Leveraging haplotype information in heritability estimation and polygenic prediction
Abstract Polygenic prediction has yet to make a major clinical breakthrough in precision medicine and psychiatry, where the application of polygenic risk scores is expected to improve clinical decision-making. Most widely used approaches for estimating polygenic risk scores are based on summary stat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55477-3 |
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| _version_ | 1841559228355969024 |
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| author | Jonas Meisner Michael Eriksen Benros Simon Rasmussen |
| author_facet | Jonas Meisner Michael Eriksen Benros Simon Rasmussen |
| author_sort | Jonas Meisner |
| collection | DOAJ |
| description | Abstract Polygenic prediction has yet to make a major clinical breakthrough in precision medicine and psychiatry, where the application of polygenic risk scores is expected to improve clinical decision-making. Most widely used approaches for estimating polygenic risk scores are based on summary statistics from external large-scale genome-wide association studies, which rely on assumptions of matching data distributions. This may hinder the impact of polygenic risk scores in modern diverse populations due to small differences in genetic architectures. Reference-free estimators of polygenic scores are instead based on genomic best linear unbiased predictions and model the population of interest directly. We introduce a framework, named hapla, with a novel algorithm for clustering haplotypes in phased genotype data to estimate heritability and perform reference-free polygenic prediction in complex traits. We utilize inferred haplotype clusters to compute accurate heritability estimates and polygenic scores in a simulation study and the iPSYCH2012 case-cohort for depression disorders and schizophrenia. We demonstrate that our haplotype-based approach robustly outperforms standard genotype-based approaches, which can help pave the way for polygenic risk scores in the future of precision medicine and psychiatry. |
| format | Article |
| id | doaj-art-9754a87eeaad48efb40bba6d6c16d779 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9754a87eeaad48efb40bba6d6c16d7792025-01-05T12:40:26ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-024-55477-3Leveraging haplotype information in heritability estimation and polygenic predictionJonas Meisner0Michael Eriksen Benros1Simon Rasmussen2Copenhagen Research Center for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University HospitalCopenhagen Research Center for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University HospitalNovo Nordisk Foundation Center for Basic Metabolic Research, University of CopenhagenAbstract Polygenic prediction has yet to make a major clinical breakthrough in precision medicine and psychiatry, where the application of polygenic risk scores is expected to improve clinical decision-making. Most widely used approaches for estimating polygenic risk scores are based on summary statistics from external large-scale genome-wide association studies, which rely on assumptions of matching data distributions. This may hinder the impact of polygenic risk scores in modern diverse populations due to small differences in genetic architectures. Reference-free estimators of polygenic scores are instead based on genomic best linear unbiased predictions and model the population of interest directly. We introduce a framework, named hapla, with a novel algorithm for clustering haplotypes in phased genotype data to estimate heritability and perform reference-free polygenic prediction in complex traits. We utilize inferred haplotype clusters to compute accurate heritability estimates and polygenic scores in a simulation study and the iPSYCH2012 case-cohort for depression disorders and schizophrenia. We demonstrate that our haplotype-based approach robustly outperforms standard genotype-based approaches, which can help pave the way for polygenic risk scores in the future of precision medicine and psychiatry.https://doi.org/10.1038/s41467-024-55477-3 |
| spellingShingle | Jonas Meisner Michael Eriksen Benros Simon Rasmussen Leveraging haplotype information in heritability estimation and polygenic prediction Nature Communications |
| title | Leveraging haplotype information in heritability estimation and polygenic prediction |
| title_full | Leveraging haplotype information in heritability estimation and polygenic prediction |
| title_fullStr | Leveraging haplotype information in heritability estimation and polygenic prediction |
| title_full_unstemmed | Leveraging haplotype information in heritability estimation and polygenic prediction |
| title_short | Leveraging haplotype information in heritability estimation and polygenic prediction |
| title_sort | leveraging haplotype information in heritability estimation and polygenic prediction |
| url | https://doi.org/10.1038/s41467-024-55477-3 |
| work_keys_str_mv | AT jonasmeisner leveraginghaplotypeinformationinheritabilityestimationandpolygenicprediction AT michaeleriksenbenros leveraginghaplotypeinformationinheritabilityestimationandpolygenicprediction AT simonrasmussen leveraginghaplotypeinformationinheritabilityestimationandpolygenicprediction |