A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium

Integrated approach to the assessment of CO2e-mitigation measures for the road passenger transport sector in Bahrain

The transport sector is one of the fastest-growing energy-consuming sectors in the world and it contributes greatly to emissions of carbon dioxide equivalent (CO2e). In Bahrain, CO2e emissions from the transport sector grew by an average of 8% annually between 1994 and 2006. The aim of this research was to develop an integrated approach to assess the measures adopted to reduce CO2e emissions by the transport sector within the context of climate change mitigation. This approach used the multi-criteria analysis methodology of the Analytic Hierarchy Process (AHP) to embed conventional assessment methods and a participatory approach. Three extensions to the original AHP methodology were developed: multi-AHP models, scenario packaging, and the examination of the plausibility of the results. The AHP results showed that certain fuel economy standards achieved the highest scores against five qualitative and quantitative criteria. Using socially and politically acceptable options, an integrated approach to CO2e mitigation could achieve a reduction in emissions of around 22% by 2030 (compared with 2010), at a cost of USD 112 per metric tonne of avoided CO2e emissions. Results from surveys of policymakers, experts, and the general public indicated that the outcomes of scenario packaging were plausible. The contributions of this research are two-fold. First, for the first time in Bahrain, the preferences of the general public have been considered and integrated with both the preferences of policymakers and experts and the results obtained from conventional assessment methods. Second, a structured approach for the integration of different assessment methods, transferable to other contexts, was developed and examined. Furthermore, multi-AHP models were introduced that can reflect the preferences of different concerned groups. Applications of this approach include assessment of the implementation of mitigation measures that could affect a number of concerned groups, decision making in energy-consuming sectors, and development of mitigation policy packages

Identification of Close Relatives in the HUGO Pan-Asian SNP Database

The HUGO Pan-Asian SNP Consortium has recently released a genome-wide dataset, which consists of 1,719 DNA samples collected from 71 Asian populations. For studies of human population genetics such as genetic structure and migration history, this provided the most comprehensive large-scale survey of genetic variation to date in East and Southeast Asia. However, although considered in the analysis, close relatives were not clearly reported in the original paper. Here we performed a systematic analysis of genetic relationships among individuals from the Pan-Asian SNP (PASNP) database and identified 3 pairs of monozygotic twins or duplicate samples, 100 pairs of first-degree and 161 second-degree of relationships. Three standardized subsets with different levels of unrelated individuals were suggested here for future applications of the samples in most types of population-genetics studies (denoted by PASNP1716, PASNP1640 and PASNP1583 respectively) based on the relationships inferred in this study. In addition, we provided gender information for PASNP samples, which were not included in the original dataset, based on analysis of X chromosome data

Population Genetic Structure of Peninsular Malaysia Malay Sub-Ethnic Groups

Patterns of modern human population structure are helpful in understanding the history of human migration and admixture. We conducted a study on genetic structure of the Malay population in Malaysia, using 54,794 genome-wide single nucleotide polymorphism genotype data generated in four Malay sub-ethnic groups in peninsular Malaysia (Melayu Kelantan, Melayu Minang, Melayu Jawa and Melayu Bugis). To the best of our knowledge this is the first study conducted on these four Malay sub-ethnic groups and the analysis of genotype data of these four groups were compiled together with 11 other populations' genotype data from Indonesia, China, India, Africa and indigenous populations in Peninsular Malaysia obtained from the Pan-Asian SNP database. The phylogeny of populations showed that all of the four Malay sub-ethnic groups are separated into at least three different clusters. The Melayu Jawa, Melayu Bugis and Melayu Minang have a very close genetic relationship with Indonesian populations indicating a common ancestral history, while the Melayu Kelantan formed a distinct group on the tree indicating that they are genetically different from the other Malay sub-ethnic groups. We have detected genetic structuring among the Malay populations and this could possibly be accounted for by their different historical origins. Our results provide information of the genetic differentiation between these populations and a valuable insight into the origins of the Malay sub-ethnic groups in Peninsular Malaysia

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Simulation and imaging of magnetic skyrmion in perpendicular magnetic anisotropy structures

Magnetic skyrmions are local whirls of the spin configuration in magnetic materials. Skyrmions are quasiparticles and they are by far the smallest magnetic (5 nm) structure which are stabilized by Dzyaloshinskii-Moriya interaction (DMI) in materials with perpendicular magnetic anisotropy. The low pinning current required and non-volatile nature of these magnetic skyrmions make it a promising candidate for memory devices. In addition, skyrmion has low energy consumptions and it can read and written data at high speeds. Therefore, this draws many interests in this area and is of practical utility for high density memory storage. The applications of the skyrmion drew many research interests after the recent findings on the interfacial DMI. Interfacial DMI is induced by Spin Orbit Coupling (SOC) between the exchange interaction between magnetic layers and heavy metal. This interfacial DMI gives the magnetic skyrmion stabilized topological structure, stabilizing it from annihilating at room temperature, hence making it feasible as a magnetic data memory device. In order to make use of this skyrmion as memory devices, it is crucial to understand its dynamics. However, there are limited studies in the non accelerating skyrmions dynamics. This project will introduce a new technique to quantify effective skyrmion mass in Synthetic Antiferromagnetic (SAF) layers. SAF was chosen because the interlayer coupling in SAF structure mitigate the occurrence of the skyrmion Hall effect. This avoids the skyrmion annihilation at the edge of the material. The aim of the thesis is to find the effective mass of magnetic skyrmions through extensive micromagnetic simulation driven by an alternating current in a SAF structure. The skyrmion dynamics is modelled using simple harmonic oscillator where the interlayer coupling force provides the restoring force in the system. These findings are further extended by observing the dependence of the effective mass on the materials parameter.Bachelor of Science in Physic

Stacking spatial-temporal deep learning on inertial data for human activity recognition

Insufficient physical activity has negative effects on quality of life and mental health. Further, physical inactivity is one of the top ten risk factors for mortality. Regular recognition and self-monitoring of physical activity are in the hope to encourage users to stay active. One such application is through intelligent human activity recognition which is usually embedded in ambient assisted living systems. A spatial-temporal deep learning is proposed in this paper for smartphone-based intelligent human physical activity recognition. In this work, a stacking spatial-temporal deep model is devised to extract deep spatial and temporal features of inertial data. In the proposed system, a convolutional architecture is pipelined with Bidirectional Long Short Term Memory to encapsulate the spatial and temporal state dependencies of the motion data. Support Vector Machine is adopted as the classifier to distinguish human activities. Empirical results demonstrate that the proposed system exhibits promising performances on two public datasets (UC Irvine dataset and Wireless Sensor Data Mining database) with 92% and 87% accuracy, respectively