Automated quality control for genome wide association studies
نویسندگان
چکیده
منابع مشابه
Automated quality control for genome wide association studies
This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts an...
متن کاملQuality control for genome-wide association studies.
This chapter is a comprehensive review of quality control (QC) methods for SNP-based genotyping panels used in genome-wide association studies. These include QC on individuals for missingness, gender checks, duplicates and cryptic relatedness, population outliers, heterozygosity and inbreeding, and QC on SNPs for missingness, minor allele frequency and Hardy-Weinberg equilibrium. The emphasis i...
متن کاملQuality control and quality assurance in genotypic data for genome-wide association studies.
Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of ...
متن کاملGenome-wide Association Studies
Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in par...
متن کاملGenome-wide Association Studies
Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in par...
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ژورنال
عنوان ژورنال: F1000Research
سال: 2016
ISSN: 2046-1402
DOI: 10.12688/f1000research.9271.1