Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission
Abstract Background The spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three u...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | deu |
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BMC
2022-09-01
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| Series: | Genetics Selection Evolution |
| Online Access: | https://doi.org/10.1186/s12711-022-00747-1 |
| _version_ | 1828418420032405504 |
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| author | Christopher Pooley Glenn Marion Stephen Bishop Andrea Doeschl-Wilson |
| author_facet | Christopher Pooley Glenn Marion Stephen Bishop Andrea Doeschl-Wilson |
| author_sort | Christopher Pooley |
| collection | DOAJ |
| description | Abstract Background The spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important ‘disease resistance single nucleotide polymorphisms (SNPs)’, but how these affect the underlying traits is an unresolved question. Recent advances in computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known about how to effectively design disease transmission experiments or field studies to maximise the precision with which these effects can be estimated. Results In this paper, we develop and validate analytical expressions for the precision of the estimates of SNP effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact groups. Maximising these expressions leads to three distinct ‘experimental’ designs, each specifying a different set of ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design termed “pure”, and (c) a multi-group design termed “mixed”, where ‘pure’ and ‘mixed’ refer to groupings that consist of individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections. The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects, such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental setup is implemented in an online software tool SIRE-PC. Conclusions Methodology was developed to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability. Designs that maximize the precision of estimates were derived. |
| first_indexed | 2024-12-10T14:37:26Z |
| format | Article |
| id | doaj.art-e6bd27a9dd494e5fa1dadc6f5926a22d |
| institution | Directory Open Access Journal |
| issn | 1297-9686 |
| language | deu |
| last_indexed | 2024-12-10T14:37:26Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj.art-e6bd27a9dd494e5fa1dadc6f5926a22d2022-12-22T01:44:48ZdeuBMCGenetics Selection Evolution1297-96862022-09-0154112210.1186/s12711-022-00747-1Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmissionChristopher Pooley0Glenn Marion1Stephen BishopAndrea Doeschl-Wilson2Biomathematics and Statistics ScotlandBiomathematics and Statistics ScotlandThe Roslin Institute, University of EdinburghAbstract Background The spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important ‘disease resistance single nucleotide polymorphisms (SNPs)’, but how these affect the underlying traits is an unresolved question. Recent advances in computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known about how to effectively design disease transmission experiments or field studies to maximise the precision with which these effects can be estimated. Results In this paper, we develop and validate analytical expressions for the precision of the estimates of SNP effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact groups. Maximising these expressions leads to three distinct ‘experimental’ designs, each specifying a different set of ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design termed “pure”, and (c) a multi-group design termed “mixed”, where ‘pure’ and ‘mixed’ refer to groupings that consist of individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections. The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects, such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental setup is implemented in an online software tool SIRE-PC. Conclusions Methodology was developed to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability. Designs that maximize the precision of estimates were derived.https://doi.org/10.1186/s12711-022-00747-1 |
| spellingShingle | Christopher Pooley Glenn Marion Stephen Bishop Andrea Doeschl-Wilson Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission Genetics Selection Evolution |
| title | Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission |
| title_full | Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission |
| title_fullStr | Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission |
| title_full_unstemmed | Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission |
| title_short | Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission |
| title_sort | optimal experimental designs for estimating genetic and non genetic effects underlying infectious disease transmission |
| url | https://doi.org/10.1186/s12711-022-00747-1 |
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