Combined genotype and haplotype tests for region-based association studies

10.1186/1471-2164-14-569BMC Genomics141-BGME

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain

As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual’s cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors

Genetic and environmental influences on fruit and vegetable consumption and depression in older adults

Background: Prior work suggests that higher fruit and vegetable consumption may protect against depression in older adults. Better understanding of the influence of genetic and environmental factors on fruit and vegetable intakes may lead to the design of more effective dietary strategies to increase intakes. In turn this may reduce the occurrence of depression in older adults. Objectives: The primary aim of this study is to estimate the genetic and environmental influences on the consumption of fruit and vegetables in older adults. The secondary aim is an exploratory analysis into possible shared genetic influences on fruit and vegetable intakes and depression. Methods: Analysis of observational data from 374 twins (67.1% female; 208 monozygotic (MZ); 166 dizygotic (DZ)) aged ≥ 65 years drawn from the Older Australian Twins Study. Dietary data were obtained using a validated food frequency questionnaire and depressive symptoms were measured using the 15-item short form Geriatric Depression Scale. The contribution of genetic and environmental influences on fruit and vegetable intake were estimated by comparing MZ and DZ twin intakes using structural equation modelling. A tri-variate twin model was used to estimate the genetic and environmental correlation between total fruit and vegetable intakes and depression. Results: In this study, vegetable intake was moderately influenced by genetics (0.39 95%CI 0.22, 0.54). Heritability was highest for brassica vegetables (0.40 95%CI 0.24, 0.54). Overall fruit intake was not significantly heritable. No significant genetic correlations were detected between fruit and vegetable intake and depressive symptoms. Conclusions: Vegetable consumption, particularly bitter tasting brassica vegetables, was significantly influenced by genetics, although environmental influences were also apparent. Consumption of fruit was only influenced by the environment, with no genetic influence detected, suggesting strategies targeting the food environment may be particularly effective for encouraging fruit consumption

The influence of rs53576 polymorphism in the oxytocin receptor (OXTR) gene on empathy in healthy adults by subtype and ethnicity: a systematic review and meta-analysis

Empathy is essential for navigating complex social environments. Prior work has shown associations between rs53576, a single nucleotide polymorphism (SNP) located in the oxytocin receptor gene (OXTR), and generalized empathy. We undertook a systematic review and meta-analysis to assess the effects of rs53576 on subdomains of empathy, specifically cognitive empathy (CE) and affective empathy (AE), in healthy adults. Twenty cohorts of 8933 participants aged 18-98 were identified, including data from the Sydney Memory and Ageing Study, a cohort of older community adults. Meta-analyses found G homozygotes had greater generalized empathic abilities only in young to middle-aged adults. While meta-analyses of empathy subdomains yielded no significant overall effects, there were differential effects based on ethnicity. G homozygotes were associated with greater CE abilities in Asian cohorts (standardized mean difference; SMD: 0.09 [2.8·10-3-0.18]), and greater AE performance in European cohorts [SMD: 0.12 (0.04-0.21)]. The current literature highlights a need for further work that distinguishes between genetic and ethnocultural effects and explores effects of advanced age on this relationship

Review and meta-analysis of genetic polymorphisms associated with exceptional human longevity

Background Many factors contribute to exceptional longevity, with genetics playing a significant role. However, to date, genetic studies examining exceptional longevity have been inconclusive. This comprehensive review seeks to determine the genetic variants associated with exceptional longevity by undertaking meta-analyses. Methods Meta-analyses of genetic polymorphisms previously associated with exceptional longevity (85+) were undertaken. For each variant, meta-analyses were performed if there were data from at least three independent studies available, including two unpublished additional cohorts. Results Five polymorphisms, ACE rs4340, APOE ε2/3/4, FOXO3A rs2802292, KLOTHO KL-VS and IL6 rs1800795 were significantly associated with exceptional longevity, with the pooled effect sizes (odds ratios) ranging from 0.42 (APOE ε4) to 1.45 (FOXO3A males). Conclusion In general, the observed modest effect sizes of the significant variants suggest many genes of small influence play a role in exceptional longevity, which is consistent with results for other polygenic traits. Our results also suggest that genes related to cardiovascular health may be implicated in exceptional longevity. Future studies should examine the roles of gender and ethnicity and carefully consider study design, including the selection of appropriate controls

