MCKAT: a multi-dimensional copy number variant kernel association test
Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific dis...
Saved in:
| Published in | BMC bioinformatics Vol. 22; no. 1; pp. 588 - 16 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
11.12.2021
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/s12859-021-04494-w |
Cover
| Abstract | Background
Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods.
Results
We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller
p
-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets.
Conclusion
A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject’s CNVs case by case. |
|---|---|
| AbstractList | Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. Results We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. Conclusion A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject’s CNVs case by case. Abstract Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. Results We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. Conclusion A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject’s CNVs case by case. Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods.BACKGROUNDCopy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods.We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets.RESULTSWe address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets.A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject's CNVs case by case.CONCLUSIONA multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject's CNVs case by case. Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject's CNVs case by case. Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. Results We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p -value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. Conclusion A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject’s CNVs case by case. Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. Results We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. Conclusion A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject's CNVs case by case. Keywords: Copy number variant, Disease-related trait, Association test, Kernel method Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject's CNVs case by case. |
| ArticleNumber | 588 |
| Audience | Academic |
| Author | Khan, Javed Maus Esfahani, Nastaran Catchpoole, Daniel Kennedy, Paul J. |
| Author_xml | – sequence: 1 givenname: Nastaran surname: Maus Esfahani fullname: Maus Esfahani, Nastaran email: nastaran.mausesfahani@student.uts.edu.au organization: Australian Artificial Intelligence Institute, University of Technology Sydney – sequence: 2 givenname: Daniel surname: Catchpoole fullname: Catchpoole, Daniel organization: Australian Artificial Intelligence Institute, University of Technology Sydney, The Tumour Bank, The Children’s Hospital at Westmead – sequence: 3 givenname: Javed surname: Khan