Improving the personalized prediction of complex traits and diseases: application to type 2 diabetes

Common complex diseases are among the top leading causes of death globally. Due to their heavy burden on the healthcare systems and on affected individuals themselves, scientists are searching for solutions to delay their onset or even better, to prevent them. Complex diseases result from many genetic and non-genetic (e.g. lifestyle and environment) factors and their interactions, but the specific risk factors differ between individuals. Therefore, prevention of such diseases requires a personalized approach that uses each person’s genetic and non-genetic information to predict his/her disease risk. In the current thesis, type 2 diabetes (T2D) was used as a model example of a common complex disease. T2D occurs when the blood sugar levels are too high and results in severe health complications when appropriate and timely treatment is not guaranteed. Many non-genetic factors have already been established as risk factors for T2D, however, the contributions of genetic risk factors and their interactions with non-genetic risk factors have been less explored. The current thesis presents methodological advancements to better use (epi)genetic information for risk prediction of T2D. It shows that genetic risk profiles can be improved by accounting for overestimation of genetic risk and by incorporating the ancestry of individuals in order to reduce health disparities. The thesis also discusses the current limitations of genetic risk profiles and summarizes the latest genomic advancements in general. In summary, this thesis brings personalized prediction a step closer to successful application with the goal to prevent disease and maintain good health for longer

Verbetering van de persoonlijke predictie van complexe eigenschappen en ziektes: een toepassing op Type 2 Diabetes

