Fast and flexible linear mixed models for genome-wide genetics.
Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced po...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-02-01
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| Series: | PLoS Genetics |
| Online Access: | http://europepmc.org/articles/PMC6383949?pdf=render |
| _version_ | 1818109281546797056 |
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| author | Daniel E Runcie Lorin Crawford |
| author_facet | Daniel E Runcie Lorin Crawford |
| author_sort | Daniel E Runcie |
| collection | DOAJ |
| description | Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries. |
| first_indexed | 2024-12-11T02:28:46Z |
| format | Article |
| id | doaj.art-04ad80ac2ada459d8876168ee1db880d |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-12-11T02:28:46Z |
| publishDate | 2019-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-04ad80ac2ada459d8876168ee1db880d2022-12-22T01:23:52ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042019-02-01152e100797810.1371/journal.pgen.1007978Fast and flexible linear mixed models for genome-wide genetics.Daniel E RuncieLorin CrawfordLinear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.http://europepmc.org/articles/PMC6383949?pdf=render |
| spellingShingle | Daniel E Runcie Lorin Crawford Fast and flexible linear mixed models for genome-wide genetics. PLoS Genetics |
| title | Fast and flexible linear mixed models for genome-wide genetics. |
| title_full | Fast and flexible linear mixed models for genome-wide genetics. |
| title_fullStr | Fast and flexible linear mixed models for genome-wide genetics. |
| title_full_unstemmed | Fast and flexible linear mixed models for genome-wide genetics. |
| title_short | Fast and flexible linear mixed models for genome-wide genetics. |
| title_sort | fast and flexible linear mixed models for genome wide genetics |
| url | http://europepmc.org/articles/PMC6383949?pdf=render |
| work_keys_str_mv | AT danieleruncie fastandflexiblelinearmixedmodelsforgenomewidegenetics AT lorincrawford fastandflexiblelinearmixedmodelsforgenomewidegenetics |