fullname: Khan, Javed organization: Center for Cancer Research, National Cancer Institute – sequence: 4 givenname: Paul J. surname: Kennedy fullname: Kennedy, Paul J. organization: Australian Artificial Intelligence Institute, University of Technology Sydney |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34895138$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kl1rFDEUhgep2A_9A17IgDd6MTXJ5NMLYVmsLlYErdfhTCazZp1JtslMP_59s93adkUkkIST533DObyHxZ4P3hbFS4yOMZb8XcJEMlUhgitEqaLV5ZPiAFOBK4IR23t03y8OU1ohhIVE7FmxX1OpGK7lQXHydf5ldva-hHKY-tFVrRusTy546EsT1teln4bGxvICogM_lr9t9LYvIaVgHIwZLEebxufF0w76ZF_cnUfFz5OPZ_PP1em3T4v57LQyTJGxwjW33ABBJm8daozsUKdaQxjmiIGoBeVCSgqNEdBi0cm2VopKrEAYTkV9VCy2vm2AlV5HN0C81gGcvi2EuNQQR2d6qy0VjHUgG0IUbSwDKoyqJaktWEExy14ftl7rqRlsa6wfI_Q7prsv3v3Sy3ChJeccSZoN3twZxHA-5SnowSVj-x68DVPShCOV--GYZ_T1X-gqTDEPeUNhTGoiuXiglpAbcL4L-V-zMdUzrmomMKcqU8f_oPJq7eBMjkjncn1H8HZHkJnRXo1LmFLSix_fd9lXj4dyP40_ickA2QImhpSi7e4RjPQmlnobS51jqW9jqS-zqN6KUob90saH9v-jugHhzuHI |
| Cites_doi | 10.3904/kjim.2015.30.3.294 10.1093/bioinformatics/btw500 10.1016/j.pathol.2016.12.345 10.1111/cas.12173 10.1155/2015/232010 10.1038/ng.3725 10.1016/j.ajhg.2014.06.009 10.1016/j.ajhg.2010.05.002 10.1371/journal.pgen.1005403 10.1016/bs.irn.2015.05.001 10.1038/ng.215 10.1158/2159-8290.CD-13-0639 10.1038/nrg.2017.4 10.1038/nature07953 10.1042/BSR20160570 10.3233/JAD-160469 10.1016/j.pnpbp.2019.02.012 10.1001/jamapsychiatry.2016.1831 10.1098/rspb.2010.1180 10.1186/1471-2105-9-292 10.1007/s00439-016-1749-4 10.1002/gepi.22180 10.1371/journal.pgen.1002334 10.1007/s00787-009-0043-6 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2021 2021. The Author(s). COPYRIGHT 2021 BioMed Central Ltd. 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2021 – notice: 2021. The Author(s). – notice: COPYRIGHT 2021 BioMed Central Ltd. – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1186/s12859-021-04494-w |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni) Medical Database Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 16 |
| ExternalDocumentID | oai_doaj_org_article_e4755fa8b2294be5a47c93823eae7415 PMC8666084 A693571649 34895138 10_1186_s12859_021_04494_w |
| Genre | Journal Article |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO ICD IHR INH INR ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX ALIPV CITATION -A0 3V. ACRMQ ADINQ C24 CGR CUY CVF ECM EIF M0N NPM PMFND 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c592t-136e6ca20cca2f0bc8f0f9dc251605a737467884abc7ad17f8d3994819a7c6473 |
| IEDL.DBID | C6C |
| ISSN | 1471-2105 |
| IngestDate | Wed Aug 27 01:31:29 EDT 2025 Thu Aug 21 18:36:16 EDT 2025 Fri Sep 05 14:01:34 EDT 2025 Fri Jul 25 10:44:29 EDT 2025 Tue Jun 17 21:05:34 EDT 2025 Tue Jun 10 20:39:44 EDT 2025 Fri Jun 27 04:19:52 EDT 2025 Wed Feb 19 02:27:39 EST 2025 Tue Jul 01 03:38:33 EDT 2025 Sat Sep 06 07:27:37 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Copy number variant Disease-related trait Association test Kernel method |
