Prediction and interaction in complex disease genetics: experience in type 1 diabetes

Role of triggering receptor expressed on myeloid cells-1 in kidney diseases: A biomarker and potential therapeutic target

ABSTRACT

Triggering receptor expressed on myeloid cells-1 (TREM-1) is a member of the immunoglobulin superfamily. As an amplifier of the inflammatory response, TREM-1 is mainly involved in the production of inflammatory mediators and the regulation of cell survival. TREM-1 has been studied in infectious diseases and more recently in non-infectious disorders. More and more studies have shown that TREM-1 plays an important pathogenic role in kidney diseases. There is evidence that TREM-1 can not only be used as a biomarker for diagnosis of disease but also as a potential therapeutic target to guide the development of novel therapeutic agents for kidney disease. This review summarized molecular biology of TREM-1 and its signaling pathways as well as immune response in the progress of acute kidney injury, renal fibrosis, diabetic nephropathy, immune nephropathy, and renal cell carcinoma.

Gene-Environment Interactions for Parkinson's Disease

OBJECTIVE

Parkinson's disease (PD) is a neurodegenerative disorder with complex etiology. Multiple genetic and environmental factors have been associated with PD, but most PD risk remains unexplained. The aim of this study was to test for statistical interactions between PD-related genetic and environmental exposures in the 23andMe, Inc. research dataset.

METHODS

Using a validated PD polygenic risk score and common PD-associated variants in the GBA gene, we explored interactions between genetic susceptibility factors and 7 lifestyle and environmental factors: body mass index (BMI), type 2 diabetes (T2D), tobacco use, caffeine consumption, pesticide exposure, head injury, and physical activity (PA).

RESULTS

We observed that T2D, as well as higher BMI, caffeine consumption, and tobacco use, were associated with lower odds of PD, whereas head injury, pesticide exposure, GBA carrier status, and PD polygenic risk score were associated with higher odds. No significant association was observed between PA and PD. In interaction analyses, we found statistical evidence for an interaction between polygenic risk of PD and the following environmental/lifestyle factors: T2D (p = 6.502 × 10-8), PA (p = 8.745 × 10-5), BMI (p = 4.314 × 10-4), and tobacco use (p = 2.236 × 10-3). Although BMI and tobacco use were associated with lower odds of PD regardless of the extent of individual genetic liability, the direction of the relationship between odds of PD and T2D, as well as PD and PA, varied depending on polygenic risk score.

INTERPRETATION

We provide preliminary evidence that associations between some environmental and lifestyle factors and PD may be modified by genotype. ANN NEUROL 2024;95:677-687.

Modifiable risk factors for multiple sclerosis have consistent directions of effect across diverse ethnic backgrounds: a nested case-control study in an English population-based cohort

BACKGROUND

Multiple sclerosis is a leading cause of non-traumatic neurological disability among young adults worldwide. Prior studies have identified modifiable risk factors for multiple sclerosis in cohorts of White ethnicity, such as infectious mononucleosis, smoking, and obesity during adolescence/early adulthood. It is unknown whether modifiable exposures for multiple sclerosis have a consistent impact on risk across ethnic groups.

AIM

To determine whether modifiable risk factors for multiple sclerosis have similar effects across diverse ethnic backgrounds.

METHODS

We conducted a nested case-control study using data from the UK Clinical Practice Research Datalink. Multiple sclerosis cases diagnosed from 2001 until 2022 were identified from electronic healthcare records and matched to unaffected controls based on year of birth. We used stratified logistic regression models and formal statistical interaction tests to determine whether the effect of modifiable risk factors for multiple sclerosis differed by ethnicity.

RESULTS

We included 9662 multiple sclerosis cases and 118,914 age-matched controls. The cohort was ethnically diverse (MS: 277 South Asian [2.9%], 251 Black [2.6%]; Controls: 5043 South Asian [5.7%], 4019 Black [4.5%]). The age at MS diagnosis was earlier in the Black (40.5 [SD 10.9]) and Asian (37.2 [SD 10.0]) groups compared with White cohort (46.1 [SD 12.2]). There was a female predominance in all ethnic groups; however, the relative proportion of males was higher in the South Asian population (proportion of women 60.3% vs 71% [White] and 75.7% [Black]). Established modifiable risk factors for multiple sclerosis-smoking, obesity, infectious mononucleosis, low vitamin D, and head injury-were consistently associated with multiple sclerosis in the Black and South Asian cohorts. The magnitude and direction of these effects were broadly similar across all ethnic groups examined. There was no evidence of statistical interaction between ethnicity and any tested exposure, and no evidence to suggest that differences in area-level deprivation modifies these risk factor-disease associations. These findings were robust to a range of sensitivity analyses.

CONCLUSIONS AND RELEVANCE

Established modifiable risk factors for multiple sclerosis are applicable across diverse ethnic backgrounds. Efforts to reduce the population incidence of multiple sclerosis by tackling these risk factors need to be inclusive of people from diverse ethnicities.

Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks

BACKGROUND

The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered.

DEVELOPMENT

We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors.

APPLICATION

For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors.

CONCLUSION

VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.

Polygenic scores in cancer

Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.

