Genetics and genomics · BEGINNER LESSON
Genome-wide
Genome-wide means covering the entire genome rather than a single gene or small set of genes. In these studies, measurements are taken across many locations in the genome so researchers can look for patterns linked to a trait or disease.
What it is
Genome-wide describes an approach that measures information across the whole genome, the full DNA content of an organism. In a genome-wide association study (GWAS), the measured data are genotype data for many polymorphisms spread across the genome, and the goal is to test whether any of them are associated with a phenotype, which is an observable trait or disease feature. Genome-wide can also be used for whole genome sequencing, which reads the entire genome, and for genome-wide expression or methylation studies that measure gene activity or DNA chemical marking across the genome.[0][1]
[1] [PDF] School of Nursing - Genome.gov [2] Chapter 11: Genome-Wide Association Studies
How genome-wide analysis works
The basic logic is to scan many genomic sites instead of starting with one candidate gene. In GWAS, the most common markers are single-nucleotide polymorphisms, or SNPs, which are single base-pair DNA differences. Each SNP is tested for association, meaning a statistical relationship, with the phenotype. Because nearby variants can be inherited together, a SNP can act as a marker for a larger region through linkage disequilibrium, which means alleles at nearby locations are correlated in a population.[0][1][2]
[1] [PDF] School of Nursing - Genome.gov [2] Chapter 11: Genome-Wide Association Studies [3] Benefits and limitations of genome-wide association studies - Nature Reviews Genetics
Why the approach is useful
Genome-wide studies are useful when the biology of a trait is not well understood, because they do not require a prior guess about which gene matters most. This is a non-parametric style of analysis in the sense that the data are allowed to reveal candidate regions. For common diseases, GWAS often focuses on common variants and may use large case-control samples to find modest effects that are spread across the genome.[0][1]
[1] [PDF] School of Nursing - Genome.gov [2] Chapter 11: Genome-Wide Association Studies
Limitations and interpretation
Genome-wide results need careful interpretation. A statistical association does not prove that the tested variant is the causal DNA change; it may only mark a nearby causal region. Genome-wide methods also have limits from missing data, platform-specific error rates, and the need for replication in an independent sample. In addition, when looking broadly across the genome, researchers may uncover incidental findings, meaning important results unrelated to the original question.[0][1][2]
[1] [PDF] School of Nursing - Genome.gov [2] Chapter 11: Genome-Wide Association Studies [3] Benefits and limitations of genome-wide association studies - Nature Reviews Genetics
SEE THE IDEA
How genome-wide study finds signals across the genome
The full set of DNA is available for analysis.
SNPs or other measurements are taken across the genome.
Each marker is checked for association with the phenotype.
Promising signals are replicated or fine-mapped to find the likely causal region.
Genome-wide studies begin with the whole genome, test many markers, and then narrow down interesting signals with follow-up studies.
Genome-wide means broad measurement first, then focused interpretation later.
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Sources and further reading
This general lesson was generated from public source material. It explains a concept; it does not interpret a private paper or provide medical advice.