Genomics: Difference between revisions
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NGS can provide truckloads of genome data faster than participants can be recruited. Because of this, NGS was supposed to usher in an era of endless GWAS fruits... | NGS can provide truckloads of genome data faster than participants can be recruited. Because of this, NGS was supposed to usher in an era of endless GWAS fruits... | ||
{{ExpandBox|width=50%| | {{ExpandBox|margin=0 0 0 25%|width=50%|sz=28px|How has it done so far?|endless garbage}} | ||
We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section. | We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section. | ||
Latest revision as of 18:32, 18 January 2018
Genome-wide association studies (GWAS) are aimed at detecting variants at genomic loci that are associated with complex traits and biological phenotypes in the population. In particular, many GWAS are focused on detecting associations between single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, and dementia.
Next-generation sequencing (NGS) now allows for extremely large GWAS datasets. Some include tens of thousands of whole-exome (WES) or whole-genome sequences (WGS), along with a variety of background and phenotype information about their contributing participant.
To recap, NGS provides WES and WGS so GWAS can find SNPs. Got it? ;)
NGS can provide truckloads of genome data faster than participants can be recruited. Because of this, NGS was supposed to usher in an era of endless GWAS fruits...
How has it done so far?
endless garbage
We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.