Cancers are composed of populations of cells with distinct molecular and phenotypic features, a phenomenon termed intra-tumor heterogeneity (ITH). ITH in lung cancers has not been well studied. We ...applied multi-region whole exome sequencing (WES) on 11 localized lung adenocarcinomas. All tumors showed clear evidence of ITH. On average, 76% of all mutations and 20/21 known cancer gene mutations were identified in all regions of individual tumors suggesting single-region sequencing may be adequate to identify the majority of known cancer gene mutations in localized lung adenocarcinomas. With a median follow-up of 21 months post-surgery, 3 patients have relapsed and all 3 patients had significantly larger fractions of subclonal mutations in their primary tumors than patients without relapse. These data indicate larger subclonal mutation fraction may be associated with increased likelihood of postsurgical relapse in patients with localized lung adenocarcinomas.
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 methods2,3,4,5,6, 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.
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.
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 1990âeuro(TM)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.
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.
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.
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.
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.
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.
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