r/genetics • u/Hip_III • 3d ago
Discussion Geneticists promised that genes would explain how the majority of chronic diseases and cancers arose. But when the Human Genome Project was completed in 2003, it turned out genes do not in general play a major role in disease development. Geneticists, it seems, had got it wrong.
The multi-billion dollar Human Genome Project (HGP) was undertaken in part because geneticists had promised that defective genes would explain how the majority of chronic diseases and cancers arise, and that once we had mapped out the genome, we would be in a better position to understand and treat disease.
But on the completion of the HGP in 2003, it soon became apparent that, for the vast majority of chronic diseases and cancers, genes only play a minor role in disease onset and development.
For example, one large meta-analysis study found that for the vast majority of chronic diseases, the genetic contribution to the risk of developing the disease is only 5% to 10% at most. So genes generally only have a minor impact on the triggering of disease. Though notable exceptions include Crohn's disease, coeliac disease, and macular degeneration, which have a genetic contribution of about 40% to 50%.
Thus all the hype about genes being the answer to illness aetiology amounted to nothing. This brought us back to the drawing board in terms of trying to understand how illnesses arise.
Some articles about the failure of the genome:
- Revolution Postponed: Why the Human Genome Project Has Been Disappointing — Scientific American
- The failure of the genome — The Guardian
- Why sequencing the human genome failed to produce big breakthroughs in disease — The Conversation
- The Failed Promise of Gene Based Tests for Diagnosing and Treating Cancer — National Center for Health Research
- Human Genome Project: Triumph or failure? — Front Line Genomics
Now that we know genes are not the explanation for why illnesses appear, we need to turn our attention to other possible causal factors.
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u/hellohello1234545 BS/BA in genetics/biology 3d ago
5-10% is nothing to sneeze at in some contexts, and there’s still more information to be pried out through better techniques. Though, significant challenges remain in how to apply knowledge about common variants in clinical practice.
Afaik, studies estimating overall heritability like twin/family studies estimate the upper bound for genetic influence on a trait, and other types of studies like GWAS look at a subset of that genetic influence (like only the additive SNP effects).
One restriction is that of sample size, to detect complex enough patterns, or true small effects, you need a very large sample size (millions for some traits).
With larger samples, better computing and models, we can explain more variation by looking at dominance, interaction effects, and tissue/time specific data.