Genomic heritability: what is it?
Whole-genome regression methods are being increasingly used for the analysis and prediction of complex traits and diseases. In human genetics, these methods are commonly used for inferences about genetic parameters, such as the amount of genetic variance among individuals or the proportion of phenot...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-05-01
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| Series: | PLoS Genetics |
| Online Access: | http://europepmc.org/articles/PMC4420472?pdf=render |
| _version_ | 1828878304201932800 |
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| author | Gustavo de Los Campos Daniel Sorensen Daniel Gianola |
| author_facet | Gustavo de Los Campos Daniel Sorensen Daniel Gianola |
| author_sort | Gustavo de Los Campos |
| collection | DOAJ |
| description | Whole-genome regression methods are being increasingly used for the analysis and prediction of complex traits and diseases. In human genetics, these methods are commonly used for inferences about genetic parameters, such as the amount of genetic variance among individuals or the proportion of phenotypic variance that can be explained by regression on molecular markers. This is so even though some of the assumptions commonly adopted for data analysis are at odds with important quantitative genetic concepts. In this article we develop theory that leads to a precise definition of parameters arising in high dimensional genomic regressions; we focus on the so-called genomic heritability: the proportion of variance of a trait that can be explained (in the population) by a linear regression on a set of markers. We propose a definition of this parameter that is framed within the classical quantitative genetics theory and show that the genomic heritability and the trait heritability parameters are equal only when all causal variants are typed. Further, we discuss how the genomic variance and genomic heritability, defined as quantitative genetic parameters, relate to parameters of statistical models commonly used for inferences, and indicate potential inferential problems that are assessed further using simulations. When a large proportion of the markers used in the analysis are in LE with QTL the likelihood function can be misspecified. This can induce a sizable finite-sample bias and, possibly, lack of consistency of likelihood (or Bayesian) estimates. This situation can be encountered if the individuals in the sample are distantly related and linkage disequilibrium spans over short regions. This bias does not negate the use of whole-genome regression models as predictive machines; however, our results indicate that caution is needed when using marker-based regressions for inferences about population parameters such as the genomic heritability. |
| first_indexed | 2024-12-13T09:02:55Z |
| format | Article |
| id | doaj.art-3dd3586e135048bf8c0822224a199ee1 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-12-13T09:02:55Z |
| publishDate | 2015-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-3dd3586e135048bf8c0822224a199ee12022-12-21T23:53:07ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042015-05-01115e100504810.1371/journal.pgen.1005048Genomic heritability: what is it?Gustavo de Los CamposDaniel SorensenDaniel GianolaWhole-genome regression methods are being increasingly used for the analysis and prediction of complex traits and diseases. In human genetics, these methods are commonly used for inferences about genetic parameters, such as the amount of genetic variance among individuals or the proportion of phenotypic variance that can be explained by regression on molecular markers. This is so even though some of the assumptions commonly adopted for data analysis are at odds with important quantitative genetic concepts. In this article we develop theory that leads to a precise definition of parameters arising in high dimensional genomic regressions; we focus on the so-called genomic heritability: the proportion of variance of a trait that can be explained (in the population) by a linear regression on a set of markers. We propose a definition of this parameter that is framed within the classical quantitative genetics theory and show that the genomic heritability and the trait heritability parameters are equal only when all causal variants are typed. Further, we discuss how the genomic variance and genomic heritability, defined as quantitative genetic parameters, relate to parameters of statistical models commonly used for inferences, and indicate potential inferential problems that are assessed further using simulations. When a large proportion of the markers used in the analysis are in LE with QTL the likelihood function can be misspecified. This can induce a sizable finite-sample bias and, possibly, lack of consistency of likelihood (or Bayesian) estimates. This situation can be encountered if the individuals in the sample are distantly related and linkage disequilibrium spans over short regions. This bias does not negate the use of whole-genome regression models as predictive machines; however, our results indicate that caution is needed when using marker-based regressions for inferences about population parameters such as the genomic heritability.http://europepmc.org/articles/PMC4420472?pdf=render |
| spellingShingle | Gustavo de Los Campos Daniel Sorensen Daniel Gianola Genomic heritability: what is it? PLoS Genetics |
| title | Genomic heritability: what is it? |
| title_full | Genomic heritability: what is it? |
| title_fullStr | Genomic heritability: what is it? |
| title_full_unstemmed | Genomic heritability: what is it? |
| title_short | Genomic heritability: what is it? |
| title_sort | genomic heritability what is it |
| url | http://europepmc.org/articles/PMC4420472?pdf=render |
| work_keys_str_mv | AT gustavodeloscampos genomicheritabilitywhatisit AT danielsorensen genomicheritabilitywhatisit AT danielgianola genomicheritabilitywhatisit |