Genome-wide aggregated trans-effects on risk of type 1 diabetes: A test of the "omnigenic" sparse effector hypothesis of complex trait genetics

The "omnigenic" hypothesis postulates that the polygenic effects of common SNPs on a typical complex trait are mediated through trans-effects on expression of a relatively sparse set of effector ("core") genes. We tested this hypothesis in a study of 4,964 cases of type 1 diabetes (T1D) and 7,497 controls by using summary statistics to calculate aggregated (excluding the HLA region) trans-scores for gene expression in blood. From associations of T1D with aggregated trans-scores, nine putative core genes were identified, of which three-STAT1, CTLA4 and FOXP3-are genes in which variants cause monogenic forms of autoimmune diabetes. Seven of these genes affect the activity of regulatory T cells, and two are involved in immune responses to microbial lipids. Four T1D-associated genomic regions could be identified as master regulators via trans-effects on gene expression. These results support the sparse effector hypothesis and reshape our understanding of the genetic architecture of T1D.

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores

Background

Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20 weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.

Methods

We identified a cohort of N=1,125 pregnant individuals who delivered between 05/2015-05/2022 at Mass General Brigham hospitals with available electronic health record (EHR) data and linked genetic data. Using clinical EHR data and systolic blood pressure polygenic risk scores (SBP PRS) derived from a large genome-wide association study, we developed machine learning (xgboost) and linear regression models to predict preeclampsia risk.

Results

Pregnant individuals with an SBP PRS in the top quartile had higher blood pressures throughout pregnancy compared to patients within the lowest quartile SBP PRS. In the first trimester, the most predictive model was xgboost, with an area under the curve (AUC) of 0.73. Adding the SBP PRS to the models improved the performance only of the linear regression model from AUC 0.70 to 0.71; the predictive power of other models remained unchanged. In late pregnancy, with data obtained up to the delivery admission, the best performing model was xgboost using clinical variables, which achieved an AUC of 0.91.

Conclusions

Integrating clinical and genetic factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented in clinical practice to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

Statistical Methods for Disease Risk Prediction with Genotype Data

Single-nucleotide polymorphism (SNP) is the basic unit to understand the heritability of complex traits. One attractive application of the susceptible SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.

Childhood type 1 diabetes mellitus and risk factor of interactions between dietary cow's milk intake and HLA-DR3/DR4 genotype

Short-term breastfeeding and early exposure to dairy products into infant diets, may be critical factors for development of type 1 diabetes. In this study, we investigate whether cow's milk proteins are risk factors for type 1 diabetes in genetically susceptible individuals (HLA DR3/DR4) by using statistical analysis and in silico approach. In order to verify the potential risk of the early introduction of cow's milk, we conducted this study to validate the veracity of this hypothesis in our population. We included 121 subjects, 55 type 1 diabetics and 74 controls from the region of Tlemcen (Algeria). Thus, the in silico approach was performed to determine the molecular mimicry region between Bovine serum albumin and beta-lactoglobulin with self-Islet antigen 2 and glutamate decarboxylase 65 by determining their sequences and their 3D structures. The risk factors associated with type 1 diabetes in a genetically predisposed individual (HLA DR3/DR4) retained by the logistic model are: type 1 and type 2 diabetes inheritance, the early introduction of cow's milk before 6 months and breastfeeding less than 9 months. Besides, the epitopes of cow's milk proteins have the capacity to bind to predisposing HLA class II molecules (HLA DR3/DR4) and induce an immune reaction by the secretion of Interleukin 4 (Th2) and Interferon (Th1) which lead to the destruction of pancreatic beta cells. The early introduction of cow's milk proteins in susceptible individuals is considered as risk factors for the pathogenesis of T1DM. The in silico approach confirm that BSA and BLG share sequence and structure homology with IA2 and GAD65.Communicated by Ramaswamy H. Sarma.

Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study

BACKGROUND

The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort.

METHODS

We validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45-63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes: BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D and BARD1. Calibration was assessed by comparing observed and expected risks in deciles of predicted risk and the calibration slope. The discriminatory ability was assessed using the area under the curve (AUC).

RESULTS

Among the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%).

CONCLUSION

The multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening.

Environmental Influences on the Relation between the 22q11.2 Deletion Syndrome and Mental Health: A Literature Review

22q11.2 deletion syndrome (22q11DS) is a clinically heterogeneous genetic syndrome, associated with a wide array of neuropsychiatric symptoms. The clinical presentation is likely to be influenced by environmental factors, yet little is known about this. Here, we review the available research literature on the role of the environment in 22q11DS. We find that within-patient design studies have mainly investigated the role of parental factors, stress, and substance use, reporting significant effects of these factors on the clinical profile. Case-control studies have been less successful, with almost no reports of significant moderating effects of the environment. We go on to hypothesize which specific environmental measures are most likely to interact with the 22q11 deletion, based on the genes in this region and their involvement in molecular pathways. We end by discussing potential reasons for the limited findings so far, including modest sample sizes and limited availability of environmental measures, and make recommendations how to move forward.

