Pleiotropy of type 2 diabetes with obesity

The chicken or the egg? Does glycaemic control predict cognitive function or the other way around?

The association between type 2 diabetes and cognitive dysfunction is well established. Prevention of the development of type 2 diabetes and its complications, as well as cognitive dysfunction and dementia, are leading goals in these fields. Deciphering the causality direction of the interplay between type 2 diabetes and cognitive dysfunction, and understanding the timeline of disease progression, are crucial for developing efficient prevention strategies. The prevailing perception is that type 2 diabetes leads to cognitive dysfunction and dementia. There is substantial evidence showing that accelerated cognitive decline in type 2 diabetes starts in midlife (mean age 40-60 years) and that it may even begin at the prediabetes stage. However, in this issue of Diabetologia, Altschul et al (doi: https://doi.org/10.1007/s00125-018-4645-8 ) show evidence for the reverse causality hypothesis, i.e. that lower cognitive function precedes poor glycaemic control. They found that cognitive function at early adolescence (age 11 years) predicts both HbA1c levels and cognitive function at age 70 years. Moreover, they found that lower cognitive function at age 70 is associated with an increase in HbA1c from age 70 to 79 years. Based on these findings, future studies should explore whether developing prevention strategies that target young adolescents with lower cognitive function will result in prevention of type 2 diabetes, breaking the vicious cycle of type 2 diabetes and cognitive dysfunction.

Multivariate analysis of genomics data to identify potential pleiotropic genes for type 2 diabetes, obesity and dyslipidemia using Meta-CCA and gene-based approach

Previous studies have demonstrated the genetic correlations between type 2 diabetes, obesity and dyslipidemia, and indicated that many genes have pleiotropic effects on them. However, these pleiotropic genes have not been well-defined. It is essential to identify pleiotropic genes using systematic approaches because systematically analyzing correlated traits is an effective way to enhance their statistical power. To identify potential pleiotropic genes for these three disorders, we performed a systematic analysis by incorporating GWAS (genome-wide associated study) datasets of six correlated traits related to type 2 diabetes, obesity and dyslipidemia using Meta-CCA (meta-analysis using canonical correlation analysis). Meta-CCA is an emerging method to systematically identify potential pleiotropic genes using GWAS summary statistics of multiple correlated traits. 2,720 genes were identified as significant genes after multiple testing (Bonferroni corrected p value < 0.05). Further, to refine the identified genes, we tested their relationship to the six correlated traits using VEGAS-2 (versatile gene-based association study-2). Only the genes significantly associated (Bonferroni corrected p value < 0.05) with more than one trait were kept. Finally, 25 genes (including two confirmed pleiotropic genes and eleven novel pleiotropic genes) were identified as potential pleiotropic genes. They were enriched in 5 pathways including the statin pathway and the PPAR (peroxisome proliferator-activated receptor) Alpha pathway. In summary, our study identified potential pleiotropic genes and pathways of type 2 diabetes, obesity and dyslipidemia, which may shed light on the common biological etiology and pathogenesis of these three disorders and provide promising insights for new therapies.

Genetic risk of type 2 diabetes in populations of the African continent: A systematic review and meta-analyses

BACKGROUND

Type 2 diabetes (T2D) is growing faster in Africa than anywhere else, driven by the dual effects of genetic and environmental factors. We conducted a systematic review and meta-analyses of published studies on genetic markers of T2D in populations within Africa.

METHODS

Multiple databases were searched for studies of genetic variants associated with T2D in populations living in Africa. Studies reporting on the association of a genetic marker with T2D or indicators of glycaemia were included. Data were extracted on study design and characteristics, genetic determinants, effect estimates of associations with T2D.

FINDINGS

Overall, 100 polymorphisms in 57 genes have been investigated in relation with T2D in populations within Africa, in 60 studies. Almost all studies used the candidate gene approach, with >88% published during 2006-2014 and 70% (42/60) originating from Tunisia and Egypt. Polymorphisms in ACE, AGRP, eNOS, GSTP1, HSP70-2, MC4R, MTHFR, PHLPP, POL1, TCF7L2, and TNF-α gene were found to be associated with T2D, with overlapping effect on various cardiometabolic traits. The polymorphisms investigated in multiple studies mostly had consistent effects across studies, with only modest or no statistical heterogeneity. Effect sizes were modestly significant [e.g., odd ratio 1.49 (95%CI 1.33-1.66) for TCF7L2 (rs7903146)]. Underpowered genome-wide studies revealed no diabetes risk loci specific to African populations.

INTERPRETATION

Current evidence on the genetic markers of T2D in African populations mostly originate from North African countries, is overall scanty and largely insufficient to reliably inform the genetic architecture of T2D across Africa.