| Language | English |
| License | 2021. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c592t-136e6ca20cca2f0bc8f0f9dc251605a737467884abc7ad17f8d3994819a7c6473 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://doi.org/10.1186/s12859-021-04494-w |
| PMID | 34895138 |
| PQID | 2611232867 |
| PQPubID | 44065 |
| PageCount | 16 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_e4755fa8b2294be5a47c93823eae7415 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8666084 proquest_miscellaneous_2609467616 proquest_journals_2611232867 gale_infotracmisc_A693571649 gale_infotracacademiconefile_A693571649 gale_incontextgauss_ISR_A693571649 pubmed_primary_34895138 crossref_primary_10_1186_s12859_021_04494_w springer_journals_10_1186_s12859_021_04494_w |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-11 |
| PublicationDateYYYYMMDD | 2021-12-11 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-11 day: 11 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2021 |
| Publisher | BioMed Central BioMed Central Ltd BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: BMC |
| References | RB Davies (4494_CR18) 1980; 29 CM Freitag (4494_CR25) 2010; 19 V La Cognata (4494_CR9) 2017; 136 JY Tzeng (4494_CR14) 2015; 11 NB Larson (4494_CR15) 2019; 43 SA McCarroll (4494_CR6) 2008; 40 R Nishimura (4494_CR23) 2013; 104 JA Vorstman (4494_CR4) 2017; 18 SH Yim (4494_CR7) 2015; 30 D El Demellawy (4494_CR21) 2017; 49 X Yingjun (4494_CR3) 2017; 37 S Vicari (4494_CR5) 2019; 92 S Girirajan (4494_CR19) 2011; 7 MC Wu (4494_CR12) 2010; 86 4494_CR22 JT Glessner (4494_CR24) 2009; 459 4494_CR2 D Liu (4494_CR17) 2008; 9 DR Schrider (4494_CR1) 2010; 277 JF Shern (4494_CR20) 2014; 4 S Lee (4494_CR13) 2014; 95 E Rees (4494_CR8) 2016; 73 CR Marshall (4494_CR10) 2017; 49 X Zhan (4494_CR16) 2016; 32 D Cuccaro (4494_CR11) 2017; 55 |
| References_xml | – volume: 30 start-page: 294 issue: 3 year: 2015 ident: 4494_CR7 publication-title: Korean J Intern Med doi: 10.3904/kjim.2015.30.3.294 – volume: 32 start-page: 3603 issue: 23 year: 2016 ident: 4494_CR16 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw500 – volume: 49 start-page: 238 issue: 3 year: 2017 ident: 4494_CR21 publication-title: Pathology doi: 10.1016/j.pathol.2016.12.345 – volume: 104 start-page: 856 issue: 7 year: 2013 ident: 4494_CR23 publication-title: Cancer Sci doi: 10.1111/cas.12173 – ident: 4494_CR22 doi: 10.1155/2015/232010 – volume: 49 start-page: 27 issue: 1 year: 2017 ident: 4494_CR10 publication-title: Nat Genet doi: 10.1038/ng.3725 – volume: 95 start-page: 5 issue: 1 year: 2014 ident: 4494_CR13 publication-title: Am J Hum Genet doi: 10.1016/j.ajhg.2014.06.009 – volume: 86 start-page: 929 issue: 6 year: 2010 ident: 4494_CR12 publication-title: Am J Hum Genet doi: 10.1016/j.ajhg.2010.05.002 – volume: 11 start-page: e1005403 issue: 10 year: 2015 ident: 