NAFLD: genetics and its clinical implications

Worldwide non-alcoholic fatty liver disease (NAFLD) is recognized as the most common type of liver disease and its burden increasing at an alarming rate. NAFLD entails steatosis, fibrosis, cirrhosis, and, finally, hepatocellular carcinoma (HCC). The substantial inter-patient variation during disease progression is the hallmark of individuals with NAFLD. The variability of NAFLD development and related complications among individuals is determined by genetic and environmental factors. Genome-wide association studies (GWAS) have discovered reproducible and robust associations between gene variants such as PNPLA3, TM6SF2, HSD17B13, MBOAT7, GCKR and NAFLD. Evidences have provided the new insights into the NAFLD biology and underlined potential pharmaceutical targets. Ideally, the candidate genes associated with the hereditability of NAFLD are mainly involved in assembly of lipid droplets, lipid remodeling, lipoprotein packing and secretion, redox status mitochondria, and de novo lipogenesis. In recent years, the ability to translate genetics into a clinical context has emerged substantially by combining genetic variants primarily associated with NAFLD into polygenic risk scores (PRS). These score in combination with metabolic factors could be utilized to identify the severe liver diseases in patients with the gene regulatory networks (GRNs). Hereby, we even have highlighted the current understanding related to the schedule therapeutic approach of an individual based on microbial colonization and dysbiosis reversal as a therapy for NAFLD. The premise of this review is to concentrate on the potential of genetic factors and their translation into the design of novel therapeutics, as well as their implications for future research into personalized medications using microbiota.

Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives

Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.

3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk

Three-dimensional (3D) chromatin organization maps help dissect cell-type-specific gene regulatory programs. Furthermore, 3D chromatin maps contribute to elucidating the pathogenesis of complex genetic diseases by connecting distal regulatory regions and genetic risk variants to their respective target genes. To understand the cell-type-specific regulatory architecture of diabetes risk, we generated transcriptomic and 3D epigenomic profiles of human pancreatic acinar, alpha, and beta cells using single-cell RNA-seq, single-cell ATAC-seq, and high-resolution Hi-C of sorted cells. Comparisons of these profiles revealed differential A/B (open/closed) chromatin compartmentalization, chromatin looping, and transcriptional factor-mediated control of cell-type-specific gene regulatory programs. We identified a total of 4,750 putative causal-variant-to-target-gene pairs at 194 type 2 diabetes GWAS signals using pancreatic 3D chromatin maps. We found that the connections between candidate causal variants and their putative target effector genes are cell-type stratified and emphasize previously underappreciated roles for alpha and acinar cells in diabetes pathogenesis.

Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk

Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05−0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.

Genetic Variants Associated with Neuropeptide Y Autoantibody Levels in Newly Diagnosed Individuals with Type 1 Diabetes

(1) Autoantibodies to the leucine variant of neuropeptide Y (NPY-LA) have been found in individuals with type 1 diabetes (T1D). We investigated the association between the levels of NPY-LA and single nucleotide polymorphisms (SNP) to better understand the genetic regulatory mechanisms of autoimmunity in T1D and the functional impacts of increased NPY-LA levels. (2) NPY-LA measurements from serum and SNP genotyping were done on 560 newly diagnosed individuals with T1D. SNP imputation with the 1000 Genomes reference panel was followed by an association analysis between the SNPs and measured NPY-LA levels. Additionally, functional enrichment and pathway analyses were done. (3) Three loci (DGKH, DCAF5, and LINC02261) were associated with NPY-LA levels (p-value < 1.5 × 10−6), which indicates an association with neurologic and vascular disorders. SNPs associated with variations in expression levels were found in six genes (including DCAF5). The pathway analysis showed that NPY-LA was associated with changes in gene transcription, protein modification, immunological functions, and the MAPK pathway. (4) Conclusively, we found NPY-LA to be significantly associated with three loci (DGKH, DCAF5, and LINC02261), and based on our findings we hypothesize that the presence of NPY-LA is associated with the regulation of the immune system and possibly neurologic and vascular disorders.

Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk

Simple Summary

Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) that predict a woman’s risk of breast cancer based on mammographically dense areas when defined by different levels of brightness. We measured these MRS for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated how much these MRSs were correlated in relatives (ρ), how much of the differences between women were due to genetic factors (heritability), and how much these MRS explained why breast cancer runs in families. The ρ estimates ranged from: 0.41 to 0.60 for MZ pairs, 0.16 to 0.26 for DZ pairs, and 0.19 to 0.29 sister pairs, respectively. Heritability estimates were 36% to 69%. Genetic factors explain most of why twins and sisters are similar in their MRS, and these genetic factors explain one-quarter to one-half as much breast cancer risk as to the current best genetic risk score.

Abstract

Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score.

In vitro beta-cell killing models using immune cells and human pluripotent stem cell-derived islets: Challenges and opportunities

Type 1 diabetes (T1D) is a disease of both autoimmunity and β-cells. The β-cells play an active role in their own demise by mounting defense mechanisms that are insufficient at best, and that can become even deleterious in the long term. This complex crosstalk is important to understanding the physiological defense mechanisms at play in healthy conditions, their alterations in the T1D setting, and therapeutic agents that may boost such mechanisms. Robust protocols to develop stem-cell-derived islets (SC-islets) from human pluripotent stem cells (hPSCs), and islet-reactive cytotoxic CD8⁺ T-cells from peripheral blood mononuclear cells offer unprecedented opportunities to study this crosstalk. Challenges to develop in vitro β-cell killing models include the cluster morphology of SC-islets, the relatively weak cytotoxicity of most autoimmune T-cells and the variable behavior of in vitro expanded CD8⁺ T-cells. These challenges may however be highly rewarding in light of the opportunities offered by such models. Herein, we discuss these opportunities including: the β-cell/immune crosstalk in an islet microenvironment; the features that make β-cells more sensitive to autoimmunity; therapeutic agents that may modulate β-cell vulnerability; and the possibility to perform analyses in an autologous setting, i.e., by generating T-cell effectors and SC-islets from the same donor.

