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See detailGWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture
Stevelink, Remi; Campbell, Ciarán; Chen, Siwei et al

in Nature Genetics (2023)

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association ... [more ▼]

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6 and 90 of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment. [less ▲]

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See detailAssessing the performance of European-derived cardiometabolic polygenic risk scores in South-Asians and their interplay with family history
Hassanin, Emadeldin Saeed Fathy Sayed UL; May, Patrick UL; Bobbili, Dheeraj Reddy UL

in BMC Medical Genomics (2023)

Background & aims We aimed to assess the performance of European-derived polygenic risk scores (PRSs) for common metabolic diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D ... [more ▼]

Background & aims We aimed to assess the performance of European-derived polygenic risk scores (PRSs) for common metabolic diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D) in the South Asian (SAS) individuals in the UK Biobank. Additionally, we studied the interaction between PRS and family history (FH) in the same population. Methods To calculate the PRS, we used a previously published model derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. Each PRS was adjusted according to an individual’s genotype location in the principal components (PC) space to derive an ancestry adjusted PRS (aPRS). We calculated the percentiles based on aPRS and stratified individuals into three aPRS categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Further, we measured the combined role of aPRS and first-degree family history (FH) in the SAS population. Results The risk of developing severe obesity for SAS individuals was almost twofold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 1.95 (95% CI = 1.71–2.23, P < 0.01). At the same time, the risk of severe obesity was lower in the low-aPRS group (OR = 0.60, CI = 0.53–0.67, P < 0.01). Results in the same direction were found in the EUR data, where the low-PRS group had an OR of 0.53 (95% CI = 0.51–0.56, P < 0.01) and the high-PRS group had an OR of 2.06 (95% CI = 2.00-2.12, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS are associated with a higher risk of these diseases, implying a greater genetic predisposition. Conclusion Our findings suggest that CAD, obesity, and T2D GWAS summary statistics generated predominantly from the EUR population can be potentially used to derive aPRS in SAS individuals for risk stratification. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, the predictive power of PRS is likely to improve further. [less ▲]

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See detailClinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence
Hassanin, Emadeldin Saeed Fathy Sayed UL; Spier, Isabel; Bobbili, Dheeraj Reddy UL et al

in BMC Medical Genomics (2023), 16(1), 42

Background and aims: Summarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification. Methods ... [more ▼]

Background and aims: Summarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification. Methods: To assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes ( APC, MLH1, MSH2, MSH6, PMS2) , 2. low (\textless 20\%), intermediate (20–80\%), or high PRS (\textgreater 80\%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively. Results: Depending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22\%, compared to 40 and 74 for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26 for non-carriers and 98 for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704). Conclusion: The findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups. [less ▲]

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See detailTransferability of European-derived cardiometabolic polygenic risk scores in the South Asians and their interplay with family history 2023.03.20.23287470
Hassanin, Emadeldin Saeed Fathy Sayed UL; Maj, Carlo; Krawitz, Peter et al

E-print/Working paper (2023)

Background & Aims We aimed to investigate the effect of polygenic risk scores (PRSs) derived from individuals of European (EUR) ancestry on common diseases among individuals of South Asian (SAS) ancestry ... [more ▼]

Background & Aims We aimed to investigate the effect of polygenic risk scores (PRSs) derived from individuals of European (EUR) ancestry on common diseases among individuals of South Asian (SAS) ancestry in the UK Biobank (UKB). Additionally, we studied the interaction between PRS and family history (FH) in the same population.Methods To calculate the PRS, we used a previously published panel of SNPs derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. We applied the PRS using summary statistics from genome-wide association studies (GWAS) for cardiometabolic and lifestyle diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D). Each PRS was adjusted according to an individual\textquoterights predicted genetic ancestry to derive an adjusted PRS (aPRS). We calculated the percentiles based on aPRS and divided them according to the percentiles into three categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates.Results The risk of developing severe obesity for individuals of SAS ancestry was almost threefold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 3.67 (95% CI = 2.47-5.48, P < 0.01). While the risk of severe obesity was lower in the low-aPRS group (OR = 0.19, CI = 0.05\textendash0.52, P < 0.01). Comparable results were found in the EUR data, where the low-PRS group had an OR of 0.26 (95% CI= 0.24-0.3, P < 0.01) and the high-PRS group had an OR of 3.2 (95% CI = 3.1-3.3, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS exhibit further higher risk to these diseases, thereby implying a greater genetic predisposition to these conditions.Conclusion Our findings suggest that using CAD, obesity, and T2D GWAS summary statistics predominantly from the EUR population have sufficient power to identify SAS individuals with higher genetic risk. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, we believe that the predictive power of PRS would improve. [less ▲]

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