Blood-based multivariate methylation risk score for cognitive impairment and dementia.

INTRODUCTION: The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection. METHODS: In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts. RESULTS: We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P = 2.0 × 10-3). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia = 2.59). DISCUSSION: Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk. HIGHLIGHTS: We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level.

Stratification by smoking status reveals an association of CHRNA5-A3-B4 genotype with body mass index in never smokers.

We previously used a single nucleotide polymorphism (SNP) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index (BMI) in a Mendelian randomisation analysis. While seeking to extend these findings in a larger sample we found that this SNP is associated with 0.74% lower body mass index (BMI) per minor allele in current smokers (95% CI -0.97 to -0.51, P = 2.00 × 10(-10)), but also unexpectedly found that it was associated with 0.35% higher BMI in never smokers (95% CI +0.18 to +0.52, P = 6.38 × 10(-5)). An interaction test confirmed that these estimates differed from each other (P = 4.95 × 10(-13)). This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking, and via the weight-reducing effects of smoking. It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI, given the opposite association with BMI in never and current smokers. This demonstrates that novel associations may be obscured by hidden population sub-structure. Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations.

The splice site variant rs11078928 may be associated with a genotype-dependent alteration in expression of GSDMB transcripts.

BACKGROUND: Many genetic variants have been associated with susceptibility to complex traits by genome wide association studies (GWAS), but for most, causal genes and mechanisms of action have yet to be elucidated. Using bioinformatics, we identified index and proxy variants associated with autoimmune disease susceptibility, with the potential to affect splicing of candidate genes. PCR and sequence analysis of whole blood RNA samples from population controls was then carried out for the 8 most promising variants to determine the effect of genetic variation on splicing of target genes. RESULTS: We identified 31 splice site SNPs with the potential to affect splicing, and prioritised 8 to determine the effect of genotype on candidate gene splicing. We identified that variants rs11078928 and rs2014886 were associated with altered splicing of the GSDMB and TSFM genes respectively. rs11078928, present in the asthma and autoimmune disease susceptibility locus on chromosome 17q12-21, was associated with the production of a novel Δ exon5-8 transcript of the GSDMB gene, and a separate decrease in the percentage of transcripts with inclusion of exon 6, whereas the multiple sclerosis susceptibility variant rs2014886, was associated with an alternative TFSM transcript encompassing a short cryptic exon within intron 2. CONCLUSIONS: Our findings demonstrate the utility of a bioinformatic approach in identification and prioritisation of genetic variants effecting splicing of their host genes, and suggest that rs11078928 and rs2014886 may affect the splicing of the GSDMB and TSFM genes respectively.

Preterm Birth Genome Project (PGP) -- validation of resources for preterm birth genome-wide studies.

We determined a series of quality control (QC) analyses to assess the usability of DNA collected and processed from different countries utilizing different DNA extraction techniques prior to genome-wide association studies (GWAS). The quality of DNA collected utilizing four different DNA extraction techniques and the impact of shipping DNA at different temperatures on array performance were evaluated. Fifteen maternal-fetal pairs were used from four countries. DNA was extracted using four approaches: whole blood, blood spots with whole genome amplification (WGA), saliva and buccal swab. Samples were sent to a genotyping facility, either on dry ice or at room temperature and genotyped using Affymetrix SNP array 6.0. QC measured included extraction techniques, effect of shipping temperatures, accuracy and Mendelian concordance. Significantly fewer (50 % ) single nucleotide polymorphisms (SNPs) passed QC metrics for buccal swab DNA (P < 0.0001) due to missing genotype data (P < 0.0001). Whole blood or saliva DNA had the highest call rates (99.2 0.4 % and 99.3 0.2 % , respectively) and Mendelian concordance. Shipment temperature had no effect. DNA from blood or saliva had the highest call rate accuracy, and buccal swabs had the lowest. DNA extracted from blood, saliva and blood spots were found suitable for GWAS in our study.

Variety in responses of wintering oystercatchers Haematopus ostralegus to near-collapse of their prey in the Exe Estuary, UK.