Problems and promises: How to tell the story of a Genome Wide Association Study?

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The 'sociology of expectations' has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the 'problematic' found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews.

Genetic overlap between type 1 diabetes and other autoimmune diseases

Type 1 diabetes (T1D) is a chronic disease caused by the destruction of pancreatic β cells, which is driven by autoreactive T lymphocytes. It has been described that a high proportion of T1D patients develop other autoimmune diseases (AIDs), such as autoimmune thyroid disease, celiac disease, or vitiligo, which suggests the existence of common etiological factors among these disorders. In this regard, genetic studies have identified a high number of loci consistently associated with T1D that also represent established genetic risk factors for other AIDs. In addition, studies focused on identifying the shared genetic component in autoimmunity have described several common susceptibility loci with a potential role in T1D. Elucidation of this genetic overlap has been useful in identifying key molecular pathways with a pathogenic role in multiple disorders. In this review, we summarize recent advances in understanding the shared genetic component between T1D and other AIDs and discuss how the identification of common pathogenic mechanisms can help in the development of new therapeutic approaches as well as in improving the use of existing drugs.

A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

Diagnosis and treatment of type 1 diabetes at the dawn of the personalized medicine era

Type 1 diabetes affects millions of people globally and requires careful management to avoid serious long-term complications, including heart and kidney disease, stroke, and loss of sight. The type 1 diabetes patient cohort is highly heterogeneous, with individuals presenting with disease at different stages and severities, arising from distinct etiologies, and overlaying varied genetic backgrounds. At present, the “one-size-fits-all” treatment for type 1 diabetes is exogenic insulin substitution therapy, but this approach fails to achieve optimal blood glucose control in many individuals. With advances in our understanding of early-stage diabetes development, diabetes stratification, and the role of genetics, type 1 diabetes is a promising candidate for a personalized medicine approach, which aims to apply “the right therapy at the right time, to the right patient”. In the case of type 1 diabetes, great efforts are now being focused on risk stratification for diabetes development to enable pre-clinical detection, and the application of treatments such as gene therapy, to prevent pancreatic destruction in a sub-set of patients. Alongside this, breakthroughs in stem cell therapies hold great promise for the regeneration of pancreatic tissues in some individuals. Here we review the recent initiatives in the field of personalized medicine for type 1 diabetes, including the latest discoveries in stem cell and gene therapy for the disease, and current obstacles that must be overcome before the dream of personalized medicine for all type 1 diabetes patients can be realized.

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.

Gluten Free Diet for the Management of Non Celiac Diseases: The Two Sides of the Coin

A lifelong adherence to a gluten-free (GF) diet is currently the only treatment for Celiac disease (CD), an autoimmune disorder that arises after gluten ingestion in individuals who are genetically predisposed. The gluten intake exerts toxic effects through several pathways involving gut barrier integrity, intestinal microbiota composition and immune system stimulation. However, despite the great benefit of GF diet for CD patients, its use has been debated. Indeed, individuals who adopt this diet regime may be at risk of nutrient deficiencies. Emerging evidence supports a beneficial effect of a GF diet also for other pathological conditions, including gluten-related disorders (GRD) often associated to CD, such as Non celiac gluten sensitivity (NCGS) and Dermatitis Herpetiforme (DH) as well as Irritable bowel syndrome (IBS) and Diabetes. This suggests a pathogenic role of gluten in these conditions. Despite the growing popularity of GF diet among consumers, to date, there are limited evidences supporting its use for the management of non-celiac diseases. Therefore, in this review, we discuss whether the GF diet could really improve the general quality of life of patients with GRD and non-GRD conditions, keeping in mind its sensorial limitations and nutritional inadequacies. In addition, we discuss the current motivations, leading to the use of a GF diet, despite the inferior quality of GF products respect to those containing gluten.

Prediction of mild parkinsonism revealed by neural oscillatory changes and machine learning

Neural oscillatory changes within and across different frequency bands are thought to underlie motor dysfunction in Parkinson's disease (PD) and may serve as biomarkers for closed-loop deep brain stimulation (DBS) approaches. Here, we used neural oscillatory signals derived from chronically implanted cortical and subcortical electrode arrays as features to train machine learning algorithms to discriminate between naive and mild PD states in a nonhuman primate model. Local field potential (LFP) data were collected over several months from a 12-channel subdural electrocorticography (ECoG) grid and a 6-channel custom array implanted in the subthalamic nucleus (STN). Relative to the naive state, the PD state showed elevated primary motor cortex (M1) and STN power in the beta, high gamma, and high-frequency oscillation (HFO) bands and decreased power in the delta band. Theta power was found to be decreased in STN but not M1. In the PD state there was elevated beta-HFO phase-amplitude coupling (PAC) in the STN. We applied machine learning with support vector machines with radial basis function (SVM-RBF) kernel and k-nearest neighbors (KNN) classifiers trained by features related to power and PAC changes to discriminate between the naive and mild states. Our results show that the most predictive feature of parkinsonism in the STN was high beta (∼86% accuracy), whereas it was HFO in M1 (∼98% accuracy). A feature fusion approach outperformed every individual feature, particularly in the M1, where ∼98% accuracy was achieved with both classifiers. Overall, our data demonstrate the ability to use various frequency band power to classify the clinical state and are also beneficial in developing closed-loop DBS therapeutic approaches.NEW & NOTEWORTHY Neurophysiological biomarkers that correlate with motor symptoms or disease severity are vital to improve our understanding of the pathophysiology in Parkinson's disease (PD) and for the development of more effective treatments, including deep brain stimulation (DBS). This work provides direct insight into the application of these biomarkers in training classifiers to discriminate between brain states, which is a first step toward developing closed-loop DBS systems.

