Improving the personalized prediction of complex traits and diseases: application to type 2 diabetes
Kuupäev
2022-07-19
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Tänapäeva 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.
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.
Kirjeldus
Väitekirja elektrooniline versioon ei sisalda publikatsioone
Doktoritöö kaitsmine toimub Groningeni Ülikoolis 7. septembril 2022
Doktoritöö kaitsmine toimub Groningeni Ülikoolis 7. septembril 2022
Märksõnad
diseases, prevention, diabetes