Rare versus common diseases: a false dichotomy in precision medicine

Abstract

Precision medicine initiatives are being launched worldwide, each with the capacity to sequence many thousands to millions of human genomes. At the strategic planning level, all are debating the extent to which these resources will be directed towards rare diseases (and cancers) versus common diseases. However, these are not mutually exclusive choices. The organizational and governmental infrastructure created for rare diseases is extensible to common diseases. As we will explain, the underlying technology can also be used to identify drug targets for common diseases with a strategy focused on naturally occurring human knockouts. This flips on its head the prevailing modus operandi of studying people with diseases of interest, shifting the onus to defining traits worth emulating by pharmaceuticals, and searching phenotypically for people with these traits. This also shifts the question of what is rare or common from the many underlying causes to the possibility of a common final pathway.

Sub-cluster-aware Network for Few-shot Skin Disease Classification

“…In the past decade, advances in deep convolutional neural networks and the availability of a large amount of annotated images have continued to push the boundaries in a variety of medical image analysis tasks, such as organ segmentation (Yu et al, 2019;Taghanaki et al, 2019), tumor segmentation (C ¸ic ¸ek et al, 2016;Li et al, 2018;Isensee et al, 2021) and disease screening (Wang et al, 2017(Wang et al, , 2018Feng et al, 2020). Apart from the relatively sufficient examples of the common diseases, there are more than 6,000 known rare diseases, at much lower prevalence, affecting 7% of the population worldwide (Chung et al, 2021). The diagnosis of rare conditions for clinicians and machines has been challenging due to the lack of experiences and clinical samples.…”

The contribution of common regulatory and protein-coding TYR variants in the genetic architecture of albinism

“…1,2 Large-scale studies using genome sequencing are eroding this distinction and are gradually unmasking the underlying complexity of human traits. [3][4][5][6][7][8] We studied a cohort of 1,313 individuals with albinism aiming to gain insights into the genetic architecture of rare, autosomal recessive disorders.We investigated the contribution of regulatory and protein-coding variants at the common and rare ends of the allele-frequency spectrum. We focused on TYR, the gene encoding tyrosinase, and found that a promoter variant, TYR c.-301C>T[rs4547091], modulates the penetrance of a prevalent, disease-associated missense change, TYR c.1205G>A [rs1126809].…”

“…1,2 Large-scale studies using genome sequencing are eroding this distinction and are gradually unmasking the underlying complexity of human traits. [3][4][5][6][7][8] We studied a cohort of 1,313 individuals with albinism aiming to gain insights into the genetic architecture of rare, autosomal recessive disorders.…”

“…1,2 Large-scale studies using genome sequencing are eroding this distinction and are gradually unmasking the underlying complexity of human traits. [3][4][5][6][7][8] Aiming to gain insights into the genetic architecture of rare recessive disorders, we studied a cohort of 1,313 individuals with albinism, an archetypal Mendelian condition. We investigated the contribution of protein-coding and regulatory variants both rare and common.…”

Association Analysis Using Set-Based Approaches in the Post-GWAS Era

“…Another classification bases on the disease prevalence in populations, i.e., the number of affected individuals in a population for a particular disease thus dichotomised into a rare or common disease [4].…”