Tutorial: a guide to performing polygenic risk score analyses

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.

Regulation of inflammation in diabetes: From genetics to epigenomics evidence

Background

Diabetes is one of the greatest public health challenges worldwide, and we still lack complementary approaches to significantly enhance the efficacy of preventive and therapeutic approaches. Genetic and environmental factors are the culprits involved in diabetes risk. Evidence from the last decade has highlighted that deregulation in the immune and inflammatory responses increase susceptibility to type 1 and type 2 diabetes. Spatiotemporal patterns of gene expression involved in immune cell polarisation depend on genomic enhancer elements in response to inflammatory and metabolic cues. Several studies have reported that most regulatory genetic variants are located in the non-protein coding regions of the genome and particularly in enhancer regions. The progress of high-throughput technologies has permitted the characterisation of enhancer chromatin properties. These advances support the concept that genetic alteration of enhancers may influence the immune and inflammatory responses in relation to diabetes.

Scope of review

Results from genome-wide association studies (GWAS) combined with functional and integrative analyses have elucidated the impacts of some diabetes risk-associated variants that are involved in the regulation of the immune system. Additionally, genetic variant mapping to enhancer regions may alter enhancer status, which in turn leads to aberrant expression of inflammatory genes associated with diabetes susceptibility. The focus of this review was to provide an overview of the current indications that inflammatory processes are regulated at the genetic and epigenomic levels in diabetes, along with perspectives on future research avenues that may improve understanding of the disease.

Major conclusions

In this review, we provide genetic evidence in support of a deregulated immune response as a risk factor in diabetes. We also argue about the importance of enhancer regions in the regulation of immune cell polarisation and how the recent advances using genome-wide methods for enhancer identification have enabled the determination of the impact of enhancer genetic variation on diabetes onset and phenotype. This could eventually lead to better management plans and improved treatment responses in human diabetes.

Update on NAFLD genetics: From new variants to the clinic

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver diseases in high-income countries and the burden of NAFLD is increasing at an alarming rate. The risk of developing NAFLD and related complications is highly variable among individuals and is determined by environmental and genetic factors. Genome-wide association studies have uncovered robust and reproducible associations between variations in genes such as PNPLA3, TM6SF2, MBOAT7, GCKR, HSD17B13 and the natural history of NAFLD. These findings have provided compelling new insights into the biology of NAFLD and highlighted potentially attractive pharmaceutical targets. More recently the development of polygenic risk scores, which have shown promising results for the clinical risk prediction of other complex traits (such as cardiovascular disease and breast cancer), have provided new impetus for the clinical validation of genetic variants in NAFLD risk stratification. Herein, we review current knowledge on the genetic architecture of NAFLD, including gene-environment interactions, and discuss the implications for disease pathobiology, drug discovery and risk prediction. We particularly focus on the potential clinical translation of recent genetic advances, discussing methodological hurdles that must be overcome before these discoveries can be implemented in everyday practice.

Mitonuclear conflict and cooperation govern the integration of genotypes, phenotypes and environments

The mitonuclear genome is the most successful co-evolved mutualism in the history of life on Earth. The cross-talk between the mitochondrial and nuclear genomes has been shaped by conflict and cooperation for more than 1.5 billion years, yet this system has adapted to countless genomic reorganizations by each partner, and done so under changing environments that have placed dramatic biochemical and physiological pressures on evolving lineages. From putative anaerobic origins, mitochondria emerged as the defining aerobic organelle. During this transition, the two genomes resolved rules for sex determination and transmission that made uniparental inheritance the dominant, but not a universal pattern. Mitochondria are much more than energy-producing organelles and play crucial roles in nutrient and stress signalling that can alter how nuclear genes are expressed as phenotypes. All of these interactions are examples of genotype-by-environment (GxE) interactions, gene-by-gene (GxG) interactions (epistasis) or more generally context-dependent effects on the link between genotype and phenotype. We provide evidence from our own studies in Drosophila, and from those of other systems, that mitonuclear interactions—either conflicting or cooperative—are common features of GxE and GxG. We argue that mitonuclear interactions are an important model for how to better understand the pervasive context-dependent effects underlying the architecture of complex phenotypes. Future research in this area should focus on the quantitative genetic concept of effect size to place mitochondrial links to phenotype in a proper context.

This article is part of the theme issue ‘Linking the mitochondrial genotype to phenotype: a complex endeavour’.

The Genetic Contribution to Type 1 Diabetes

PURPOSE OF REVIEW

To provide an updated summary of discoveries made to date resulting from genome-wide association study (GWAS) and sequencing studies, and to discuss the latest loci added to the growing repertoire of genetic signals predisposing to type 1 diabetes (T1D).