Globally, habitat loss or degradation is a major threat to many species, and those with specific habitat requirements are particularly vulnerable. Many species of wading birds (Charadrii) are dependent upon intertidal sites to feed, but, as a result of anthropogenic pressures, the prey landscape has changed at many estuaries. Behavioral adaptations may be able to buffer these changes. In this study over multiple seasons, we aimed to investigate the foraging behaviors of wintering Eurasian oystercatchers in the Exe Estuary where mussel beds, the preferred prey at this site, have almost disappeared in the last decade. From 2018 to 2021, GPS tracking devices were deployed on 24 oystercatchers, and the foraging locations of adults, sub-adults, and juveniles were determined. Of the 12 birds tracked over multiple winter periods, 10 used the same foraging home ranges but a juvenile and sub-adult changed locations interannually. The dominant prey species at key foraging sites were assessed, and we found that younger birds were more likely to visit sites with lower quality prey, likely due to being at a competitive disadvantage, and also to explore sites further away. Individuals were generally consistent in the areas of the estuary used in early and late winter, and over 90% of locations were recorded in the protected area boundary, which covers the sand and mudflats of the Exe. These findings suggest high specificity of the current protected area for oystercatchers in the Exe Estuary, although, if the prey landscape continues to decline, younger individuals may provide the potential for adaptation by finding and foraging at additional sites. Continued monitoring of individual behavior within populations that are facing dramatic changes to their prey is essential to understand how they may adapt and to develop suitable management plans to conserve threatened species.

GPS tracking reveals landfill closures induce higher foraging effort and habitat switching in gulls.

BACKGROUND: Landfills are a major subsidy for some animals, with implications for their life history and demography. Gulls feed extensively on food from landfills and closures are expected to have ecological consequences, but how this influences movement ecology is virtually unknown. METHODS: We used GPS-tracking to quantify foraging behaviour and habitat choice of lesser black-backed gulls (Larus fuscus) breeding at two colonies before and after closure of two nearby landfills. RESULTS: Following closure, gulls from both colonies travelled further and for longer to forage. Gulls also changed habitat selection, although this differed by colony - birds from one colony shifted to agricultural habitats, while at the other, increased their use of urban areas. These behavioural responses had no effect on adult body condition but hint at potential direct effects of higher foraging costs and indirect impacts by shifting to new habitats. CONCLUSIONS: Our results demonstrate how landfill availability influences gull foraging movements and habitat selection. We also emphasize the value of biologging to detect rapid behavioural responses in contrast to more conventional demographic approaches, which is especially important for animals that spend the majority of their lives away from direct observation.

Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity.

CONTEXT: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity. OBJECTIVE: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies. DESIGN: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models. RESULTS: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10-9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10-9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity. CONCLUSION: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity.

Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference.

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.

Genetic variation and human aging: progress and prospects.

The genetics of aging has seen extraordinary progress over the last few decades, with animal models suggesting key roles for a number of metabolic pathways. However, humans outlive laboratory models many times over, and only evidence from humans can ultimately identify the drivers of human aging. In this article we thematically review progress in identifying human genetic variants associated with longevity. We also look at the bigger picture of progress in identifying genetic associates of disease and functioning and healthy aging in older people. Although much of the existing evidence is fragmentary, recent exciting findings and robust methods are taking the field rapidly forward.

Ant colony optimisation of decision tree and contingency table models for the discovery of gene-gene interactions.

In this study, ant colony optimisation (ACO) algorithm is used to derive near-optimal interactions between a number of single nucleotide polymorphisms (SNPs). This approach is used to discover small numbers of SNPs that are combined into a decision tree or contingency table model. The ACO algorithm is shown to be very robust as it is proven to be able to find results that are discriminatory from a statistical perspective with logical interactions, decision tree and contingency table models for various numbers of SNPs considered in the interaction. A large number of the SNPs discovered here have been already identified in large genome-wide association studies to be related to type II diabetes in the literature, lending additional confidence to the results.

Biological interpretation of genome-wide association studies using predicted gene functions.

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Eye size in birds and the timing of song at dawn.

