variants are seen in the genome. Intergenome comparisons based on shared disease-causing variants, positions the sequenced genome between Asian and European genomes in congruence with geographical location of the region. On comparison, bead arrays perform better than sequencing platforms in correctly calling genotypes in low-coverage sequenced genome regions however in the event of novel SNP or indel near genotype calling position can lead to false calls using bead arrays.

Conclusions

We report, for the first time, reference genome resource for the population of Persian ancestry. The resource provides a starting point for designing large-scale genetic studies in Peninsula including Kuwait, and Persian population. Such efforts on populations under-represented in global genome variation surveys help augment current knowledge on human genome diversity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1233-x) contains supplementary material, which is available to authorized users.

Genetic Counseling for Diabetes Mellitus

Most diabetes is polygenic in etiology, with (type 1 diabetes, T1DM) or without (type 2 diabetes, T2DM) an autoimmune basis. Genetic counseling for diabetes generally focuses on providing empiric risk information based on family history and/or the effects of maternal hyperglycemia on pregnancy outcome. An estimated one to five percent of diabetes is monogenic in nature, e.g., maturity onset diabetes of the young (MODY), with molecular testing and etiology-based treatment available. However, recent studies show that most monogenic diabetes is misdiagnosed as T1DM or T2DM. While efforts are underway to increase the rate of diagnosis in the diabetes clinic, genetic counselors and clinical geneticists are in a prime position to identify monogenic cases through targeted questions during a family history combined with working in conjunction with diabetes professionals to diagnose and assure proper treatment and familial risk assessment for individuals with monogenic diabetes.

Genetic variation at the CYP2C19 gene associated with metabolic syndrome susceptibility in a South Portuguese population: results from the pilot study of the European Health Examination Survey in Portugal

BACKGROUND

Metabolic syndrome (MetS) is a cluster of conditions that occur together, increasing the risk of heart disease, stroke and diabetes. Since pathways implicated in different diseases reveal surprising insights into shared genetic bases underlying apparently unrelated traits, we hypothesize that there are common genetic components involved in the clustering of MetS traits. With the aim of identifying these common genetic components, we have performed a genetic association study by integrating MetS traits in a continuous MetS score.

METHODS

A cross-sectional study developed in the context of the Portuguese Component of the European Health Examination Survey (EHES) was used. Data was collected through a detailed questionnaire and physical examination. Blood samples were collected and biochemical analyses were performed. Waist circumference, blood pressure, glucose, triglycerides and high density lipoprotein cholesterol (HDL) levels were used to compute a continuous MetS score, obtained by Principal Component Analysis. A total of 37 single nucleotide polymorphisms (SNPs) were genotyped and individually tested for association with the score, adjusting for confounding variables.

RESULTS

A total of 206 individuals were studied. Calculated MetS score increased progressively with increasing number of risk factors (P < 0.001). We found a significant association between CYP2C19 rs4244285 and the MetS score not detected using the MetS dichotomic approach. Individuals with the A allelic variant seem to be protected against MetS, displaying a lower MetS score (Mean difference: 0.847; 95%CI: 0.163-1.531; P = 0.015), after adjustment for age, gender, smoking status, excessive alcohol consumption and physical inactivity. An additive genetic effect of GABRA2 rs279871, NPY rs16147 and TPMT rs1142345 in the MetS score variation was also found.

CONCLUSIONS

This is the first report of a genetic association study using a continuous MetS score. The significant association found between the CYP2C19 polymorphism and the MetS score but not with the individual associated traits, emphasizes the importance of lipid metabolism in a MetS common etiological pathway and consequently on the clustering of different cardiovascular risk factors. Despite the sample size limitation of our study, this strategy can be useful to find genetic factors involved in the etiology of other disorders that are defined in a dichotomized way.

Is There a Right Time to Know? The Right Not to Know and Genetic Testing in Children

The increasing implementation of next-generation sequencing technologies in the clinical context and the expanding commercial offer of genetic tests directly-toconsumers has increased the availability of previously inaccessible genetic information. A particular concern in both situations is how the volume of novel information will affect the processing of genetic and genomic information from minors. For minors, it is argued that in the provision of genetic testing, their "right not to know" should be respected as much as possible. Testing a minor early in life eliminates the possibility for the minor to make use of his or her "right not to know." The article discusses the theoretical underpinnings of the right not know, analyzes reasons why various direct-to-consumer companies process samples from minors, and discusses the right not to know in relation to common complex disorders in a pediatric population.

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.

Genetic and genomic testing in clinical practice: what you need to know

The clinical applications of genetic testing are growing rapidly and they now account for a significant percentage of total laboratory testing procedures. Many clinicians are uncomfortable with the types and applications of genetic tests and the dependable resources that are available for self-education. Furthermore, Direct to Consumer genetic testing has presented several challenges to healthcare providers as consumers now have an access to tests that they may not fully understand and results they may act upon inappropriately. This article presents some of the issues and resources to help nurses navigate this changing landscape.

Clinical utility of genetic risk testing in primary care: the example of Type 2 diabetes

Genetic advances in Type 2 diabetes (T2D) have led to the discovery and validation of multiple markers for this complex disease. Despite low predictive value of current T2D markers beyond clinical risk factors and family history, researchers are exploring the clinical utility and outcomes of implementation in practice, and testing is available via direct-to-consumer markets. Clinical utility research demonstrates high hypothetical utility to patients for motivating behavior change and potentially reducing risk. However, trials to date have not demonstrated improvements in behavioral and clinical outcomes over and above counseling based on traditional risk factors. Ongoing research in T2D genetics and associated risk-prediction models is necessary to refine genetic risk pathways, algorithms for risk prediction and use of this information in clinical care. Further research is also needed to explore care models and support interventions that address the needs of personalized risk information and sustainable preventive behaviors to reduce the rising prevalence of T2D.

How can polygenic inheritance be used in population screening for common diseases?

Advances in genomics have near-term impact on diagnosis and management of monogenic disorders. For common complex diseases, the use of genomic information from multiple loci (polygenic model) is generally not useful for diagnosis and individual prediction. In principle, the polygenic model could be used along with other risk factors in stratified population screening to target interventions. For example, compared to age-based criterion for breast, colorectal, and prostate cancer screening, adding polygenic risk and family history holds promise for more efficient screening with earlier start and/or increased frequency of screening for segments of the population at higher absolute risk than an established screening threshold; and later start and/or decreased frequency of screening for segments of the population at lower risks. This approach, while promising, faces formidable challenges for building its evidence base and for its implementation in practice. Currently, it is unclear whether or not polygenic risk can contribute enough discrimination to make stratified screening worthwhile. Empirical data are lacking on population-based age-specific absolute risks combining genetic and non-genetic factors, on impact of polygenic risk genes on disease natural history, as well as information on comparative balance of benefits and harms of stratified interventions. Implementation challenges include difficulties in integration of this information in the current health-care system in the United States, the setting of appropriate risk thresholds, and ethical, legal, and social issues. In an era of direct-to-consumer availability of personal genomic information, the public health and health-care systems need to prepare for an evidence-based integration of this information into population screening.

Phenotype prediction from genome-wide association studies: application to smoking behaviors

Background

A great success of the genome wide association study enabled us to give more attention on the personal genome and clinical application such as diagnosis and disease risk prediction. However, previous prediction studies using known disease associated loci have not been successful (Area Under Curve 0.55 ~ 0.68 for type 2 diabetes and coronary heart disease). There are several reasons for poor predictability such as small number of known disease-associated loci, simple analysis not considering complexity in phenotype, and a limited number of features used for prediction.

Methods

In this research, we investigated the effect of feature selection and prediction algorithm on the performance of prediction method thoroughly. In particular, we considered the following feature selection and prediction methods: regression analysis, regularized regression analysis, linear discriminant analysis, non-linear support vector machine, and random forest. For these methods, we studied the effects of feature selection and the number of features on prediction. Our investigation was based on the analysis of 8,842 Korean individuals genotyped by Affymetrix SNP array 5.0, for predicting smoking behaviors.

Results

To observe the effect of feature selection methods on prediction performance, selected features were used for prediction and area under the curve score was measured. For feature selection, the performances of support vector machine (SVM) and elastic-net (EN) showed better results than those of linear discriminant analysis (LDA), random forest (RF) and simple logistic regression (LR) methods. For prediction, SVM showed the best performance based on area under the curve score. With less than 100 SNPs, EN was the best prediction method while SVM was the best if over 400 SNPs were used for the prediction.

Conclusions

Based on combination of feature selection and prediction methods, SVM showed the best performance in feature selection and prediction.

Finding a place for genomics in health disparities research

The existence of pronounced differences in health outcomes between US populations is a problem of moral significance and public health urgency. Pursuing research on genetic contributors to such disparities, despite striking data on the fundamental role of social factors, has been controversial. Still, advances in genomic science are providing an understanding of disease biology at a level of precision not previously possible. The potential for genomic strategies to help in addressing population-level disparities therefore needs to be carefully evaluated. Using 3 examples from current research, we argue that the best way to maximize the benefits of population-based genomic investigations, and mitigate potential harms, is to direct research away from the identification of genetic causes of disparities and instead focus on applying genomic methodologies to the development of clinical and public health tools with the potential to ameliorate healthcare inequities, direct population-level health interventions or inform public policy. Such a transformation will require close collaboration between transdisciplinary teams and community members as well as a reorientation of current research objectives to better align genomic discovery efforts with public health priorities and well-recognized barriers to fair health care delivery.

Is genetic testing useful to predict type 2 diabetes?

The early identification of individuals at risk for type 2 diabetes (T2D) enables prevention. Recent genome-wide association studies (GWAS) have added at least 40 genetic variants to the list of already well characterized T2D risk predictors, including family history, obesity, and elevated fasting plasma glucose levels. Although these variants can significantly predict T2D alone and as a part of genotype risk scores, they do not yet offer clinical discrimination beyond that achieved with common clinical measurements. Future progress on at least two research fronts may improve the predictive performance of genotype information. First, expanded GWAS efforts in non-European populations will allow targeted sequencing of risk loci and the identification of true causal variants. Second, studies with longer prediction time horizons may demonstrate that genotype information performs better than clinical risk predictors over a longer period of the life course. At present, however, genetic testing cannot be recommended for clinical T2D risk prediction in adults.

The role of disease characteristics in the ethical debate on personal genome testing

BACKGROUND

Companies are currently marketing personal genome tests directly-to-consumer that provide genetic susceptibility testing for a range of multifactorial diseases simultaneously. As these tests comprise multiple risk analyses for multiple diseases, they may be difficult to evaluate. Insight into morally relevant differences between diseases will assist researchers, healthcare professionals, policy-makers and other stakeholders in the ethical evaluation of personal genome tests.

DISCUSSION

In this paper, we identify and discuss four disease characteristics--severity, actionability, age of onset, and the somatic/psychiatric nature of disease--and show how these lead to specific ethical issues. By way of illustration, we apply this framework to genetic susceptibility testing for three diseases: type 2 diabetes, age-related macular degeneration and clinical depression. For these three diseases, we point out the ethical issues that are relevant to the question whether it is morally justifiable to offer genetic susceptibility testing to adults or to children or minors, and on what conditions.

SUMMARY

We conclude that the ethical evaluation of personal genome tests is challenging, for the ethical issues differ with the diseases tested for. An understanding of the ethical significance of disease characteristics will improve the ethical, legal and societal debate on personal genome testing.

Effect of genetic testing for risk of type 2 diabetes mellitus on health behaviors and outcomes: study rationale, development and design

Abstract

Trial Registration

ClinicalTrials.gov: NCT00849563

Five years of GWAS discovery

The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.

A methodological perspective on genetic risk prediction studies in type 2 diabetes: recommendations for future research

Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions.