Erratum: Detection and interpretation of shared genetic influences on 42 human traits

Pickrell, Joseph K.; Berisa, Tomaz; Liu, Jimmy Z; Ségurel, Laure; Tung, Joyce Y.; Hinds, David A.

doi:10.1038/ng1016-1296a

Cited by 17 publications

(5 citation statements)

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“…), with another eight distinct diseases showing detectable GDF5 signals via candidate gene association studies (Fig. 1, Supplementary Table 1; Supplementary Data 1 17,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] ). These findings make GDF5 one of the most replicated inherited genetic risk factors for a myriad of distinct joint and skeletal diseases.…”

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confidence: 99%

“…However, as alluded to there are no protein-coding mutations that can explain its population-level GWAS associations 46,47 , and recent association studies and numerous clinical observations suggest instead a highly modularized joint-specific response at GDF5. Therefore, common variants at this locus, which span a >100 kb risk haplotype containing much non-coding sequence 17,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]48 , likely have very specific impacts on individual joints, albeit in general, less is known regarding the causal variants at GDF5 underlying its multiple GWAS associations 17,20,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] .…”

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confidence: 99%

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Joint disease-specificity at the regulatory base-pair level

et al. 2021

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Given the pleiotropic nature of coding sequences and that many loci exhibit multiple disease associations, it is within non-coding sequence that disease-specificity likely exists. Here, we focus on joint disorders, finding among replicated loci, that GDF5 exhibits over twenty distinct associations, and we identify causal variants for two of its strongest associations, hip dysplasia and knee osteoarthritis. By mapping regulatory regions in joint chondrocytes, we pinpoint two variants (rs4911178; rs6060369), on the same risk haplotype, which reside in anatomical site-specific enhancers. We show that both variants have clinical relevance, impacting disease by altering morphology. By modeling each variant in humanized mice, we observe joint-specific response, correlating with GDF5 expression. Thus, we uncouple separate regulatory variants on a common risk haplotype that cause joint-specific disease. By broadening our perspective, we finally find that patterns of modularity at GDF5 are also found at over three-quarters of loci with multiple GWAS disease associations.

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confidence: 99%

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Joint disease-specificity at the regulatory base-pair level

et al. 2021

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“…For example, a particular locus might have opposing effects on 2 phenotypes, despite overall positive genetic correlation. 43 Thus, the determination of pleiotropy for individual risk variants must be accomplished separately, 57 and genetic correlations reported here will be underestimates, to the extent that the direction of shared effects is not consistent across all loci. 8 Finally, genetic correlation results apply to populations and not individuals .…”

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confidence: 91%

Genetic Correlation Profile of Schizophrenia Mirrors Epidemiological Results and Suggests Link Between Polygenic and Rare Variant (22q11.2) Cases of Schizophrenia

Duncan

Shen

Ballon

et al. 2017

Schizophrenia Bulletin

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New methods in genetics research, such as linkage disequilibrium score regression (LDSR), quantify overlap in the common genetic variants that influence diverse phenotypes. It is becoming clear that genetic effects often cut across traditional diagnostic boundaries. Here, we introduce genetic correlation analysis (using LDSR) to a nongeneticist audience and report transdisciplinary discoveries about schizophrenia. This analytical study design used publically available genome wide association study (GWAS) data from approximately 1.5 million individuals. Genetic correlations between schizophrenia and 172 medical, psychiatric, personality, and metabolomic phenotypes were calculated using LDSR, as implemented in LDHub in order to identify known and new genetic correlations. Consistent with previous research, the strongest genetic correlation was with bipolar disorder. Positive genetic correlations were also found between schizophrenia and all other psychiatric phenotypes tested, the personality traits of neuroticism and openness to experience, and cigarette smoking. Novel results were found with medical phenotypes: schizophrenia was negatively genetically correlated with serum citrate, positively correlated with inflammatory bowel disease, and negatively correlated with BMI, hip, and waist circumference. The serum citrate finding provides a potential link between rare cases of schizophrenia (strongly influenced by 22q11.2 deletions) and more typical cases of schizophrenia (with polygenic influences). Overall, these genetic correlation findings match epidemiological findings, suggesting that common variant genetic effects are part of the scaffolding underlying phenotypic comorbidity. The “genetic correlation profile” is a succinct report of shared genetic effects, is easily updated with new information (eg, from future GWAS), and should become part of basic disease knowledge about schizophrenia.

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“…By extension of this concept, mutations in a single‐gene could increase the risk of multiple seemingly unrelated diseases. In the case of PD, Pickrell et al 75 reported that a nonsynonymous variant in zinc transporter 8, SLC39A8 is associated with the risk of schizophrenia and PD. Furthermore, genetic links have been found between PD and many autoimmune diseases, including type 1 diabetes, Crohn's disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis 76 .…”

Section: Current and Potential Repurposing Strategies In Parkinson’s Diseasementioning

confidence: 99%