4494_CR14 publication-title: PLoS Genet doi: 10.1371/journal.pgen.1005403 – ident: 4494_CR2 doi: 10.1016/bs.irn.2015.05.001 – volume: 40 start-page: 1107 issue: 9 year: 2008 ident: 4494_CR6 publication-title: Nat Genet doi: 10.1038/ng.215 – volume: 4 start-page: 216 issue: 2 year: 2014 ident: 4494_CR20 publication-title: Cancer Discov doi: 10.1158/2159-8290.CD-13-0639 – volume: 18 start-page: 362 issue: 6 year: 2017 ident: 4494_CR4 publication-title: Nat Rev Genet doi: 10.1038/nrg.2017.4 – volume: 459 start-page: 569 issue: 7246 year: 2009 ident: 4494_CR24 publication-title: Nature doi: 10.1038/nature07953 – volume: 37 start-page: BSR20160570 issue: 4 year: 2017 ident: 4494_CR3 publication-title: Biosci Rep doi: 10.1042/BSR20160570 – volume: 55 start-page: 37 issue: 1 year: 2017 ident: 4494_CR11 publication-title: J Alzheimers Dis doi: 10.3233/JAD-160469 – volume: 92 start-page: 421 year: 2019 ident: 4494_CR5 publication-title: Prog Neuropsychopharmacol Biol Psychiatry doi: 10.1016/j.pnpbp.2019.02.012 – volume: 73 start-page: 963 issue: 9 year: 2016 ident: 4494_CR8 publication-title: JAMA Psychiatry doi: 10.1001/jamapsychiatry.2016.1831 – volume: 277 start-page: 3213 issue: 1698 year: 2010 ident: 4494_CR1 publication-title: Proc R Soc B Biol Sci doi: 10.1098/rspb.2010.1180 – volume: 29 start-page: 323 issue: 3 year: 1980 ident: 4494_CR18 publication-title: J R Stat Soc Ser C (Appl Stat) – volume: 9 start-page: 292 issue: 1 year: 2008 ident: 4494_CR17 publication-title: BMC Bioinform doi: 10.1186/1471-2105-9-292 – volume: 136 start-page: 13 issue: 1 year: 2017 ident: 4494_CR9 publication-title: Hum Genet doi: 10.1007/s00439-016-1749-4 – volume: 43 start-page: 122 issue: 2 year: 2019 ident: 4494_CR15 publication-title: Genet Epidemiol doi: 10.1002/gepi.22180 – volume: 7 start-page: e1002334 issue: 11 year: 2011 ident: 4494_CR19 publication-title: PLoS Genet doi: 10.1371/journal.pgen.1002334 – volume: 19 start-page: 67 issue: 1 year: 2010 ident: 4494_CR25 publication-title: Eur Child Adolesc Psychiatry doi: 10.1007/s00787-009-0043-6 |
| SSID | ssj0017805 |
| Score | 2.3723357 |
| Snippet | Background
Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders,... Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism,... Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders,... Abstract Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 588 |
| SubjectTerms | Algorithms Analysis Association test Autism Bioinformatics Biologists Biomedical and Life Sciences Care and treatment Chromosomes Computational Biology/Bioinformatics Computer Appl. in Life Sciences Copy number Copy number variant Copy number variations Cytogenetics Datasets Diagnosis Disease Disease-related trait DNA Copy Number Variations Genome Genome-Wide Association Study Genomes Genotype & phenotype Humans Intellectual disabilities Kernel method Kernels Life Sciences Mental disorders Mental retardation Methodology