The Relationship between Native American Ancestry, Body Mass Index and Diabetes Risk among Mexican-Americans

Higher body mass index (BMI) is a well-established risk factor for type 2 diabetes, and rates of obesity and type 2 diabetes are substantially higher among Mexican-Americans relative to non-Hispanic European Americans. Mexican-Americans are genetically diverse, with a highly variable distribution of Native American, European, and African ancestries. Here, we evaluate the role of Native American ancestry on BMI and diabetes risk in a well-defined Mexican-American population. Participants were randomly selected among individuals residing in the Houston area who are enrolled in the Mexican-American Cohort study. Using a custom Illumina GoldenGate Panel, we genotyped DNA from 4,662 cohort participants for 87 Ancestry-Informative Markers. On average, the participants were of 50.2% Native American ancestry, 42.7% European ancestry and 7.1% African ancestry. Using multivariate linear regression, we found BMI and Native American ancestry were inversely correlated; individuals with <20% Native American ancestry were 2.5 times more likely to be severely obese compared to those with >80% Native American ancestry. Furthermore, we demonstrated an interaction between BMI and Native American ancestry in diabetes risk among women; Native American ancestry was a strong risk factor for diabetes only among overweight and obese women (OR = 1.190 for each 10% increase in Native American ancestry). This study offers new insight into the complex relationship between obesity, genetic ancestry, and their respective effects on diabetes risk. Findings from this study may improve the diabetes risk prediction among Mexican-American individuals thereby facilitating targeted prevention strategies.

A pleiotropy-informed Bayesian false discovery rate adapted to a shared control design finds new disease associations from GWAS summary statistics

Genome-wide association studies (GWAS) have been successful in identifying single nucleotide polymorphisms (SNPs) associated with many traits and diseases. However, at existing sample sizes, these variants explain only part of the estimated heritability. Leverage of GWAS results from related phenotypes may improve detection without the need for larger datasets. The Bayesian conditional false discovery rate (cFDR) constitutes an upper bound on the expected false discovery rate (FDR) across a set of SNPs whose p values for two diseases are both less than two disease-specific thresholds. Calculation of the cFDR requires only summary statistics and have several advantages over traditional GWAS analysis. However, existing methods require distinct control samples between studies. Here, we extend the technique to allow for some or all controls to be shared, increasing applicability. Several different SNP sets can be defined with the same cFDR value, and we show that the expected FDR across the union of these sets may exceed expected FDR in any single set. We describe a procedure to establish an upper bound for the expected FDR among the union of such sets of SNPs. We apply our technique to pairwise analysis of p values from ten autoimmune diseases with variable sharing of controls, enabling discovery of 59 SNP-disease associations which do not reach GWAS significance after genomic control in individual datasets. Most of the SNPs we highlight have previously been confirmed using replication studies or larger GWAS, a useful validation of our technique; we report eight SNP-disease associations across five diseases not previously declared. Our technique extends and strengthens the previous algorithm, and establishes robust limits on the expected FDR. This approach can improve SNP detection in GWAS, and give insight into shared aetiology between phenotypically related conditions.

Many diseases have a significant hereditary component, only part of which has been explained by analysis of genome-wide association studies (GWAS). Shared aetiology, treatment protocols, and overlapping results from existing GWAS suggest similarities in genetic susceptibility between related diseases, which may be exploited to detect more disease-associated SNPs without the need for further data. We extend an existing method for detecting SNPs associated with a given disease by conditioning on association with another disease. Our extension allows GWAS for the two conditions to share control samples, enabling larger overall control groups and application to the common case when GWAS for related diseases pool control samples. We demonstrate that our technique limits the expected overall false discovery rate at a threshold dependent on the two diseases. We apply our technique to genotype data from ten immune mediated diseases. Overall pleiotropy between phenotypes is demonstrated graphically. We are able to declare several SNPs significant at a genome-wide level whilst controlling at a lower false-discovery rate than would be possible using a conventional approach, identifying eight previously unknown disease associations. This technique can improve SNP detection in GWAS by re-analysing existing data, and gives insight into the shared genetic bases of autoimmune diseases.

Segregation of a latent high adiposity phenotype in families with a history of type 2 diabetes mellitus implicates rare obesity-susceptibility genetic variants with large effects in diabetes-related obesity

Background

We recently reported significantly greater weight gain in non-diabetic healthy subjects with a 1^st degree family history (FH+) of type 2 diabetes mellitus (T2DM) than in a matched control group without such history (FH−) during voluntary overfeeding, implying co-inheritance of susceptibilities to T2DM and obesity. We have estimated the extent and mode of inheritance of susceptibility to increased adiposity in FH+.

