Meta-analysis of sample-level dbGaP data reveals novel shared genetic link between body height and Crohn's disease

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P_FDR = 3.40 × 10^-5), CCR5 rs333 (OR = 1.99, P_FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P_FDR = 2.64 × 10^-4), TCF7L2 rs114758349 (OR = 1.77, P_FDR = 9.37 × 10^-5), and CCL2 rs1024611 (OR = 1.38, P_FDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10^-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.

Cross-ancestry genome-wide association studies identified heterogeneous loci associated with differences of allele frequency and regulome tagging between participants of European descent and other ancestry groups from the UK Biobank

To investigate cross-ancestry genetics of complex traits, we conducted a phenome-wide analysis of loci with heterogeneous effects across African, Admixed-American, Central/South Asian, East Asian, European and Middle Eastern participants of the UK Biobank (N = 441 331). Testing 843 phenotypes, we identified 82 independent genomic regions mapping variants showing genome-wide significant (GWS) associations (P < 5 × 10-8) in the trans-ancestry meta-analysis and GWS heterogeneity among the ancestry-specific effects. These included (i) loci with GWS association in one ancestry and concordant but heterogeneous effects among the other ancestries and (ii) loci with a GWS association in one ancestry group and an experiment-wide significant discordant effect (P < 6.1 × 10-4) in at least another ancestry. Since the trans-ancestry GWS associations were mostly driven by the European ancestry sample size, we investigated the differences of the allele frequency (ΔAF) and linkage disequilibrium regulome tagging (ΔLD) between European populations and the other ancestries. Within loci with concordant effects, the degree of heterogeneity was associated with European-Middle Eastern ΔAF (P = 9.04 × 10-6) and ΔLD of European populations with respect to African, Admixed-American and Central/South Asian groups (P = 8.21 × 10-4, P = 7.17 × 10-4 and P = 2.16 × 10-3, respectively). Within loci with discordant effects, ΔAF and ΔLD of European populations with respect to African and Central/South Asian ancestries were associated with the degree of heterogeneity (ΔAF: P = 7.69 × 10-3 and P = 5.31 × 10-3, ΔLD: P = 0.016 and P = 2.65 × 10-4, respectively). Considering the traits associated with cross-ancestry heterogeneous loci, we observed enrichments for blood biomarkers (P = 5.7 × 10-35) and physical appearance (P = 1.38 × 10-4). This suggests that these specific phenotypic classes may present considerable cross-ancestry heterogeneity owing to large allele frequency and LD variation among worldwide populations.