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9 Publications visible to you, out of a total of 9
WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Abstract (Expand)

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, … however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother's, and, to a lesser extent, with father's TL having the strongest influence on the offspring. In this cohort, mother's, but not father's age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait.

Authors: Lilit Nersisyan, Maria Nikoghosyan, Laurent C. Francioli, Androniki Menelaou, Sara L. Pulit, Clara C. Elbers, Wigard P. Kloosterman, Jessica van Setten, Isaäc J. Nijman, Ivo Renkens, Paul I. W. de Bakker, Freerk van Dijk, Pieter B. T. Neerincx, Patrick Deelen, Alexandros Kanterakis, Martijn Dijkstra, Heorhiy Byelas, K. Joeri van der Velde, Mathieu Platteel, Morris A. Swertz, Cisca Wijmenga, Pier Francesco Palamara, Itsik Pe’er, Kai Ye, Eric-Wubbo Lameijer, Matthijs H. Moed, Marian Beekman, Anton J. M. de Craen, H. Eka D. Suchiman, P. Eline Slagboom, Victor Guryev, Abdel Abdellaoui, Jouke Jan Hottenga, Mathijs Kattenberg, Gonneke Willemsen, Dorret I. Boomsma, Elisabeth M. van Leeuwen, Lennart C. Karssen, Najaf Amin, Fernando Rivadeneira, Aaron Isaacs, Albert Hofman, André G. Uitterlinden, Cornelia M. van Duijn, Mannis van Oven, Manfred Kayser, Martijn Vermaat, Jeroen F. J. Laros, Johan T. den Dunnen, David van Enckevort, Hailiang Mei, Mingkun Li, Mark Stoneking, Barbera D. C. van Schaik, Jan Bot, Tobias Marschall, Alexander Schönhuth, Jayne Y. Hehir-Kwa, Robert E. Handsaker, Paz Polak, Mashaal Sohail, Dana Vuzman, Karol Estrada, Steven A. McCarroll, Shamil R. Sunyaev, Fereydoun Hormozdiari, Vyacheslav Koval, Carolina Medina-Gomez, Ben Oostra, Jan H. Veldink, Leonard H. van den Berg, Steven J. Pitts, Shobha Potluri, Purnima Sundar, David R. Cox, Peter de Knijff, Qibin Li, Yingrui Li, Yuanping Du, Ruoyan Chen, Hongzhi Cao, Jun Wang, Ning Li, Sujie Cao, Jasper A. Bovenberg, Gert-Jan B. van Ommen, Arsen Arakelyan

Date Published: 10th Dec 2019

Publication Type: Journal

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians – A Machine Learning Approach

Abstract (Expand)

Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, … associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied. Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians. Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations. Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions. Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.

Authors: Maria Nikoghosyan, Siras Hakobyan, Anahit Hovhannisyan, Henry Loeffler-Wirth, Hans Binder, Arsen Arakelyan

Date Published: 26th Apr 2019

Publication Type: Journal

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