Methodology Article Microarrays Multidimensional methods Phenotype Rhabdomyosarcoma Risk factors Schizophrenia Similarity Statistical analysis |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NaxUxEA9SELyI9XNtlVUEDxq6m82nt2fxUZV60BZ6C9l8qCj7St97lv73zmx2X7sV8eJ1MwvJb2aSGTL5DSEveIqmCobRwESgeATQVtSJhqikE85I1XdvOPwkD475hxNxcqXVF9aEZXrgDNxe5EqI5HTLmOFtFI4rb_DyKrqo8vNyVplqTKaG-wNk6h-fyGi5t6yRp41iOULFueH0fHIM9Wz9f-7JVw6l6wWT125N-8NofofcHqLIcpZnv01uxO4uuZn7Sl7cI_PD_Y-zozelK_t6QRqQwj_Tb5R-cXpR5j4g5S9IlAHZ8kc86-LP0l2qqoQIdHWfHM_fHe0f0KFhAvXCMGwrL6P0jlWgFpaq1utUJRM8xDCQtTjVYG8RrblrvXKhVkkHiE84BAVOeclV84BsdYsuPiJlgI0nuDpyETkPvmrrRouUkqvBwVulCvJqxM-eZl4M2-cTWtqMtgW0bY-2PS_IW4R4I4mc1v0H0LQdNG3_pemCPEcFWWSt6LAs5qtbL5f2_ZfPdiZNIzDzMwV5OQilBajKu-GVAawKia4mkrsTSXArPx0e7cAObr20kG5iCKolrP_ZZhj_xFK1Li7WKAMZs1SylgV5mM1ms-6Ga4hoG10QNTGoCTDTke77t570W0OeWWlekNej6V1O6-_AP_4fwO-QWwxdp2a0rnfJ1upsHZ9AKLZqn_Ze9xvQfS2J priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagCIkL4k2goICQOIDVOPGTC1oqlgIqB2il3izHj4JAybLZpeq_x-Mku6QIrutZ7Xoenm_iyTcIPaPBq8KpEruSOQwpANeMBOy84IYZxUWa3nD4iR8c0w8n7GR44NYNbZXjmZgOatdaeEa-F5E-ZH_JxevFTwxTo-B2dRihcRldIfFnwM_l_N3mFgH4-scXZSTf6wiwtWFoSigoVRSfTZJR4uz_-2T-IzVdbJu8cHeaUtL8Bro-YMl81hv_Jrrkm1voaj9d8vw2mh_uf5wdvcpNnroGsQMi_56EI7ft4jzvp4Hkv2K5HPWbf_fLxv_IzdZgecShqzvoeP72aP8AD2MTsGWqhOHy3HNryiIapwxFbWUognI2IplYuxhRwYQRKamprTCOiCBdRCk0QgMjLKeiuot2mrbx91Hu4vHjDPGUeUqdLWpSSRZCMCSGeS1Ehl6M-tOLnh1Dp6pCct1rW0dt66RtfZahN6DijSQwW6cP2uWpHgJFeyoYC0bWZalo7Zmhwiq4rPTGA_rJ0FMwkAbuigaaY07Nuuv0-y-f9YyrikH9pzL0fBAKbTSVNcO7BnFXQHc1kdydSMbgstPl0Q_0ENyd3rpihp5sluGb0LDW-HYNMrFu5oITnqF7vdts9l1RGXFtJTMkJg41Ucx0pfn2NVF_y1htFpJm6OXoetu_9W_FP_j_Lh6iayUEBSkxIbtoZ7Vc-0cRaq3qxymefgOh_CUx priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELZKERIXxJuUggJC4gChceJXkBBaKlYFtBygK_VmOX4U1Cop-6Dsv2cmj11SyoHreiKtP89k5pMn3xDyjAVfpK7IEpdxl2AKSEpOQ-K8FIabQshmesPksziYso9H_GiL9OOOOgDnl1I7nCc1nZ2--vVj9RYC_k0T8ErszSmqsCXYbJAyVrDk_Aq5CplJIBmbsM2tAur39x_OXPrcIDk1Gv5_v6n_SFUX2ygv3KU2KWp8k9zoast41DrDLbLlq9vkWjttcnWHjCf7n0aHr2MTN12EiUNh_1aUI7b12Spup4PEP4E-A97xiZ9V_jQ2mwOMoS5d3CXT8fvD_YOkG6OQWF5kOGxeeGFNlsJhZSEtrQppKJyFyga4jJE5ThxRipnSSuOoDMpB1cKgVDDSCibze2S7qiv_gMQOXkfOUM-4Z8zZtKS54iEEQyHsSykj8qLHT5-1ahm6YRlK6BZtDWjrBm19HpF3CPHaEpWumx_q2bHuAkd7JjkPRpVZVrDSc8OkLfDy0huP1VBEnuIBadSyqLBZ5tgs53P94esXPRJFzpEPFhF53hmFGo7Kmu7bA9gVyl8NLHcHlhBsdrjc-4HufVUDCcXCVAnY_5P1Mj6JDWyVr5doAzxaSEFFRO63brPed84U1Lm5iogcONQAmOFK9f1bIwWugH2mikXkZe96m7_1b-B3_s_8IbmeYZDQLKF0l2wvZkv_CEqxRfm4ia_f-1EtPA priority: 102 providerName: Scholars Portal |
| Title | MCKAT: a multi-dimensional copy number variant kernel association test |