Väitekirja elektrooniline versioon ei sisalda publikatsiooneDoktoritöö kaitsmine toimub Groningeni Ülikoolis 7. septembril 2022Tänapäeva maailmas on komplekshaigused üheks juhtivaks haigestumuse ja suremuse põhjuseks. Komplekshaigused tekivad mitmete geneetiliste ja mitte-geneetiliste (nt elustiili ja keskkonna) riskitegurite ning nendevaheliste keerukate koosmõjude tulemusel. Kuna need haigused põhjustavad terviseprobleeme ja on liigselt koormavad tervishoiusüsteemidele, siis otsivad teadlased lahendusi, kuidas neid haigusi avastada veel enne nende väljakujunemist. On teada, et erinevused geneetiliste komponentide ja elustiili osas põhjustavad haigusriski varieerumist inimeste vahel, mistõttu üheks lahenduseks selliste keerukate haiguste ennetamisel peetakse personaalset lähenemist, mis inimese geneetilise ja mitte-geneetilise info põhjal ennustaks tema haigusriski. Käesolevas väitekirjas käsitleti komplekshaigusena teist tüüpi diabeeti (T2D), mis tekib kõrge veresuhkru taseme korral ning põhjustab õigeaegse ja korrektse ravi puudumisel tüsistusi. T2D teadaolevateks riskiteguriteks on kõrgem vanus, madal kehaline aktiivsus, liigne kaloraaž, madal sotsiaalmajanduslik staatus, suitsetamine ja alkoholi tarvitamine. Geneetilisi riskitegureid ja nende seoseid elustiili ning keskkondlike riskiteguritega on küll uuritud, aga haiguse keerukuse tõttu pole täpseid toimemehhanisme veel välja selgitatud. Seetõttu uuriti käesolevas väitekirjas inimese genoomi, et mõista, kuidas paremini kasutada geneetilist informatsiooni T2D riski prognoosimiseks. Selleks kasutati polügeenset riskiskoori (PRS), mis summeerib inimese haiguse tekke geneetilise riski ja mille abil tuvastatakse juba praegu kõrgesse T2D riskirühma kuuluvaid indiviide. Siiski on veel mitmeid vastakaid seisukohti praeguseks väljatöötatud PRS-ide geneetilise riski hindamises. Näiteks ei pruugi PRS-i ennustustäpsus olla piisav või seda ei ole võimalik arvutada iga indiviidi jaoks sarnaselt, kuna iga genoom on mõjutatud suure hulga riskitegurite poolt, mis võivad erinevates populatsioonides erineda. Käesoleva väitekirja teadusartiklitel põhinevad viis peatükki keskendusid personaliseeritud T2D ennetuse parendamisele geneetiliste meetodite kaudu, PRS-i metoodiliste piirangute käsitlemisele ning epigeneetiliste riskitegurite rolli uurimisele T2D korral. Esimeses peatükis valideeriti kahes suures Euroopa biopangas PRS-meetodina topeltkaalutud geneetiline riskiskoor. Teises peatükis töötati välja uued PRS-meetodid, et parandada PRS-i ülekantavust neile indiviididele, kelle esivanemad pärinevad erinevatest populatsioonidest, kelle genoomid olid segunenud ja keda oli tänu uudsete geneetiliste meetodite kasutamisele võimalik uurida. Kolmandas peatükis uuriti PRS-i ülekantavust kahe Euroopa populatsiooni vahel, kus genoomid võivad erineda mitmete populatsioonispetsiifiliste tegurite tõttu. Neljandas peatükis testiti metülatsiooniskooride (MS) seost T2D ja selle glükeemiliste endofenotüüpidega, et teha kindlaks, kas epigeneetilised mehhanismid vahendavad keskkonna ja geeni-keskkonna koosmõjusid T2D tekkes. Viiendas peatükis anti ülevaade genoomika valdkonna viimastest arengutest ning nende rakendamise võimalustest personaalmeditsiinis just Eesti Geenivaramu näitel. Töö tulemused näitasid, et topeltkaalutud GRS töötas paremini kui traditsiooniline GRS. Uued PRS-id, mis kasutasid geneetilise lookuse kindlast populatsioonist põlvnemise hindamise meetodit, parandasid komplekstunnuste prognoosimise täpsust hiljuti segunenud indiviidide puhul, kes varasemalt jäeti geneetilistest uuringutest välja, kuid uudse PRS meetodi tõttu on võimalik neid nüüd kaasata personaalmeditsiini uuringutesse. Uudne populatsioonistruktuuri korrigeerimisviis geneetilistes analüüsides ei parandanud PRS ülekantavust kahe Euroopa kohordi vahel ja isegi traditsioonilise lähenemisviisiga saadud PRS sisaldas populatsioonistruktuuri. MS-id näitasid, et epigeneetika on võimalik molekulaarne vahelüli, mis peegeldab keskkonna ja elustiili mõju T2D-le ja selle endofenotüüpidele. Töö tulemused näitavad komplekstunnuste ja -haiguste personaalse ennetuse täpsema ja laialdasema rakenduse võimalikkust, mis viib meid sammu lähemale personaalmeditsiinile eesmärgiga pikendada inimeste tervena elatud aastate arvu.In nowadays world, common complex diseases are among the top leading causes of death globally. These diseases result from many genetic and non-genetic (e.g. lifestyle and environment) factors and from interactions between them. Since such diseases have a high health burden for the affected individual and place a heavy load on the healthcare systems, scientists are searching for solutions to delay their onset or even better, to prevent them. Evidently, differences in genetic and non-genetic components result in variation in disease risk between individuals. Therefore, prevention of such complex diseases requires a personalized approach that uses each person’s genetic and non-genetic information to predict his or her disease risk. In the current thesis, type 2 diabetes (T2D) was used as a model example of a common complex disease, T2D occurs when the blood sugar levels are too high and results in severe health complications when appropriate and timely treatment is not guaranteed. Factors such as higher age, low physical activity, high calorie intake, low socioeconomic position, smoking, and alcohol consumption have already been established as risk factors for T2D. However, the contributions of genetic risk factors and their interactions with non-genetic risk factors have not been so well explored. Therefore, the current thesis zooms in on the human genome to understand how better to use genetic information for risk prediction of T2D, leveraging on recently developed polygenic risk score (PRS – a measure combining a person’s genetic risk for a disease) approaches. Such PRSs could already enable detection of the high-risk individuals for T2D according to their genetic composition at young ages before the onset of the disease. However, there are still several limitations regarding the use of a PRS in clinical practice as its performance does not reach to the estimated levels or it cannot be constructed for each individual in a similar way due to the population-specific risk factors, causing too low estimated risks when applied in non-Europeans or admixed individuals. Therefore, current thesis presents five chapters, which mainly focus on improving the personalized prediction via genetics, tackling the current methodological limitations for PRSs, plus investigating the role of epigenetic risk factors for T2D. In the first chapter, a PRS method (called doubly-weighted GRS) was validated in two European biobanks. In the second chapter, novel PRS methods were developed to improve the PRS transferability for individuals with admixed ancestry. In the third chapter, the PRS transferability issue was investigated on a finer-scale, that is, whether a principal component projection (a method to account for population structure) could mitigate the transferability issue between two European populations. In the fourth chapter, associations of methylation scores (MSs) with prevalent T2D and its glycemic endophenotypes were tested to see whether epigenetic mechanisms could represent environmental and gene-environment effects on top of the genetics. In the fifth chapter the latest advancements in the genomics field were discussed and how to apply these in the personalized medicine framework with the prime example of the Estonian Biobank. The findings of this thesis showed that the doubly-weighted GRS indeed performed better that the traditional GRS in both European biobanks. The novel PRSs, which used the information from the method estimating genetic ancestry in a specific genetic locus could improve the prediction for the recently admixed individuals. These PRS methods made it possible to include individuals and having them benefit from personalized prediction, who were previously just excluded from the genetic studies. The traditional population-specific principal components outperformed our approach. However, the resulting PRS still contained population structure. Lastly, MSs showed a promising trend towards representing the environmental triggers for T2D and its underlying traits. In summary, the doctoral thesis resulted in more accurate and broader application of personalized prediction for complex traits and diseases leading us a step closer to personalized medicine, which makes it easier to maintain health and to prolong healthy life years.https://www.ester.ee/record=b550890

Validating the doubly weighted genetic risk score for the prediction of type 2 diabetes in the Lifelines and Estonian Biobank cohorts

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high-risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide polymorphism in the risk score, to correct for “Winner's curse” bias in the effect size estimates. The traditional (single-weighted genetic risk score; swGRS) and dwGRS were calculated for participants in Lifelines (n = 12,018) and EstBB (n = 34,129). The dwGRS was found to have stronger association with incident T2D (hazard ratio [HR] = 1.26 [95% confidence interval: 1.10–1.43] and HR = 1.35 [1.28–1.42]) compared to the swGRS (HR = 1.21 [1.07–1.38] and HR = 1.25 [1.19–1.32]) in Lifelines and EstBB, respectively. Comparing the 5-year predicted risks from the models with and without the dwGRS, the continuous net reclassification index was 0.140 (0.034–0.243; p =.009 Lifelines), and 0.257 (0.194–0.319; p < 2 × 10−16 EstBB). The dwGRS provided incremental value to the T2D prediction model with established phenotypic predictors. It clearly distinguished the risk groups for incident T2D in both biobanks thereby showing its clinical relevance

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example.

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer

Mediators of the association between educational attainment and type 2 diabetes mellitus:a two-step multivariable Mendelian randomisation study

Aims/hypothesis: Type 2 diabetes mellitus is a major health burden disproportionately affecting those with lower educational attainment (EA). We aimed to obtain causal estimates of the association between EA and type 2 diabetes and to quantify mediating effects of known modifiable risk factors. Methods: We applied two-step, two-sample multivariable Mendelian randomisation (MR) techniques using SNPs as genetic instruments for exposure and mediators, thereby minimising bias due to confounding and reverse causation. We leveraged summary data on genome-wide association studies for EA, proposed mediators (i.e. BMI, blood pressure, smoking, television watching) and type 2 diabetes. The total effect of EA on type 2 diabetes was decomposed into a direct effect and indirect effects through multiple mediators. Additionally, traditional mediation analysis was performed in a subset of the National Health and Nutrition Examination Survey 2013–2014. Results: EA was inversely associated with type 2 diabetes (OR 0.53 for each 4.2 years of schooling; 95% CI 0.49, 0.56). Individually, the largest contributors were BMI (51.18% mediation; 95% CI 46.39%, 55.98%) and television watching (50.79% mediation; 95% CI 19.42%, 82.15%). Combined, the mediators explained 83.93% (95% CI 70.51%, 96.78%) of the EA–type 2 diabetes association. Traditional analysis yielded smaller effects but showed consistent direction and priority ranking of mediators. Conclusions/interpretation: These results support a potentially causal protective effect of EA against type 2 diabetes, with considerable mediation by a number of modifiable risk factors. Interventions on these factors thus have the potential of substantially reducing the burden of type 2 diabetes attributable to low EA

Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose

This upload includes the sample code that was used in the paper "Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose", which has been accepted for publication in Nature Communications. Abstract The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 hours of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 hours post meal (h2=11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N=518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve discovery of genetic variants associated with glucose homeostasis, even in fasting conditions

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer

Ul Mira 14, Saint Petersburg 197101, Russia 6 St. Petersburg Hospital No. 31, Pr. Dinamo 3, Saint Petersburg

The spread of carbapenemase-producing Enterobacteriaceae is a global problem; however, no exact data on the epidemiology of carbapenemase in the Baltic countries and St. Petersburg area is available. We aimed to evaluate the epidemiology of carbapenemaseproducing Escherichia coli and Klebsiella pneumoniae in the Baltic States and St. Petersburg, Russia, and to compare the different methods for carbapenemase detection. From January to May 2012, all K. pneumoniae ( = 1983) and E. coli ( = 7774) clinical isolates from 20 institutions in Estonia, Latvia, Lithuania, and St. Petersburg, Russia were screened for carbapenem susceptibility. The IMP, VIM, GIM, NDM, KPC, and OXA-48 genes were detected using real-time PCR and the ability to hydrolyze ertapenem was determined using MALDI-TOF MS. Seventy-seven strains were found to be carbapenem nonsusceptible. From these, 15 K. pneumoniae strains hydrolyzed ertapenem and carried the NDM gene. All of these strains carried integron 1 and most carried integron 3 as well as genes of the CTX-M-1 group. No carbapenemase-producing E. coli or K. pneumoniae strains were found in Estonia, Latvia, or Lithuania; however, NDM-positive K. pneumoniae was present in the hospital in St. Petersburg, Russia. A MALDI-TOF MS-based assay is a suitable and cost-effective method for the initial confirmation of carbapenemase production