Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model

Corbeil, R. R.; Searle, S. R.

doi:10.2307/1267913

“…; s 2 K and s 2 e are estimated by restricted maximum likelihood (REML). 25,26 Given that the variance parameters have been estimated, the BLUP of allelic effects for SNPs in class k is: …”

Section: Lmm and Blup For Complex Traitsmentioning

confidence: 99%

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Coram

¹

,

Fang

²

,

Candille

³

et al. 2017

The American Journal of Human Genetics

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An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.

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“…25,26 Given that the variance parameters have been estimated, the BLUP of allelic effects for SNPs in class k is: …”

Section: Lmm and Blup For Complex Traitsmentioning

confidence: 99%

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Coram¹,

Fang²,

Candille³

et al. 2017

The American Journal of Human Genetics

View full text Add to dashboard Cite

An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.

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“…is trivial, whereas it is not for unbalanced data; indeed in the then necessarily iterative procedures that are used it can be an advantage of REML estimators that the matrices to be inverted have order k less than those for ML estimators, tmi and tmi + k respectively (see Hemmerle and Hartley [1973] and Corbeil and Searle [1974]). …”

Section: The Modelsmentioning

confidence: 99%

“…(10)] is in some sense a marginal case insofar as the relative efficiency of ML or REML estimators is concerned, this model was selected for a numerical study with unbalanced data of either 0 or 1 observation per cell. There is, for such data, no closed form solution to equations (2) -(4) and (5), (6) and so the study is based on iterative solutions using, respectively, procedures of Hemmerle and Hartley [1973] and Corbeil and Searle [1974).…”

Section: Numerical Studies On Unbalanced Datamentioning

confidence: 99%

A Comparison of Variance Component Estimators

Corbeil¹,

Searle²

1976

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Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model

Cited by 380 publications

References 8 publications

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations

A Comparison of Variance Component Estimators

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