| URI | https://link.springer.com/article/10.1186/s12859-021-04494-w https://www.ncbi.nlm.nih.gov/pubmed/34895138 https://www.proquest.com/docview/2611232867 https://www.proquest.com/docview/2609467616 https://pubmed.ncbi.nlm.nih.gov/PMC8666084 https://doaj.org/article/e4755fa8b2294be5a47c93823eae7415 |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9NAEF7RVkhcEG8MJTIIiQNYeO19mVsaNZSgVKgPKbfVeh-AQE7VJFT998ysnbQucODiSN6x5J3Z8X6Tmf2GkNcs-Cp3VZG5grsMt4Cs5jRkzkthuKmEjN0bpofi4JRNZnzW0eTgWZjr-XuqxPsFRYa1DAsJcsYqll1skR1OSxETs2K0yRggN__6UMxfn-ttPJGf_8-v8LVt6GaJ5I08adx-xvfI3Q43psPW0PfJLd88ILfbTpKXD8l4Ovo8PPmQmjRWCGYOSftbwo3Uzs8u07bzR_oLQmPQZfrDnzf-Z2qujJMC5lw-Iqfj_ZPRQda1SMgsrwpsJC-8sKbIwRBFyGurQh4qZwG1QJxiZIndRJRiprbSOCqDcoBIGMAAI61gsnxMtpt545-S1MGnxhnqGfeMOZvXtFQ8hGAouHQtZULervWnz1omDB0jCCV0q20N2tZR2_oiIXuo4o0ksljHG2Bc3TmF9kxyHoyqi6JiteeGSVthYtIbj0gnIa_QQBp5KhoshPlqVouF_nR8pIeiKjnGelVC3nRCYQ6msqY7VwCzQmqrnuRuTxIcyfaH1-tAd4680BBgIuhUAub_cjOMT2JxWuPnK5SBGFlIQUVCnrTLZjPvkinAsKVKiOwtqJ5i-iPN92-R5ltBZJkrlpB366V39Vr_Vvyz_xN_Tu4U6CS0yCjdJdvL85V_ATBrWQ_IlpxJuKrxxwHZGQ4nxxP43ds__HI0iL43iH9gwHXK1G8rQyVv |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwEB4tixBcEG8CCwQE4gDW5uHYDhJCZWHV0u0eoCv1ZhzbWRAoKX1Q9U_xG_Hk0ZJFcNtrPIni8Tzt8XwAT2lu08CkETFRYgi6AJIlYU6M5UwlKmW8Qm8YHbP-Cf0wSSY78Ku9C4Nlla1NrAy1KTXuke-7SB-9v2D8zfQHQdQoPF1tITRqsRja9cqlbPPXg3dufZ9F0eH78UGfNKgCRCdphNjrzDKtosD9e5QHmRZ5kKdGO0fvQnvFYwTgEIKqTHNlQp4L45w4dZ5Tcc0oj913L8BFijvjTn_4ZJPghYgP0F7MEWx_HmJ3OIJFEAGlKSWrjvOrMAL-9gR_uMKzZZpnzmorF3h4Da42savfq4XtOuzY4gZcqtEs1zfhcHQw7I1f-cqvqhSJQeCAuumHr8vp2q_RR_yfLj136-l_s7PCfvfVVkB8F_cubsHJuTD0NuwWZWHvgm-cuTMqtDSxlBodZGEskjzPVejMSsa5By9a_slp3Y1DVlmMYLLmtnTclhW35cqDt8jiDSV20q4elLNT2SimtJQnSa5EFkUpzWyiKNcpHo5aZTHa8uAJLpDEXhkFFuOcquV8LgefPsoeS-ME883Ug-cNUV66pdKqudvgZoXttTqUex1Kp8y6O9zKgWyMyVxuRd-Dx5thfBML5ApbLpHG5emMs5B5cKcWm828YypcHB0LD3hHoDqM6Y4UX79UrcaFy24DQT142Yre9rf-zfh7_5_FI7jcH4-O5NHgeHgfrkSoIGFEwnAPdhezpX3gwrxF9rDSLR8-n7cy_wanqmGQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELagCMQF8SyBAgYhcQCrceJXuC0Lq5bSCkEr9WY5fhQESlb7oOq_x-Nkt02BA9d4IsUznsw3mvE3CL1kwVe5qwriCu4IhABScxqI81IYbioh0_SG_QOxc8Q-HvPjC7f4U7f7qiTZ3WkAlqZmsT11oXNxJbbnFHjXCLQX5IxVjJxeRdcYhD4o14rxuo4AjP2rqzJ_fW8QjhJr_5__5gvB6XLj5KXqaQpKk9voVo8m8agz_x10xTd30fVuvuTZPTTZH--NDt9ig1PfIHFA5d_RcGDbTs9wNw8E_4oJc9Qw_uFnjf-JzbnJcESii_voaPLhcLxD-sEJxPKqgPHywgtrijyapwh5bVXIQ-VsxDIxezGyhBkjSjFTW2kclUG5iFNYBAdGWsFk-QBtNG3jHyLs4g_IGeoZ94w5m9e0VDyEYGh09FrKDL1e6U9PO34MnfIKJXSnbR21rZO29WmG3oGK15LAbZ0etLMT3buK9kxyHoyqi6JiteeGSVtBudIbD_gnQy_AQBrYKxpojzkxy_lc7379okeiKjlkgFWGXvVCoY2msqa_bRB3BYRXA8mtgWR0LztcXp0D3bv3XMe0E6CoEnH_z9fL8Ca0rDW-XYJMzJyFFFRkaLM7Nut9l0xFZFuqDMnBgRooZrjSfP-WyL9VzDdzxTL0ZnX0zj_r34p_9H_iz9CNz-8n-tPuwd5jdLMAf6EFoXQLbSxmS_8k4rBF_TS52m8qLynh |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=MCKAT%3A+a+multi-dimensional+copy+number+variant+kernel+association+test&rft.jtitle=BMC+bioinformatics&rft.au=Maus+Esfahani%2C+Nastaran&rft.au=Catchpoole%2C+Daniel&rft.au=Khan%2C+Javed&rft.au=Kennedy%2C+Paul+J.&rft.date=2021-12-11&rft.pub=BioMed+Central&rft.eissn=1471-2105&rft.volume=22&rft.issue=1&rft_id=info:doi/10.1186%2Fs12859-021-04494-w&rft.externalDocID=10_1186_s12859_021_04494_w |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |