16 research outputs found
Serum retinol binding protein 4 is negatively related to estrogen in Chinese women with obesity: a cross-sectional study
A computerized statistical framework for coalescent analysis
Coalescent theory represents the most significant progress in theoretical population genetics in the past three decades. The coalescent theory states that all genes or alleles in a given population are ultimately inherited from a single ancestor shared by all members of the population, known as the most recent common ancestor. It is now widely recognized as a cornerstone for rigorous statistical analyses of molecular data from population [1]. The scientists have developed a large number of coalescent models and methods[2,3,4,5,6], which are not only applied in coalescent analysis and process, but also in today’s population genetics and genome studies, even public health. The thesis aims at completing a statistical framework based on computers for coalescent analysis. This framework provides a large number of coalescent models and statistic methods to assist students and researchers in coalescent analysis, whose results are presented in various formats as texts, graphics and printed pages. In particular, it also supports to create new coalescent models and statistical methods
Using Mata to import Illumina SNP chip data for genome-wide association studies
Modern genetic genome-wide association studies (GWAS) typically rely on single nucleotide polymorphism (SNP) chip technology to determine hundreds of thousands of genotypes for an individual sample. Once these genotypes are ascertained, each SNP alone or combination is tested for association outcomes of interest such as disease status or severity. Project Heartbeat! was a longitudinal study conducted in the 1990’s that explored changes in lipids, hormones and morphological changes in children from age 8 to 18 years of age. A GWAS study is currently being conducted to look for SNPs that are associated with these developmental changes. While there are specialty programs available for the analysis of hundreds of thousands of SNPs they are not capable of modeling longitudinal data. Stata is well equipped for modeling longitudinal data but cannot load hundreds of thousands of variables into memory simultaneously. This talk will briefly describe the use of Mata to import hundreds of thousands of SNPs from the Illumina SNP chip platform and how to load that data into Stata for longitudinal modeling.
Using Mata to import Illumina SNP chip data for genome-wide association studies
Modern genetic genome-wide association studies typically rely on single nucleotide polymorphism (SNP) chip technology to determine hundreds of thousands of genotypes for an individual sample. Once these genotypes are ascertained, each SNP alone or in combination is tested for association outcomes of interest such as disease status or severity. Project Heartbeat! was a longitudinal study conducted in the 1990s that explored changes in lipids and hormones and morphological changes in children from 8 to 18 years of age. A genome-wide association study is currently being conducted to look for SNPs that are associated with these developmental changes. While there are specialty programs available for the analysis of hundreds of thousands of SNPs, they are not capable of modeling longitudinal data. Stata is well equipped for modeling longitudinal data but cannot load hundreds of thousands of variables into memory simultaneously. This talk will briefly describe the use of Mata to import hundreds of thousands of SNPs from the Illumina SNP chip platform and how to load those data into Stata for longitudinal modeling.
Hunting for genes with longitudinal phenotype data using Stata
Project Heartbeat! was a longitudinal study of metabolic and morphological changes in adolescents aged 8–18 years and was conducted in the 1990s. A study is currently being conducted to consider the relationship between a collection of phenotypes (including BMI, blood pressure, and blood lipids) and a panel of 1,500 candidate SNPs (single nucleotide polymorphisms). Traditional genetics software such as PLINK and HelixTree lacks the ability to model longitudinal phenotype data. This talk will describe the use of Stata for a longitudinal genetic association study from the early stages of data checking (allele frequencies and Hardy-Weinberg equilibrium), modeling of individual SNPs, the use of false discovery rates to control for the large number of comparisons, exporting and importing data through PHASE for haplotype reconstruction, selection of tagSNPs in Stata, and the analysis of haplotypes. We will also discuss strategies for scaling up to an Illumina 100k SNP chip using Stata. All SNP and gene names will be de-identified, because this is a work in progress.
Hunting for Genes with Longitudinal Phenotype Data Using Stata
Project Heartbeat! was a longitudinal study of metabolic and morphological changes in adolescents aged 8-18 years and was conducted in the 1990s. A study is currently being conducted to consider the relationship between a collection of phenotypes including BMI, blood pressure and blood lipids and a panel of 1500 candidate SNPs (single nucleotide polymorphisms). Traditional genetics software such as PLINK and HelixTree lacks the ability to model longitudinal phenotype data. This talk will describe the use of Stata for a longitudinal genetic association study from the early stages of data checking (allele frequencies and Hardy-Weinberg Equilibrium), modeling of individual SNPs, the use of False Discovery Rates to control for the large number of comparisons, exporting and importing the data through PHASE for haplotype reconstruction, selection of tagSNPs in Stata, and the analysis of haplotypes. We will also discuss strategies for scaling up to an Illumina 100k SNP chip using Stata. All SNP and gene names will be de-identified as this is a work in progress.
10-Fold-Stack Multilayer-Grown Nanomembrane GaAs Solar Cells
Multilayer-grown
nanomembrane GaAs represents an enabling materials
platform for cost-efficient III–V photovoltaics. Herein we
present for the first time 10-fold-stack ultrathin (emitter + base:
300 nm) GaAs solar cells. Photovoltaic performance of 10-fold-stack
GaAs solar cells exhibited promising uniformity, with only slight
efficiency degradation, where comparatively poor short-wavelength
response was mainly responsible for the slightly reduced performance
in early grown materials. Secondary ion mass spectrometry revealed
the concentration of p-type dopant has been changed due to the out-diffusion
of beryllium, while the extent of diffusion increasingly diminished
in early grown stacks because of the reduced concentration gradient
as well as the decrease of beryllium diffusivity at longer annealing
times. It is therefore concluded that the performance degradation
in 10-fold-stack GaAs solar cells does not develop continuously throughout
the growth, but instead becomes spontaneously saturated at longer
growth times, providing promising outlook for the practical application
of multilayer epitaxy toward cost-competitive GaAs solar cells
Multilayer-Grown Ultrathin Nanostructured GaAs Solar Cells as a Cost-Competitive Materials Platform for III–V Photovoltaics
Large-scale deployment of GaAs solar
cells in terrestrial photovoltaics
demands significant cost reduction for preparing device-quality epitaxial
materials. Although multilayer epitaxial growth in conjunction with
printing-based materials assemblies has been proposed as a promising
route to achieve this goal, their practical implementation remains
challenging owing to the degradation of materials properties and resulting
nonuniform device performance between solar cells grown in different
sequences. Here we report an alternative approach to circumvent these
limitations and enable multilayer-grown GaAs solar cells with uniform
photovoltaic performance. Ultrathin single-junction GaAs solar cells
having a 300-nm-thick absorber (<i>i.e</i>., emitter and
base) are epitaxially grown in triple-stack releasable multilayer
assemblies by molecular beam epitaxy using beryllium as a p-type impurity.
Microscale (∼500 × 500 μm<sup>2</sup>) GaAs solar
cells fabricated from respective device layers exhibit excellent uniformity
(<3% relative) of photovoltaic performance and contact properties
owing to the suppressed diffusion of p-type dopant as well as substantially
reduced time of epitaxial growth associated with ultrathin device
configuration. Bifacial photon management employing hexagonally periodic
TiO<sub>2</sub> nanoposts and a vertical p-type metal contact serving
as a metallic back-surface reflector together with specialized epitaxial
design to minimize parasitic optical losses for efficient light trapping
synergistically enable significantly enhanced photovoltaic performance
of such ultrathin absorbers, where ∼17.2% solar-to-electric
power conversion efficiency under simulated AM1.5G illumination is
demonstrated from 420-nm-thick single-junction GaAs solar cells grown
in triple-stack epitaxial assemblies