Genomic Medicine–Progress, Pitfalls, and Promise

Shendure, Jay; Findlay, Gregory M.; Snyder, Matthew W.

doi:10.1016/j.cell.2019.02.003

Cited by 175 publications

(121 citation statements)

References 103 publications

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“…As fastPAINTOR is generally not designed to work with >10 annotations 18,19 (and was too slow in our simulations to estimate the significance of each annotation and include only conditionally significant annotations as done in ref. 18 ), we selected a subset of 10 highly informative annotations by (1) scoring each annotation based on its average contribution to effect variance | | across all SNPs, using the true of the generative model; (2) iteratively selecting top-ranked annotations such that no annotation has correlation >0.3 (in absolute value) with a previously selected annotation, until selecting 10 annotations. We determined that 10 annotations yielded approximately optimal power while maintaining correct calibration ( Supplementary Table 4).…”

Section: Fine-mapping Simulationsmentioning

confidence: 99%

“…Genome-wide association studies of complex traits have been extremely successful in identifying loci harboring causal variants but less successful in fine-mapping the underlying causal variants, making the development of fine-mapping methods a key priority 1,2 . Fine-mapping methods aim to pinpoint causal variants by accounting for linkage disequilibrium (LD) between variants [3][4][5][6][7][8][9][10][11][12] , but have limited power in the presence of strong LD.…”

Section: Introductionmentioning

confidence: 99%

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Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Weissbrod

Hormozdiari

Benner

et al. 2019

Preprint

162

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Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (and/or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winner's curse. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In analyses of 47 UK Biobank traits (average N=317K), PolyFun + SuSiE identified 3,025 fine-mapped varianttrait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were finemapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 25 (hair color) to 3,400 (height) to 550,000 (chronotype). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

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Section: Fine-mapping Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Weissbrod

Hormozdiari

Benner

et al. 2019

Preprint

162

View full text Add to dashboard Cite

show abstract

“…The mapping of the first human genome was completed in 2002 and costs $2.7 billion. Since that time, advances in technology alongside dramatic reduction in DNA sequencing costs (now less than $1000 for a whole human genome, although more than this for the interpretation in addition) have seen that whole genome sequencing (WGS) realises the potential to move from a predominately research setting into mainstream clinical practice …”

Section: Where Are We Today?mentioning

confidence: 99%

“…Since that time, advances in technology alongside dramatic reduction in DNA sequencing costs (now less than $1000 for a whole human genome, although more than this for the interpretation in addition) have seen that whole genome sequencing (WGS) realises the potential to move from a predominately research setting into mainstream clinical practice. 2 Every person has 4 to 5 million variants in the genome that make us unique individuals. Many of these variants do not have any implications for health, whereas others increase or decrease disease risk.…”

Section: Where Are We Today?mentioning

confidence: 99%

The New Genomics Era: Integration of genomics into mainstream oncology and implications for psycho‐oncological care

2020

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“…6,10,[34][35][36][37] However, the mechanistic underpinnings of the mutations have been limited and for allosteric mutations not linked to their driver/passenger identification, 38 although where they were taken up, the outcome was illuminating. 6,10,[34][35][36][37] However, the mechanistic underpinnings of the mutations have been limited and for allosteric mutations not linked to their driver/passenger identification, 38 although where they were taken up, the outcome was illuminating.…”

mentioning

confidence: 99%

Autoinhibition can identify rare driver mutations and advise pharmacology

2019

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Identification of protein mutations that drive cancer is a major challenge. A primary reason is that driver mutations are principally identified by their high frequency even though they can also be rare. Driver mutations can locate at functional (binding or active) sites. We dub these orthosteric drivers. However, often they are allosteric drivers. Identification is particularly formidable for rare allosteric drivers. Autoinhibition, where a segment of the protein covers its functional site, is a common allosteric regulation mechanism. A modest shift in the equilibrium can switch the system from the autoinhibited to the active state. This can suggest why (i) mutations are likely to evolve to target it; (ii) inhibitors can straightforwardly relieve the autoinhibition but not vice versa; and why (iii) mutations that relieve the autoinhibition are likely to be drivers—even if they are rare. We explain in simple terms the linkage between allosteric driver mutations, release of autoinhibition, free energy landscapes, and targeted pharmacology in precision medicine. We review the literature and propose new concepts in identification of rare drivers in this framework.

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Genomic Medicine–Progress, Pitfalls, and Promise

Cited by 175 publications

References 103 publications

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

The New Genomics Era: Integration of genomics into mainstream oncology and implications for psycho‐oncological care

Autoinhibition can identify rare driver mutations and advise pharmacology

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