Single Nucleotide Polymorphism Genotyping Using BeadChip Microarrays

Genetic data analysis of large numbers of single nucleotide variants (SNVs), including genome-wide association studies (GWAS), exome chips, and whole exome (WES) or whole-genome (WGS) sequencing data, requires well defined processing steps. As a result, several freely available analytic toolkits have been developed to streamline these processes. Among these, PLINK is the most comprehensive in terms of its quality control and analytic modules, although its focus remains on SNVs. PLINK fulfills two analytic needs-aiding the process of performing quality control (QC) on large data sets and providing basic statistical tools to analyze the variants in genetic models. The current version of PLINK (v1.90b) has incorporated several sophisticated statistical modeling features, such as those that were introduced by GCTA (genome-wide complex trait analysis), including mixed-model association analysis and cluster-based algorithms. Although PLINK is diverse in its applicability to data management and analysis, in some instances, other available tools offer more optimal options. Here we provide a practical overview of major PLINK features with respect to QC, data management, and association mapping, along with learned shortcuts and limitations to be considered. In cases where PLINK features are limited, we provide alternative approaches using additional freely available pipelines. © 2018 by John Wiley & Sons, Inc.

Section: Long Format (Lgen) Filesmentioning

confidence: 99%

PLINK: Key Functions for Data Analysis

Slifer

2018

“…Estimates of genetic sharing, often represented as a genetic relationship matrix (GRM), can then be used in a variety of statistical analyses, most notably for the estimation of “chip” heritability and to adjust single‐variant statistical analyses for sharing (due to various forms of sample stratification). These analyses are typically based on data from very large scale genome‐wide single nucleotide polymorphism (SNP) genotyping arrays ( UNIT here; Lambert et al., 2013).…”

Section: Key Conceptsmentioning

confidence: 99%

Analysis of Heritability Using Genome‐Wide Data

Hall

Bush

2016

Most analyses of genome-wide association data consider each variant independently without considering or adjusting for the genetic background present in the rest of the genome. New approaches to genome analysis use representations of genomic sharing to better account for confounding factors like population stratification, or to directly approximate heritability through the estimated sharing of individuals in a dataset. These approaches use mixed linear models, which relate genotypic sharing to phenotypic sharing, and rely on the efficient computation of genetic sharing among individuals in a dataset. This unit describes the principles and practical application of mixed-models for the analysis of genome-wide association study data.

“…All CFTR variants present at greater than 0.1% in a pan‐ethnic population comprise those currently recommended by the ACMG, so additional variants selected for testing by each assay may not align, as the allele frequency for each additional variant may be unknown and therefore the pathogenicity of each variant may not be as well understood. Some examples of the currently available FDA‐approved methods include oligonucleotide ligation assay (see UNIT here, Nickerson et al., ), fluorescent bead‐based array (see UNIT here, Lambert et al., ), and electrochemical detection in a microfluidic chamber. The majority of the variants tested by these assays are single‐nucleotide substitutions (both coding and intronic), though there are a handful of small deletions included as well (namely involving codons 507 and 508 in the CFTR gene).…”

Section: Strategic Planningmentioning

confidence: 99%

Molecular Diagnosis of Cystic Fibrosis

Deignan

Grody

2016

This unit describes a recommended approach to identifying causal genetic variants in an individual suspected of having cystic fibrosis. An introduction to the genetics and clinical presentation of cystic fibrosis is initially presented, followed by a description of the two main strategies used in the molecular diagnosis of cystic fibrosis: (1) an initial targeted variant panel used to detect only the most common cystic fibrosis-causing variants in the CFTR gene, and (2) sequencing of the entire coding region of the CFTR gene to detect additional rare causal CFTR variants. Finally, the unit concludes with a discussion regarding the analytic and clinical validity of these approaches.