Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases
Abstract Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and sing...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53461-x |
| _version_ | 1797275147317542912 |
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| author | Xiao Yuan Jieqiong Su Jing Wang Bing Dai Yanfang Sun Keke Zhang Yinghua Li Jun Chuan Chunyan Tang Yan Yu Qiang Gong |
| author_facet | Xiao Yuan Jieqiong Su Jing Wang Bing Dai Yanfang Sun Keke Zhang Yinghua Li Jun Chuan Chunyan Tang Yan Yu Qiang Gong |
| author_sort | Xiao Yuan |
| collection | DOAJ |
| description | Abstract Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3–91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools. |
| first_indexed | 2024-03-07T15:09:08Z |
| format | Article |
| id | doaj.art-7264bbdd16874e4098f50930617f1676 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:09:08Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7264bbdd16874e4098f50930617f16762024-03-05T18:44:29ZengNature PortfolioScientific Reports2045-23222024-02-0114111010.1038/s41598-024-53461-xRefined preferences of prioritizers improve intelligent diagnosis for Mendelian diseasesXiao Yuan0Jieqiong Su1Jing Wang2Bing Dai3Yanfang Sun4Keke Zhang5Yinghua Li6Jun Chuan7Chunyan Tang8Yan Yu9Qiang Gong10Changsha Kingmed Center for Clinical LaboratoryChangsha Kingmed Center for Clinical LaboratoryChangsha Kingmed Center for Clinical LaboratoryChangsha Kingmed Center for Clinical LaboratoryChangsha Kingmed Center for Clinical LaboratoryChangsha Kingmed Center for Clinical LaboratoryGuangzhou Kingmed Center for Clinical LaboratoryGenetalks Biotech. Co., Ltd.Changsha Kingmed Center for Clinical LaboratoryChangsha Kingmed Center for Clinical LaboratoryChangsha Kingmed Center for Clinical LaboratoryAbstract Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3–91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.https://doi.org/10.1038/s41598-024-53461-x |
| spellingShingle | Xiao Yuan Jieqiong Su Jing Wang Bing Dai Yanfang Sun Keke Zhang Yinghua Li Jun Chuan Chunyan Tang Yan Yu Qiang Gong Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases Scientific Reports |
| title | Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases |
| title_full | Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases |
| title_fullStr | Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases |
| title_full_unstemmed | Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases |
| title_short | Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases |
| title_sort | refined preferences of prioritizers improve intelligent diagnosis for mendelian diseases |
| url | https://doi.org/10.1038/s41598-024-53461-x |
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