A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets

Motivation: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30 000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. The first stage is based on the Balanced Iterative Reducing and Clustering using Hierarchies algorithm with the second stage being a conventional k-means clustering technique. This algorithm has been implemented in a software tool (HPCluster) designed to cluster gene expression data. We compared the clustering results using the two-stage hyperplane algorithm with the conventional k-means algorithm from other available programs. Because, the first stage traverses the data in a single scan, the performance and speed increases substantially. The data reduction accomplished in the first stage of the algorithm reduces the memory requirements allowing us to cluster 44 460 genes without failure and significantly decreases the time to complete when compared with popular k-means programs. The software was written in C# (.NET 1.1)

The heritability of amyloid burden in older adults: the Older Australian Twins Study

Objective To determine the proportional genetic contribution to the variability of cerebral β-amyloid load in older adults using the classic twin design. Methods Participants (n=206) comprising 61 monozygotic (MZ) twin pairs (68 (55.74%) females; mean age (SD): 71.98 (6.43) years), and 42 dizygotic (DZ) twin pairs (56 (66.67%) females; mean age: 71.14 (5.15) years) were drawn from the Older Australian Twins Study. Participants underwent detailed clinical and neuropsychological evaluations, as well as MRI, diffusion tensor imaging (DTI) and amyloid PET scans. Fifty-eight participants (17 MZ pairs, 12 DZ pairs) had PET scans with 11Carbon-Pittsburgh Compound B, and 148 participants (44 MZ pairs, 30 DZ pairs) with 18Fluorine-NAV4694. Cortical amyloid burden was quantified using the centiloid scale globally, as well as the standardised uptake value ratio (SUVR) globally and in specific brain regions. Small vessel disease (SVD) was quantified using total white matter hyperintensity volume on MRI, and peak width of skeletonised mean diffusivity on DTI. Heritability (h2) and genetic correlations were measured with structural equation modelling under the best fit model, controlling for age, sex, tracer and scanner. results The heritability of global amyloid burden was moderate (0.41 using SUVR; 0.52 using the centiloid scale) and ranged from 0.20 to 0.54 across different brain regions. There were no significant genetic or environmental correlations between global amyloid burden and markers of SVD. Conclusion Amyloid deposition, the hallmark early feature of Alzheimer’s disease, is under moderate genetic influence, suggesting a major environmental contribution that may be amenable to intervention

Gene-based multiple trait analysis for exome sequencing data

The common genetic variants identified through genome-wide association studies explain only a small proportion of the genetic risk for complex diseases. The advancement of next-generation sequencing technologies has enabled the detection of rare variants that are expected to contribute significantly to the missing heritability. Some genetic association studies provide multiple correlated traits for analysis. Multiple trait analysis has the potential to improve the power to detect pleiotropic genetic variants that influence multiple traits. We propose a gene-level association test for multiple traits that accounts for correlation among the traits. Gene- or region-level testing for association involves both common and rare variants. Statistical tests for common variants may have limited power for individual rare variants because of their low frequency and multiple testing issues. To address these concerns, we use the weighted-sum pooling method to test the joint association of multiple rare and common variants within a gene. The proposed method is applied to the Genetic Association Workshop 17 (GAW17) simulated mini-exome data to analyze multiple traits. Because of the nature of the GAW17 simulation model, increased power was not observed for multiple-trait analysis compared to single-trait analysis. However, multiple-trait analysis did not result in a substantial loss of power because of the testing of multiple traits. We conclude that this method would be useful for identifying pleiotropic genes

Association tests for rare and common variants based on genotypic and phenotypic measures of similarity between individuals

Genome-wide association studies have helped us identify thousands of common variants associated with several widespread complex diseases. However, for most traits, these variants account for only a small fraction of phenotypic variance or heritability. Next-generation sequencing technologies are being used to identify additional rare variants hypothesized to have higher effect sizes than the already identified common variants, and to contribute significantly to the fraction of heritability that is still unexplained. Several pooling strategies have been proposed to test the joint association of multiple rare variants, because testing them individually may not be optimal. Within a gene or genomic region, if there are both rare and common variants, testing their joint association may be desirable to determine their synergistic effects. We propose new methods to test the joint association of several rare and common variants with binary and quantitative traits. Our association test for quantitative traits is based on genotypic and phenotypic measures of similarity between pairs of individuals. For the binary trait or case-control samples, we recently proposed an association test based on the genotypic similarity between individuals. Here, we develop a modified version of this test for rare variants. Our tests can be used for samples taken from multiple subpopulations. The power of our test statistics for case-control samples and quantitative traits was evaluated using the GAW17 simulated data sets. Type I error rates for the proposed tests are well controlled. Our tests are able to identify some of the important causal genes in the GAW17 simulated data sets