RECENT FINDINGS

Genetic studies have identified over 60 loci associated with T1D susceptibility. GWAS alone does not specifically inform on underlying mechanisms, but in combination with other sequencing and omics-data, advances are being made in our understanding of T1D genetic etiology and pathogenesis. Current knowledge indicates that genetic variation operating in both pancreatic β cells and in immune cells is central in mediating T1D risk. One of the main challenges is to determine how these recently discovered GWAS-implicated variants affect the expression and function of gene products. Once we understand the mechanism of action for disease-causing variants, we will be well placed to apply targeted genomic approaches to impede the premature activation of the immune system in an effort to ultimately prevent the onset of T1D.

Translational genomics and precision medicine: Moving from the lab to the clinic

Translational genomics aims to improve human health by building on discoveries made through genetics research and applying them in the clinical setting. This progress has been made possible by technological advances in genomics and analytics and by the digital revolution. Such advances should enable the development of prognostic markers, tailored interventions, and the design of prophylactic preventive approaches. We are at the cusp of predicting disease risk for some disorders by means of polygenic risk scores integrated with classical epidemiological risk factors. This should lead to better risk stratification and clinical decision-making. A deeper understanding of the link between genome-wide sequence and association with well-characterized phenotypes will empower the development of biomarkers to aid diagnosis, inform disease progression trajectories, and allow better targeting of treatments to those patients most likely to respond.

Identification of Novel T1D Risk Loci and Their Association With Age and Islet Function at Diagnosis in Autoantibody-Positive T1D Individuals: Based on a Two-Stage Genome-Wide Association Study

OBJECTIVE

Type 1 diabetes (T1D) is a highly heritable disease with much lower incidence but more adult-onset cases in the Chinese population. Although genome-wide association studies (GWAS) have identified >60 T1D loci in Caucasians, less is known in Asians.

RESEARCH DESIGN AND METHODS

We performed the first two-stage GWAS of T1D using 2,596 autoantibody-positive T1D case subjects and 5,082 control subjects in a Chinese Han population and evaluated the associations between the identified T1D risk loci and age and fasting C-peptide levels at T1D diagnosis.

RESULTS

We observed a high genetic correlation between children/adolescents and adult T1D case subjects (r _g = 0.87), as well as subgroups of autoantibody status (r _g ≥ 0.90). We identified four T1D risk loci reaching genome-wide significance in the Chinese Han population, including two novel loci, rs4320356 near BTN3A1 (odds ratio [OR] 1.26, P = 2.70 × 10^-8) and rs3802604 in GATA3 (OR 1.24, P = 2.06 × 10^-8), and two previously reported loci, rs1770 in MHC (OR 4.28, P = 2.25 × 10^-232) and rs705699 in SUOX (OR 1.46, P = 7.48 × 10^-20). Further fine mapping in the MHC region revealed five independent variants, including another novel locus, HLA-C position 275 (omnibus P = 9.78 × 10^-12), specific to the Chinese population. Based on the identified eight variants, we achieved an area under the curve value of 0.86 (95% CI 0.85-0.88). By building a genetic risk score (GRS) with these variants, we observed that the higher GRS were associated with an earlier age of T1D diagnosis (P = 9.08 × 10^-11) and lower fasting C-peptide levels (P = 7.19 × 10^-3) in individuals newly diagnosed with T1D.

CONCLUSIONS

Our results extend current knowledge on genetic contributions to T1D risk. Further investigations in different populations are needed for genetic heterogeneity and subsequent precision medicine.

New insights on the genetics of type 1 diabetes

PURPOSE OF REVIEW

The genetic risk for type 1 diabetes has been studied for over half a century, with the strong genetic associations of type 1 diabetes forming critical evidence for the role of the immune system in pathogenesis. In this review, we discuss some of the original research leading to recent developments in type 1 diabetes genetics.

RECENT FINDINGS

We examine the translation of polygenic scores for type 1 diabetes into tools for prediction and diagnosis of type 1 diabetes, in particular, when used in combination with other biomarkers and clinical features, such as age and islet-specific autoantibodies. Furthermore, we review the description of age associations with type 1 diabetes genetic risk, and the investigation of loci linked to type 2 diabetes in progression of type 1 diabetes. Finally, we consider current limitations, including the scarcity of data from racial and ethnic minorities, and future directions.

SUMMARY

The development of polygenic risk scores has allowed the integration of type 1 diabetes genetics into diagnosis and prediction. Emerging information on the role of specific genes in subgroups of individuals with the disease, for example, early-onset, mild autoimmunity, and so forth, is facilitating our understanding of the heterogeneity of type 1 diabetes, with the ultimate goal of using genetic information in research and clinical practice.

Machine Learning SNP Based Prediction for Precision Medicine

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

Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis

OBJECTIVE

Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.

RESEARCH DESIGN AND METHODS

In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.

RESULTS

The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; P < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.

CONCLUSIONS

An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

Possible Prevention of Diabetes with a Gluten-Free Diet

Gluten seems a potentially important determinant in type 1 diabetes (T1D) and type 2 diabetes (T2D). Intake of gluten, a major component of wheat, rye, and barley, affects the microbiota and increases the intestinal permeability. Moreover, studies have demonstrated that gluten peptides, after crossing the intestinal barrier, lead to a more inflammatory milieu. Gluten peptides enter the pancreas where they affect the morphology and might induce beta-cell stress by enhancing glucose- and palmitate-stimulated insulin secretion. Interestingly, animal studies and a human study have demonstrated that a gluten-free (GF) diet during pregnancy reduces the risk of T1D. Evidence regarding the role of a GF diet in T2D is less clear. Some studies have linked intake of a GF diet to reduced obesity and T2D and suggested a role in reducing leptin- and insulin-resistance and increasing beta-cell volume. The current knowledge indicates that gluten, among many environmental factors, may be an aetiopathogenic factors for development of T1D and T2D. However, human intervention trials are needed to confirm this and the proposed mechanisms.