Methods

Normoglycaemic participants were categorised either FH+ (≥1 1^st degree relative with T2DM, 50F/30M, age 45±14 (SD) yr) or FH− (71F/51M, age 43±14 yr). Log-transformed anthropometric measurements (height, hip and waist circumferences) and lean, bone and fat mass (Dual Energy X-ray Absorptiometry) data were analysed by rotated Factor Analysis. The age- and gender-adjusted distributions of indices of adiposity in FH+ were assessed by fits to a bimodal model and by relative risk ratios (RR, FH+/FH−) and interpreted in a purely genetic model of FH effects.

Results

The two orthogonal factors extracted, interpretable as Frame and Adiposity accounted for 80% of the variance in the input data. FH+ was associated with significantly higher Adiposity scores (p<0.01) without affecting Frame scores. Adiposity scores in FH+ conformed to a bimodal normal distribution, consistent with dominant expression of major susceptibility genes with 59% (95% CI 40%, 74%) of individuals under the higher mode. Calculated risk allele frequencies were 0.09 (0.02, 0.23) in FH−, 0.36 (0.22, 0.48) in FH+ and 0.62 (0.36, 0.88) in unobserved T2DM-affected family members.

Conclusions

The segregation of Adiposity in T2DM-affected families is consistent with dominant expression of rare risk variants with major effects, which are expressed in over half of FH+ and which can account for most T2DM-associated obesity in our population. The calculated risk allele frequency in FH− suggests that rare genetic variants could also account for a substantial fraction of the prevalent obesity in this society.

Five linkage regions each harbor multiple type 2 diabetes genes in the African American subset of the GENNID Study

We previously localized type 2 diabetes (T2D)-susceptibility genes to five chromosomal regions through a genome-wide linkage scan of T2D and age of diagnosis (AOD) in the African American subset of the GENNID sample. To follow up these findings, we repeated the linkage and association analysis using genotypes on an additional 9203 fine-mapping single nucleotide polymorphisms (SNPs) selected to tag genes under the linkage peaks. In each of the five regions, we confirmed linkage and inferred the presence of ≥2 susceptibility genes. The evidence of multiple susceptibility genes consisted of: (1) multiple linkage peaks in four of the five regions; and (2) association of T2D and AOD with SNPs within ≥2 genes in every region. The associated genes included 3 previously reported to associate with T2D or related traits (GRB10, NEDD4L, LIPG) and 24 novel candidate genes, including genes in lipid metabolism (ACOXL) and cell-cell and cell-matrix adhesion (MAGI2, CLDN4, CTNNA2).

Single nucleotide polymorphisms in JAZF1 and BCL11A gene are nominally associated with type 2 diabetes in African-American families from the GENNID study

Prior type 2 diabetes (T2D) genome-wide association studies (GWASs) have generated a list of well-replicated susceptibility loci in populations of European and Asian ancestry. To validate the trans-ethnic contribution of the single-nucleotide polymorphisms (SNPs) involved in these GWASs, we performed a family-based association analysis of 32 selected GWAS SNPs in a cohort of 1496 African-American (AA) subjects from the Genetics of NIDDM (GENNID) study. Functional roles of these SNPs were evaluated by screening cis-eQTLs in transformed lymphoblast cell lines available for a sub-group of Genetics of NIDDM (GENNID) families from Arkansas. Only three of the 32 GWAS-derived SNPs showed nominally significant association with T2D in our AA cohort. Among the replicated SNPs rs864745 in JAZF1 and rs10490072 in BCL11A gene (P=0.006 and 0.03, respectively, after adjustment for body mass index) were within the 1-lod drop support interval of T2D linkage peaks reported in these families. Genotyping of 19 tag SNPs in these two loci revealed no further common SNPs or haplotypes that may be a stronger predictor of T2D susceptibility than the index SNPs. Six T2D GWAS SNPs (rs6698181, rs9472138, rs730497, rs10811661, rs11037909 and rs1153188) were associated with nearby transcript expression in transformed lymphoblast cell lines of GENNID AA subjects. Thus, our study indicates a nominal role for JAZF1 and BCL11A variants in T2D susceptibility in AAs and suggested little overlap in known susceptibility to T2D between European- and African-derived populations when considering GWAS SNPs alone.

Detecting pleiotropy and epistasis using variance components linkage analysis in jPAP

jPAP (Java Pedigree Analysis Package) performs variance components linkage analysis of either quantitative or discrete traits. Multivariate linkage analysis of two or more traits (all quantitative, all discrete, or any combination) allows the inference of pleiotropy between the traits. The inclusion of multiple quantitative trait loci in linkage analysis allows the inference of epistasis between loci. A user-friendly graphical user interface facilitates the usage of jPAP.