Why do different species of birds start their dawn choruses at different times? We test the hypothesis that the times at which different species start singing at dawn are related to their visual capability at low light intensities. Birds with large eyes can achieve greater pupil diameters and hence, all other things being equal, greater visual sensitivity and resolution than birds with small eyes. We estimated the maximum pupil diameter of passerine birds by measuring the diameter of the exposed eye surface, and measured the times of the first songs at dawn of songbirds present in different bird communities, and the light intensities at these times. Using phylogenetic comparative analyses, we found that songbirds with large eyes started to sing at lower light intensities (and therefore earlier) than species with smaller eyes. These relationships were stronger when differences in body size were controlled for statistically, and were consistent between two phylogenies and when species were treated as independent data points. Our results therefore provide robust support for the hypothesis that visual capability at low light levels influences the times at which birds start to sing at dawn.

Differences in smoking associated DNA methylation patterns in South Asians and Europeans.

BACKGROUND: DNA methylation is strongly associated with smoking status at multiple sites across the genome. Studies have largely been restricted to European origin individuals yet the greatest increase in smoking is occurring in low income countries, such as the Indian subcontinent. We determined whether there are differences between South Asians and Europeans in smoking related loci, and if a smoking score, combining all smoking related DNA methylation scores, could differentiate smokers from non-smokers. RESULTS: Illumina HM450k BeadChip arrays were performed on 192 samples from the Southall And Brent REvisited (SABRE) cohort. Differential methylation in smokers was identified in 29 individual CpG sites at 18 unique loci. Interaction between smoking status and ethnic group was identified at the AHRR locus. Ethnic differences in DNA methylation were identified in non-smokers at two further loci, 6p21.33 and GNG12. With the exception of GFI1 and MYO1G these differences were largely unaffected by adjustment for cell composition. A smoking score based on methylation profile was constructed. Current smokers were identified with 100% sensitivity and 97% specificity in Europeans and with 80% sensitivity and 95% specificity in South Asians. CONCLUSIONS: Differences in ethnic groups were identified in both single CpG sites and combined smoking score. The smoking score is a valuable tool for identification of true current smoking behaviour. Explanations for ethnic differences in DNA methylation in association with smoking may provide valuable clues to disease pathways.

Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity.

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp- and oral glucose tolerance test-based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (ß in SDs per allele [95% CI], -0.03 [-0.04, -0.01]; P = 0.004). This score was associated with lower BMI (-0.01 [-0.01, -0.0]; P = 0.02) and gluteofemoral fat mass (-0.03 [-0.05, -0.02; P = 1.4 × 10(-6)) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.

Paradoxical lower serum triglyceride levels and higher type 2 diabetes mellitus susceptibility in obese individuals with the PNPLA3 148M variant.

BACKGROUND: Obesity is highly associated with elevated serum triglycerides, hepatic steatosis and type 2 diabetes (T2D). The I148M (rs738409) genetic variant of patatin-like phospholipase domain-containing 3 gene (PNPLA3) is known to modulate hepatic triglyceride accumulation, leading to steatosis. No association between PNPLA3 I148M genotype and T2D in Europeans has been reported. Aim of this study is to examine the relationship between PNPLA3 I148M genotypes and serum triglycerides, insulin resistance and T2D susceptibility by testing a gene-environment interaction model with severe obesity. METHODS AND FINDINGS: PNPLA3 I148M was genotyped in a large obese cohort, the SOS study (n = 3,473) and in the Go-DARTS (n = 15,448), a T2D case-control study. Metabolic parameters were examined across the PNPLA3 I148M genotypes in participants of the SOS study at baseline and at 2- and 10-year follow up after bariatric surgery or conventional therapy. The associations with metabolic parameters were validated in the Go-DARTS study. Serum triglycerides were found to be lower in the PNPLA3 148M carriers from the SOS study at baseline and from the Go-DARTS T2D cohort. An increased risk for T2D conferred by the 148M allele was found in the SOS study (O.R. 1.09, 95% C.I. 1.01-1.39, P = 0.040) and in severely obese individuals in the Go-DARTS study (O.R. 1.37, 95% C.I. 1.13-1.66, P = 0.001). The 148M allele was no longer associated with insulin resistance or T2D after bariatric surgery in the SOS study and no association with the 148M allele was observed in the less obese (BMI<35) individuals in the Go-DARTS study (P for interaction  = 0.002). This provides evidence for the obesity interaction with I48M allele and T2D risk in a large-scale cross-sectional and a prospective interventional study. CONCLUSIONS: Severely obese individuals carrying the PNPLA3 148M allele have lower serum triglyceride levels, are more insulin resistant and more susceptible to T2D. This study supports the hypothesis that obesity-driven hepatic lipid accumulation may contribute to T2D susceptibility.

