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 μm²) 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 TiO₂ 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