A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Korte, Arthur; Vilhjálmsson, Bjarni J.; Segura, Vincent; Platt, Alexander; Long, Quan; Nordborg, Magnus

doi:10.1038/ng.2376

Cited by 400 publications

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References 40 publications

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“…For instance, Maier et al (2015) used multivariate WGR models and reported estimates of genetic correlations between psychiatric disorders, and Furlotte and Eskin (2015) presented a methodology that incorporates genetic marker information for the analysis of multiple traits that, according to the authors, "provide fundamental insights into the nature of co-expressed genes." In a similar spirit, Korte et al (2012) argued that multitrait-marker-enabled regressions can be useful for understanding pleiotropy. More recently, Bulik-Sullivan et al (2015) proposed a methodology for "estimating genetic correlation" using statistics derived from single-marker genome-wide association studies (GWAS) and reported estimates of such correlations among 25 human traits.…”

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confidence: 99%

Do Molecular Markers Inform About Pleiotropy?

et al. 2015

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The availability of dense panels of common single-nucleotide polymorphisms and sequence variants has facilitated the study of statistical features of the genetic architecture of complex traits and diseases via whole-genome regressions (WGRs). At the onset, traits were analyzed trait by trait, but recently, WGRs have been extended for analysis of several traits jointly. The expectation is that such an approach would offer insight into mechanisms that cause trait associations, such as pleiotropy. We demonstrate that correlation parameters inferred using markers can give a distorted picture of the genetic correlation between traits. In the absence of knowledge of linkage disequilibrium relationships between quantitative or disease trait loci and markers, speculating about genetic correlation and its causes (e.g., pleiotropy) using genomic data is conjectural.KEYWORDS genetic correlation; genomic correlation; genomic heritability; linkage disequilibrium; pleiotropy T HE interindividual differences for a trait or disease risk that can be explained by genetic factors, such as trait heritability (h 2 ), the genetic correlation (r G ), and the coheritability between two traits (r G h 1 h 2 ), are very important parameters in quantitative genetic studies of animals, humans, and plants. These quantities play a role in the study of evolution due to artificial and natural selection, and knowledge thereof is required for statistical prediction of outcomes in animal and plant breeding as well as medicine. Traditionally, these parameters have been estimated using phenotypes and pedigrees, e.g., family and twin data in human genetics. The availability of dense panels of common single-nucleotide polymorphisms (SNPs) and of sequence data more recently has made it possible to assess kinship among distantly related individuals (Morton et al. 1971;Thompson 1975;Ritland 1996;Lynch and Ritland 1999). This development has opened new opportunities for study of the genetic architecture of complex traits and diseases. For instance, Yang et al. (2010) suggested using whole-genome regressions (WGRs) (Meuwissen et al. 2001) to assess the proportion of variance of a trait or disease risk that can be explained by a regression of phenotypes on common SNPs or genomic heritability and a related parameter, the "missing heritability." More recently, WGR models have been extended for the analysis of systems of multiple traits, so the concept of genomic correlation also has entered into the picture (Jia and Jannink 2012; Lee et al. 2012). For instance, Maier et al. (2015) used multivariate WGR models and reported estimates of genetic correlations between psychiatric disorders, and Furlotte and Eskin (2015) presented a methodology that incorporates genetic marker information for the analysis of multiple traits that, according to the authors, "provide fundamental insights into the nature of co-expressed genes." In a similar spirit, Korte et al. (2012) argued that multitrait-marker-enabled regressions can be useful for understanding pleiotropy....

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Do Molecular Markers Inform About Pleiotropy?

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“…This exclusion could lead to reduced accuracy in estimating genetic effects, and spurious genetic associations Table 2 Correlation estimates±standard errors between regression coefficients using data partitioned by age or gender for quantitative traits reflecting health BMI body mass index, WHR waist-to-hip ratio, PP pulse pressure, HDL high-density lipoprotein cholesterol, TG triglyceride, LDL low-density lipoprotein cholesterol, GLU glucose might be identified by disregarding heterogeneity in the analysis. Furthermore, the use of trivariate mixed model treating the three age groups as different traits would help improve statistical power in identifying genetic associations by genetic correlation (Lee et al 2012;Korte et al 2012). Similarly, bivariate mixed model treating males as one trait and females as the other trait can be employed for traits with gender-dependent PVSNP (Lee and Pollak 1997).…”

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Age- and gender-dependent heterogeneous proportion of variation explained by SNPs in quantitative traits reflecting human health