The paraoxonase (PON1) Q192R polymorphism is not associated with poor health status or depression in the ELSA or INCHIANTI studies.

BACKGROUND: The human paraoxonase (PON1) protein detoxifies certain organophosphates, and the PON1 Q192R polymorphism (rs662) affects PON1 activity. Groups with higher dose exposure to organophosphate sheep dips or first Gulf War nerve toxins reported poorer health if they had 192R, and these associations have been used to exemplify Mendelian randomization analysis. However, a reported association of 192R with depression in a population-based study of older women recently cast doubt on the specificity of the higher dose findings. We aimed to examine associations between the PON1 Q192R polymorphism and self-reported poor health and depression in two independent population-based studies. METHODS: We used logistic regression models to examine the associations in men and women aged 60-79 years from the English Longitudinal Study of Ageing (ELSA, n = 3158) and InCHIANTI (n = 761) population studies. Outcomes included the Center for Epidemiologic Studies Depression (CES-D) scale, self-rated general health status and (in ELSA only) diagnoses of depression. RESULTS: The PON1 Q192R polymorphism was not associated with self-reported poor health {meta-analysis: odds ratio (OR) = 1.01 [confidence interval (CI) 0.91-1.13], P = 0.80} or depressive symptoms in either study or in meta-analyses [CES-D: OR = 1.01 (CI 0.87-1.17), P = 0.90]. There was also no association with histories of diagnosed depression in ELSA [OR = 1.03 (CI 0.82-1.30), P = 0.80]. CONCLUSIONS: We found no evidence of an association between the PON1 Q192R polymorphism and poor general or mental health in two independent population-based studies. Neither the claimed Q192R association with depression in the general population nor its theoretical implications were supported.

A common haplotype of the glucokinase gene alters fasting glucose and birth weight: association in six studies and population-genetics analyses.

Fasting glucose is associated with future risk of type 2 diabetes and ischemic heart disease and is tightly regulated despite considerable variation in quantity, type, and timing of food intake. In pregnancy, maternal fasting glucose concentration is an important determinant of offspring birth weight. The key determinant of fasting glucose is the enzyme glucokinase (GCK). Rare mutations of GCK cause fasting hyperglycemia and alter birth weight. The extent to which common variation of GCK explains normal variation of fasting glucose and birth weight is not known. We aimed to comprehensively define the role of variation of GCK in determination of fasting glucose and birth weight, using a tagging SNP (tSNP) approach and studying 19,806 subjects from six population-based studies. Using 22 tSNPs, we showed that the variant rs1799884 is associated with fasting glucose at all ages in the normal population and exceeded genomewide levels of significance (P=10-9). rs3757840 was also highly significantly associated with fasting glucose (P=8x10-7), but haplotype analysis revealed that this is explained by linkage disequilibrium (r2=0.2) with rs1799884. A maternal A allele at rs1799884 was associated with a 32-g (95% confidence interval 11-53 g) increase in offspring birth weight (P=.002). Genetic variation influencing birth weight may have conferred a selective advantage in human populations. We performed extensive population-genetics analyses to look for evidence of recent positive natural selection on patterns of GCK variation. However, we found no strong signature of positive selection. In conclusion, a comprehensive analysis of common variation of the glucokinase gene shows that this is the first gene to be reproducibly associated with fasting glucose and fetal growth.