Mahmoud Amiri Roudbar, Seyed Milad Vahedi, Jin Jin, Mina Jahangiri, Hossein Lanjanian, Danial Habibi, Sajedeh Masjoudi, Parisa Riahi, Sahand Tehrani Fateh, Farideh Neshati, Asiyeh Sadat Zahedi, Maryam Moazzam-Jazi, Leila Najd-Hassan-Bonab, Seyedeh Fatemeh Mousavi, Sara Asgarian, Maryam Zarkesh, Mohammad Reza Moghaddas, Albert Tenesa, Anoshirvan Kazemnejad, Hassan Vahidnezhad, Hakon Hakonarson, Fereidoun Azizi, Mehdi Hedayati, Maryam Sadat Daneshpour, Mahdi Akbarzadeh
{"title":"家庭结构对 2 型糖尿病遗传率和基因组预测准确性的影响","authors":"Mahmoud Amiri Roudbar, Seyed Milad Vahedi, Jin Jin, Mina Jahangiri, Hossein Lanjanian, Danial Habibi, Sajedeh Masjoudi, Parisa Riahi, Sahand Tehrani Fateh, Farideh Neshati, Asiyeh Sadat Zahedi, Maryam Moazzam-Jazi, Leila Najd-Hassan-Bonab, Seyedeh Fatemeh Mousavi, Sara Asgarian, Maryam Zarkesh, Mohammad Reza Moghaddas, Albert Tenesa, Anoshirvan Kazemnejad, Hassan Vahidnezhad, Hakon Hakonarson, Fereidoun Azizi, Mehdi Hedayati, Maryam Sadat Daneshpour, Mahdi Akbarzadeh","doi":"10.1186/s40246-024-00669-7","DOIUrl":null,"url":null,"abstract":"This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of $$\\:{\\text{h}}^{2}$$ and $$\\:{\\text{h}}_{\\text{S}\\text{N}\\text{P}}^{2}$$ . A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.","PeriodicalId":13183,"journal":{"name":"Human Genomics","volume":"18 1","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes\",\"authors\":\"Mahmoud Amiri Roudbar, Seyed Milad Vahedi, Jin Jin, Mina Jahangiri, Hossein Lanjanian, Danial Habibi, Sajedeh Masjoudi, Parisa Riahi, Sahand Tehrani Fateh, Farideh Neshati, Asiyeh Sadat Zahedi, Maryam Moazzam-Jazi, Leila Najd-Hassan-Bonab, Seyedeh Fatemeh Mousavi, Sara Asgarian, Maryam Zarkesh, Mohammad Reza Moghaddas, Albert Tenesa, Anoshirvan Kazemnejad, Hassan Vahidnezhad, Hakon Hakonarson, Fereidoun Azizi, Mehdi Hedayati, Maryam Sadat Daneshpour, Mahdi Akbarzadeh\",\"doi\":\"10.1186/s40246-024-00669-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. 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The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes
This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of $$\:{\text{h}}^{2}$$ and $$\:{\text{h}}_{\text{S}\text{N}\text{P}}^{2}$$ . A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.
期刊介绍:
Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics.
Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.