Lee

2015

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Age-related effects are often included as covariates in the analytical model for genome-wide association analysis of quantitative traits reflecting human health. Nevertheless, previous studies have hardly examined the effects of age on the proportion of variation explained by single nucleotide polymorphisms (PVSNP) in these traits. In this study, the PVSNP estimates of body mass index (BMI), waist-to-hip ratio, pulse pressure, high-density lipoprotein cholesterol level, triglyceride level (TG), low-density lipoprotein cholesterol level, and glucose level were obtained from Korean consortium metadata partitioned by gender or by age. Restricted maximum likelihood estimates of the PVSNP were obtained in a mixed model framework. Previous studies using pedigree data suggested possible differential heritability of certain traits with regard to gender, which we observed in our current study (BMI and TG; P<0.05). However, the PVSNP analysis based on age revealed that, with respect to every trait tested, individuals aged 40 to 49 exhibited significantly lower PVSNP estimates than individuals aged 50 to 59 or 60 to 69 (P<0.05). The consistent heterogeneous PVSNP with respect to age may be due to degenerated genetic functions in individuals between the ages of 50 and 69. Our results suggest the genetic mechanism of age-and gender-dependent PVSNP of quantitative traits related to human health should be further examined.

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“…Например, архив изображений рака, связанный с Атласом гено-ма рака [13], включает в себя 31 сайт и более 3 млн изображений, загруженных за первый год работы. Стандартные подходы основаны на статистических моделях, таких как логистическая регрессия и линей-ные смешанные модели [14][15][16], с акцентом на ис-правление потенциальных смещений и смещающих факторов и приведение шкалы данных.…”

Section: большие данные и их анализunclassified

Personalized medicine: genome, e-health and intelligent systems. Part 1. Genomics and monitoring of clinical data

Кобринский¹

2017

Ross. vestn. perinatol. pediatr.

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Виртуальную интеграцию всех медицинских све-дений человека, аккумулируемых в течение жиз-ни в его многочисленных электронных медицинских картах (сумма сведений в которых отвечает общему понятию здоровья -Health Electronic Record, HER), системах мобильного здравоохранения (m-Health), обеспечивающего персональный домашний мони-торинг физиологических показателей (артериальное давление, ЭКГ и т.д.), и в персональных кабинетах («дневники здоровья»), следует рассматривать с пози-ций персоноцентрированной парадигмы электронного здравоохранения (E-Health) [1], т.е. как пациент-фо-кусированное, пациент-активное и пациент-довери-тельное здравоохранение [2]. При этом риск возник-новения ряда хронических заболеваний, определяемый на молекулярно-генетическом уровне, должен затем контролироваться путем регулярного измерения ряда параметров организма портативными устройствами в и вовне медицинских организаций.Персонализированная медицина -платформа, которая использует информацию о генах человека, белках и окружающей среде для предотвращения, своевременной диагностики и лечения заболева-ний. Другими словами, ее именуют медициной 4Р, или P4 predictive, или предсказательной, preventive, или предупредительной, personalize, или персонали-зированной, и participant, или партисипативной. Ос-новные ее принципы более детально можно предста-вить следующим образом [3]:• предиктивная медицина -предсказание особен-ностей состояния здоровья (возможные в будущем заболевания, реагирование на лекарства и факто-ры внешней среды и др.) конкретного человека до появления первых симптомов; • превентивная медицина -проведение профилак-тических мероприятий в отношении возможных,Адрес для корреспонденции: Кобринский Борис Аркадьевич -д.м.н., проф., зав. лабораторией систем поддержки принятия клинических решений Ин-ститута современных информационных технологий в медицине Федераль-ного исследовательского центра «Информатика и управление» РАН 117312 Москва, пр-т 60-летия Октября, д.9Персонализированная медицина: геном, электронное здравоохранение и интеллектуальные системы. Часть 1. Геномика и мониторинг клинических данных Б.А. КобринскийИнститут современных информационных технологий в медицине Федерального исследовательского центра «Инфор-матика и управление» РАН, Москва 16-20. DOI: 10.21508/102716-20. DOI: 10.21508/ -4065-2017 Abstract. The transition to personalized medicine in practical terms should combine the solution of the genomics problems as the basis for possible diseases and phenotypic manifestations that are markers and early signs of emerging pathological changes. Most diseases have their first principles in childhood. Therefore, in all age groups, it is necessary to monitor the minimum deviations and their dynamics, use mobile devices for this purpose and accumulate the received data. Processing big data (Big Data) will provide more informative information. On this basis it will be possible to identify analogs for targeted therapy in similar variants of diseases in large databases of publications on the problem of interest.

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A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Cited by 400 publications

References 40 publications

Do Molecular Markers Inform About Pleiotropy?

Do Molecular Markers Inform About Pleiotropy?

Age- and gender-dependent heterogeneous proportion of variation explained by SNPs in quantitative traits reflecting human health

Personalized medicine: genome, e-health and intelligent systems. Part 1. Genomics and monitoring of clinical data

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