vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-26-28vavilov-5037Research ArticleМЕДИЦИНСКАЯ ГЕНЕТИКАMEDICAL GENETICSГенетические корреляции между болезнями человека и плейотропные геныPleiotropic genes underlying genetic correlations across human diseasesЗоркольцеваИ. В.ZorkoltsevaI. V.

Новосибирск

Novosibirsk

zor@bionet.nsc.ruБелоноговаН. М.BelonogovaN. M.

Новосибирск

Novosibirsk

КириченкоА. В.KirichenkoA. V.

Новосибирск

Novosibirsk

ЦепиловЯ. А.TsepilovY. A.

Новосибирск

Novosibirsk

АксеновичТ. И.AxenovichT. I.

Новосибирск

Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesRussian Federation202606042026302250258Copyright © Зоркольцева И.В., Белоногова Н.М., Кириченко А.В., Цепилов Я.А., Аксенович Т.И., 20262026Зоркольцева И.В., Белоногова Н.М., Кириченко А.В., Цепилов Я.А., Аксенович Т.И.Zorkoltseva I.V., Belonogova N.M., Kirichenko A.V., Tsepilov Y.A., Axenovich T.I.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/5037

Характеристикой глобальной генетической общности признаков человека считается их генетическая корреляция. Ее основной механизм – плейотропия, проявляющаяся на различных уровнях. Наиболее интересна плейотропия на уровне генов, поскольку именно они являются фундаментальными функциональными единицами генома. Используя результаты полногеномного анализа ассоциаций 324 болезней, находящиеся в открытом доступе, мы отобрали группу из 45 болезней, в которой каждая пара показала значимую генетическую корреляцию. Эти болезни принадлежали 10 нозологическим категориям. Поиск генов с плейотропными эффектами осуществляли с помощью трех подходов: полногеномного анализа ассоциаций на уровне гена; выбора однонуклеотидных полиморфизмов (SNP) внутри кодирующей части гена, значимо ассоциированных хотя бы с двумя болезнями; и метаанализа сигналов ассоциации SNP со всеми болезнями с последующей идентификацией независимых локусов и приоритизации генов в них. Для всех отобранных таким образом генов мы провели биоинформатический анализ. Всего было идентифицировано 167 плейотропных генов, вовлеченных в контроль 39 болезней. Наиболее плейотропными в нашем исследовании были гены LPA, TCF7L2, SLC22A3, FES, CDKN2B и APOE, каждый из которых контролировал семь-девять болезней. Мы провели биоинформатический анализ всех генов и показали, что найденные нами для 39 болезней плейотропные гены участвуют в контроле еще 501 заболевания. Полученные данные указывают на высокую плейотропную способность этих генов, которая обеспечивается их участием в различных биологических процессах, таких как поддержание гомеостаза, межклеточная сигнализация, регуляция клеточной пролиферации, транспорт веществ и каталитическая активность, а также выполнением ими различных молекулярных функций, в частности связывания с сигнальными рецепторами. Таким образом, мы показали, что 87 % болезней, представляющих полносвязную группу, имеют общие гены с хотя бы еще одной болезнью. Это свидетельствует о том, что генетические корреляции между болезнями в значительной степени обусловлены плейотропными эффектами генов.

Genetic correlation is a key characteristic of the global genetic similarity of human traits. Its primary underlying mechanism is pleiotropy, which operates at various biological levels. Gene-level pleiotropy is of particular interest, as genes are the fundamental functional units of the genome. Using publicly available results from genome-wide association studies for 324 diseases, we selected a set of 45 diseases in which every pair exhibited a significant genetic correlation. These diseases belonged to 10 nosological categories. The search for genes with pleiotropic effects was carried out using three approaches: (1) gene-based association analysis, (2) selection of single nucleotide polymorphisms (SNP) within gene coding regions significantly associated with at least two diseases, and (3) a cross-trait meta-analysis of SNP association signals followed by the identification of independent loci and gene prioritization within those loci. A comprehensive bioinformatic analysis was performed on all genes identified through these methods. We identified 167 pleiotropic genes implicated in 39 diseases. The most pleiotropic genes in our study were LPA, TCF7L2, SLC22A3, FES, CDKN2B, and APOE, which were associated with 7 to 9 diseases each. Bioinformatic analysis revealed that the pleiotropic genes identified for these 39 diseases are also involved in the genetic architecture of 501 other diseases and traits. This indicates a high degree of pleiotropy, facilitated by the involvement of these genes in diverse biological processes – including homeostasis, cell-cell signaling, regulation of cell proliferation, transport, and catalytic activity – and various molecular functions, such as signaling receptor binding. Thus, we demonstrated that 87% of diseases within a fully connected correlation network share associated genes with at least one other disease. This finding strongly suggests that genetic correlations between human diseases are largely driven by the pleiotropic effects of shared genes.

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The authors declare that there are no conflicts of interest present.