scholarly journals The population-specific impact of Neandertal introgression on human disease

2020 ◽  
Author(s):  
Michael Dannemann
Keyword(s):  
Prostate Cancer ◽  
Human Disease ◽  
European Ancestry ◽  
Summary Statistics ◽  
Behavioral Traits ◽  
Disease Phenotypes ◽  
Disease Associations ◽  
Specific Impact ◽  
People Today

Abstract Since the discovery of admixture between modern humans and Neandertals, multiple studies investigated the effect of Neandertal-derived DNA on human disease and non-disease phenotypes. These studies have linked Neandertal ancestry to skin and hair related phenotypes, immunity, neurological and behavioral traits. However, these inferences have so far been limited to cohorts with participants of European ancestry. Here, I analyze summary statistics from 40 disease GWAS cohorts of ∼212,000 individuals provided by the Biobank Japan Project for phenotypic effects of Neandertal DNA. I show that Neandertal DNA is associated with autoimmune diseases, prostate cancer and type 2 diabetes. Many of these disease associations are linked to population-specific Neandertal DNA, highlighting the importance of studying a wider range of ancestries to characterize the phenotypic legacy of Neandertals in people today.

scholarly journals Multi-ancestry gene-trait connection landscape using electronic health record (EHR) linked biobank data

2021 ◽  
Author(s):  
Binglan Li ◽  
Yogasudha Veturi ◽  
Anastasia Lucas ◽  
Yuki Bradford ◽  
Shefali Setia Verma ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Association Study ◽  
Complex Disease ◽  
The United States ◽  
European Ancestry ◽  
Health Record ◽  
Summary Statistics ◽  
A Genome ◽  
Disease Associations ◽  
Electronic Health

Understanding genetic factors of complex traits across ancestry groups holds a key to improve the overall health care quality for diverse populations in the United States. In recent years, multiple electronic health record-linked (EHR-linked) biobanks have recruited participants of diverse ancestry backgrounds; these biobanks make it possible to obtain phenome-wide association study (PheWAS) summary statistics on a genome-wide scale for different ancestry groups. Moreover, advancement in bioinformatics methods provide novel means to accelerate the translation of basic discoveries to clinical utility by integrating GWAS summary statistics and expression quantitative trait locus (eQTL) data to identify complex trait-related genes, such as transcriptome-wide association study (TWAS) and colocalization analyses. Here, we combined the advantages of multi-ancestry biobanks and data integrative approaches to investigate the multi-ancestry, gene-disease connection landscape. We first performed a phenome-wide TWAS on Electronic Medical Records and Genomics (eMERGE) III network participants of European ancestry (N = 68,813) and participants of African ancestry (N = 12,658) populations, separately. For each ancestry group, the phenome-wide TWAS tested gene-disease associations between 22,535 genes and 309 curated disease phenotypes in 49 primary human tissues, as well as cross-tissue associations. Next, we identified gene-disease associations that were shared across the two ancestry groups by combining the ancestry-specific results via meta-analyses. We further applied a Bayesian colocalization method, fastENLOC, to prioritize likely functional gene-disease associations with supportive colocalized eQTL and GWAS signals. We replicated the phenome-wide gene-disease analysis in the analogous Penn Medicine BioBank (PMBB) cohorts and sought additional validations in the PhenomeXcan UK Biobank (UKBB) database, PheWAS catalog, and systematic literature review. Phenome-wide TWAS identified many proof-of-concept gene-disease associations, e.g. FTO-obesity association (p = 7.29e-15), and numerous novel disease-associated genes, e.g. association between GATA6-AS1 with pulmonary heart disease (p = 4.60e-10). In short, the multi-ancestry, gene-disease connection landscape provides rich resources for future multi-ancestry complex disease research. We also highlight the importance of expanding the size of non-European ancestry datasets and the potential of exploring ancestry-specific genetic analyses as these will be critical to improve our understanding of the genetic architecture of complex disease.

scholarly journals Analysis of overlapping genetic association in type 1 and type 2 diabetes

2020 ◽  
Author(s):  
Jamie RJ Inshaw ◽  
Carlo Sidore ◽  
Francesco Cucca ◽  
M. Irina Stefana ◽  
Daniel J. M. Crouch ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Posterior Probability ◽  
Zinc Finger Protein ◽  
Meta Analysis ◽  
European Ancestry ◽  
Stepwise Logistic Regression ◽  
Summary Statistics

Aims/hypothesis: Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. Methods: Using publicly available type 2 diabetes summary statistics from a genomewide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate<0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined colocalisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a colocalisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases. Results: Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability ≥0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (PGM1). In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect. Conclusions/interpretation: That four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases.

scholarly journals Analysis of overlapping genetic association in type 1 and type 2 diabetes

Diabetologia ◽  
2021 ◽  
Author(s):  
Jamie R. J. Inshaw ◽  
Carlo Sidore ◽  
Francesco Cucca ◽  
M. Irina Stefana ◽  
Daniel J. M. Crouch ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Posterior Probability ◽  
Meta Analysis ◽  
European Ancestry ◽  
Stepwise Logistic Regression ◽  
Summary Statistics ◽  
A Genome

Abstract Aims/hypothesis Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. Methods Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases. Results Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect. Conclusions/interpretation Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases. Graphical abstract

Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

2020 ◽  
Author(s):  
John E. McGeary ◽  
Chelsie Benca-Bachman ◽  
Victoria Risner ◽  
Christopher G Beevers ◽  
Brandon Gibb ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Cognitive Reappraisal ◽  
Twin Studies ◽  
European Ancestry ◽  
Summary Statistics ◽  
Depression Severity ◽  
Uk Biobank ◽  
Polygenic Scores ◽  
Adults And Children ◽  
The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

Type 2 diabetes and the risk of mortality among patients with prostate cancer

2014 ◽  
Vol 25 (3) ◽  
pp. 329-338 ◽  
Author(s):  
Leah Bensimon ◽  
Hui Yin ◽  
Samy Suissa ◽  
Michael N. Pollak ◽  
Laurent Azoulay
Keyword(s):  
Prostate Cancer ◽  
Type 2 Diabetes ◽  
Risk Of Mortality

scholarly journals Prostate cancer risk variants of the HOXB genetic locus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
William D. Dupont ◽  
Joan P. Breyer ◽  
Spenser H. Johnson ◽  
W. Dale Plummer ◽  
Jeffrey R. Smith
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Cancer Genetics ◽  
Genetic Locus ◽  
Familial Cancer ◽  
Prostate Cancer Risk ◽  
European Ancestry ◽  
Ovarian Cancer Screening ◽  
Hereditary Prostate Cancer ◽  
Aggressive Disease

AbstractThe G84E germline mutation of HOXB13 predisposes to prostate cancer and is clinically tested for familial cancer care. We investigated the HOXB locus to define a potentially broader contribution to prostate cancer heritability. We sought HOXB locus germline variants altering prostate cancer risk in three European-ancestry case–control study populations (combined 7812 cases and 5047 controls): the International Consortium for Prostate Cancer Genetics Study; the Nashville Familial Prostate Cancer Study; and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Multiple rare genetic variants had concordant and strong risk effects in these study populations and exceeded genome-wide significance. Independent risk signals were best detected by sentinel variants rs559612720 within SKAP1 (OR = 8.1, P = 2E−9) and rs138213197 (G84E) within HOXB13 (OR = 5.6, P = 2E−11), separated by 567 kb. Half of carriers inherited both risk alleles, while others inherited either alone. Under mutual adjustment, the variants separately carried 3.6- and 3.1-fold risk, respectively, while joint inheritance carried 11.3-fold risk. These risks were further accentuated among men meeting criteria for hereditary prostate cancer, and further still for those with early-onset or aggressive disease. Among hereditary prostate cancer cases diagnosed under age 60 and with aggressive disease, joint inheritance carried a risk of OR = 27.7 relative to controls, P = 2E−8. The HOXB sentinel variant pair more fully captured genetic risk for prostate cancer within the study populations than either variant alone.

Prostate cancer genetic propensity risk score may modify the association between this tumour and type 2 diabetes mellitus (MCC-Spain study)

2021 ◽  
Author(s):  
Rocío Barrios-Rodríguez ◽  
Esther García-Esquinas ◽  
Beatriz Pérez-Gómez ◽  
Gemma Castaño-Vinyals ◽  
Javier Llorca ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Risk Score ◽  
Cancer Genetic
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