The characteristics and pivotal roles of triggering receptor expressed on myeloid cells-1 in autoimmune diseases

Triggering receptor expressed on myeloid cells-1 (TREM-1) engagement can directly trigger inflammation or amplify an inflammatory response by synergizing with TLRs or NLRs. Autoimmune diseases are a family of chronic systemic inflammatory disorders. The pivotal role of TREM-1 in inflammation makes it important to explore its immunological effects in autoimmune diseases. In this review, we summarize the structural and functional characteristics of TREM-1. Particularly, we discuss recent findings on TREM-1 pathway regulation in various autoimmune diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), type 1 diabetes (T1D), and psoriasis. This receptor may potentially be manipulated to alter the inflammatory response to chronic inflammation and possible therapies are explored in this review.

Decreased levels of keratin 8 sensitize mice to streptozotocin-induced diabetes

AIM

Diabetes is a result of an interplay between genetic, environmental and lifestyle factors. Keratin intermediate filaments are stress proteins in epithelial cells, and keratin mutations predispose to several human diseases. However, the involvement of keratins in diabetes is not well known. K8 and its partner K18 are the main β-cell keratins, and knockout of K8 (K8^-/- ) in mice causes mislocalization of glucose transporter 2, mitochondrial defects, reduced insulin content and altered systemic glucose/insulin control. We hypothesize that K8/K18 offer protection during β-cell stress and that decreased K8 levels contribute to diabetes susceptibility.

METHODS

K8-heterozygous knockout (K8^+/- ) and wild-type (K8^+/+ ) mice were used to evaluate the influence of keratin levels on endocrine pancreatic function and diabetes development under basal conditions and after T1D streptozotocin (STZ)-induced β-cell stress and T2D high-fat diet (HFD).

RESULTS

Murine K8^+/- endocrine islets express ~50% less K8/K18 compared with K8^+/+ . The decreased keratin levels have little impact on basal systemic glucose/insulin regulation, β-cell health or insulin levels. Diabetes incidence and blood glucose levels are significantly higher in K8^+/- mice after low-dose/chronic STZ treatment, and STZ causes more β-cell damage and polyuria in K8^+/- compared with K8^+/+ . K8 appears upregulated 5 weeks after STZ treatment in K8^+/+ islets but not in K8^+/- . K8^+/- mice showed no major susceptibility risk to HFD compared to K8^+/+ .

CONCLUSION

Partial K8 deficiency reduces β-cell stress tolerance and aggravates diabetes development in response to STZ, while there is no major susceptibility to HFD.

System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes

Genome-wide association studies (GWASs) have discovered >50 risk loci for type 1 diabetes (T1D). However, those variations only have modest effects on the genetic risk of T1D. In recent years, accumulated studies have suggested that gene-gene interactions might explain part of the missing heritability. The purpose of our research was to identify potential and novel risk genes for T1D by systematically considering the gene-gene interactions through network analyses. We carried out a novel system network analysis of summary GWAS statistics jointly with transcriptomic gene expression data to identify some of the missing heritability for T1D using weighted gene co-expression network analysis (WGCNA). Using WGCNA, seven modules for 1852 nominally significant (P ≤ 0.05) GWAS genes were identified by analyzing microarray data for gene expression profile. One module (tagged as green module) showed significant association (P ≤ 0.05) between the module eigengenes and the trait. This module also displayed a high correlation (r = 0.45, P ≤ 0.05) between module membership (MM) and gene significant (GS), which indicated that the green module of co-expressed genes is of significant biological importance for T1D status. By further describing the module content and topology, the green module revealed a significant enrichment in the "regulation of immune response" (GO:0050776), which is a crucially important pathway in T1D development. Our findings demonstrated a module and several core genes that act as essential components in the etiology of T1D possibly via the regulation of immune response, which may enhance our fundamental knowledge of the underlying molecular mechanisms for T1D.

Sex-Specific Gene-by-Vitamin D Interactions Regulate Susceptibility to Central Nervous System Autoimmunity

Vitamin D3 (VitD) insufficiency is postulated to represent a major modifiable risk factor for multiple sclerosis (MS). While low VitD levels strongly correlate with higher MS risk in white populations, this is not the case for other ethnic groups, suggesting the existence of a genetic component. Moreover, VitD supplementation studies in MS so far have not shown a consistent benefit. We sought to determine whether direct manipulation of VitD levels modulates central nervous system autoimmune disease in a sex-by-genotype-dependent manner. To this end, we used a dietary model of VitD modulation, together with the autoimmune animal model of MS, experimental autoimmune encephalomyelitis (EAE). To assess the impact of genotype-by-VitD interactions on EAE susceptibility, we utilized a chromosome substitution (consomic) mouse model that incorporates the genetic diversity of wild-derived PWD/PhJ mice. High VitD was protective in EAE in female, but not male C57BL/6J (B6) mice, and had no effect in EAE-resistant PWD/PhJ (PWD) mice. EAE protection was accompanied by sex- and genotype-specific suppression of proinflammatory transcriptional programs in CD4 T effector cells, but not CD4 regulatory T cells. Decreased expression of proinflammatory genes was observed with high VitD in female CD4 T effector cells, specifically implicating a key role of MHC class II genes, interferon gamma, and Th1 cell-mediated neuroinflammation. In consomic strains, effects of VitD on EAE were also sex- and genotype dependent, whereby high VitD: (1) was protective, (2) had no effect, and (3) unexpectedly had disease-exacerbating effects. Systemic levels of 25(OH)D differed across consomic strains, with higher levels associated with EAE protection only in females. Analysis of expression of key known VitD metabolism genes between B6 and PWD mice revealed that their expression is genetically determined and sex specific and implicated Cyp27b1 and Vdr as candidate genes responsible for differential EAE responses to VitD modulation. Taken together, our results support the observation that the association between VitD status and MS susceptibility is genotype dependent and suggest that the outcome of VitD status in MS is determined by gene-by-sex interactions.

Quantifying performance of a diagnostic test as the expected information for discrimination: Relation to the C-statistic

Although the C-statistic is widely used for evaluating the performance of diagnostic tests, its limitations for evaluating the predictive performance of biomarker panels have been widely discussed. The increment in C obtained by adding a new biomarker to a predictive model has no direct interpretation, and the relevance of the C-statistic to risk stratification is not obvious. This paper proposes that the C-statistic should be replaced by the expected information for discriminating between cases and non-cases (expected weight of evidence, denoted as Λ), and that the strength of evidence favouring one model over another should be evaluated by cross-validation as the difference in test log-likelihoods. Contributions of independent variables to predictive performance are additive on the scale of Λ. Where the effective number of independent predictors is large, the value of Λ is sufficient to characterize fully how the predictor will stratify risk in a population with given prior probability of disease, and the C-statistic can be interpreted as a mapping of Λ to the interval from 0.5 to 1. Even where this asymptotic relationship does not hold, there is a one-to-one mapping between the distributions in cases and non-cases of the weight of evidence favouring case over non-case status, and the quantiles of these distributions can be used to calculate how the predictor will stratify risk. This proposed approach to reporting predictive performance is demonstrated by analysis of a dataset on the contribution of microbiome profile to diagnosis of colorectal cancer.

Clinical and research uses of genetic risk scores in type 1 diabetes

Type 1 diabetes (T1D) is a chronic disease of high blood glucose caused by autoimmune destruction of pancreatic beta cells eventually resulting in severe insulin deficiency. T1D has a significant heritable risk. Genetic associations found are particularly strong in the HLA class II region but T1D is a polygenic disease associated with over 60 loci across the genome. Polygenic risk scores are one method of summing these genetic risk elements as a single continuous variable. This review discusses the clinical and research utility of genetic risk scores in T1D particularly in disease prediction and progression. We also explore creative uses of genetic risk scores in big data and the limitations of using a genetic risk score. The increase in publically available genetic data and rapid fall in costs of genotyping mean that a T1D genetic risk score (T1D GRS) is likely to prove useful for disease prediction, discrimination, investigation of unusual cohorts, and investigation of biology in large datasets where genetic data are available.

Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine

Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative method of supervised data mining (Logic Learning Machine or LLM). Data were taken from a pilot cross-sectional study to identify subjects with PG behaviour, assessed by two internationally validated scales (SOGS and Lie/Bet). Information was obtained from 251 gamblers recruited in six betting establishments. Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire. The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend. Furthermore, the combination of LLM and LR indicated the presence of two different types of PG, namely: (a) daily gamblers, more prone to illegal behaviour, with poor money management skills and who started gambling at an early age, and (b) non-daily gamblers, characterised by superstitious beliefs and a higher preference for immediate reward games. Finally, instant gratification games were strongly associated with the number of games usually played. Studies on gamblers habitually frequently betting shops are rare. The finding of different types of PG by habitual gamblers deserves further analysis in larger studies. Advanced data mining algorithms, like LLM, are powerful tools and potentially useful in identifying risk factors for PG.

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

BACKGROUND

Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.

METHODS AND FINDINGS

The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%-9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%-54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%-60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.

CONCLUSIONS

A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials.

Collective effects of common single nucleotide polymorphisms and genetic risk prediction in type 1 diabetes

Type 1 diabetes (T1D) is a common autoimmune disease and may be related to multiple genetic and environmental risk factors. Previous genetic studies have focused on looking for individual polymorphic risk variants. Here, we studied the overall levels of genetic diversity in T1D patients by making use of a previously published study including 1865 cases and 2828 reference samples with genotyping data for 500 K common single nucleotide polymorphisms (SNPs). We determined the minor allele (MA) status of each SNP in the reference samples and calculated the total number of MAs or minor allele contents (MAC) of each individual. We found the average MAC of cases to be greater than that of the reference samples. By focusing on MAs with strong linkage to cases, we further identified a set of 112 SNPs that could predict 19.19% of cases. These results suggest that overall genetic variation over a threshold level may be a risk factor in T1D and provide a new genetic method for predicting the disorder.

A population genetic interpretation of GWAS findings for human quantitative traits

Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes-notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10-3.