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Zhu Z, Chen X, Zhang S, Yu R, Qi C, Cheng L, Zhang X. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. Hum Genet 2023; 142:1543-1560. [PMID: 37755483 DOI: 10.1007/s00439-023-02602-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
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Denault WRP, Jugessur A. Correction to: Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis. BMC Bioinformatics 2021; 22:276. [PMID: 34039267 PMCID: PMC8157621 DOI: 10.1186/s12859-021-04160-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- William R P Denault
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway. .,Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway. .,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
| | - Astanand Jugessur
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
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Denault WRP, Romanowska J, Helgeland Ø, Jacobsson B, Gjessing HK, Jugessur A. A fast wavelet-based functional association analysis replicates several susceptibility loci for birth weight in a Norwegian population. BMC Genomics 2021; 22:321. [PMID: 33932983 PMCID: PMC8088671 DOI: 10.1186/s12864-021-07582-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 11/28/2022] Open
Abstract
Background Birth weight (BW) is one of the most widely studied anthropometric traits in humans because of its role in various adult-onset diseases. The number of loci associated with BW has increased dramatically since the advent of whole-genome screening approaches such as genome-wide association studies (GWASes) and meta-analyses of GWASes (GWAMAs). To further contribute to elucidating the genetic architecture of BW, we analyzed a genotyped Norwegian dataset with information on child’s BW (N=9,063) using a slightly modified version of a wavelet-based method by Shim and Stephens (2015) called WaveQTL. Results WaveQTL uses wavelet regression for regional testing and offers a more flexible functional modeling framework compared to conventional GWAS methods. To further improve WaveQTL, we added a novel feature termed “zooming strategy” to enhance the detection of associations in typically small regions. The modified WaveQTL replicated five out of the 133 loci previously identified by the largest GWAMA of BW to date by Warrington et al. (2019), even though our sample size was 26 times smaller than that study and 18 times smaller than the second largest GWAMA of BW by Horikoshi et al. (2016). In addition, the modified WaveQTL performed better in regions of high LD between SNPs. Conclusions This study is the first adaptation of the original WaveQTL method to the analysis of genome-wide genotypic data. Our results highlight the utility of the modified WaveQTL as a complementary tool for identifying loci that might escape detection by conventional genome-wide screening methods due to power issues. An attractive application of the modified WaveQTL would be to select traits from various public GWAS repositories to investigate whether they might benefit from a second analysis. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-07582-6).
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Affiliation(s)
- William R P Denault
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway. .,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway. .,Centre for Fertility and Health (CeFH), Norwegian Institute of Public Health, Oslo, Norway.
| | - Julia Romanowska
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Centre for Fertility and Health (CeFH), Norwegian Institute of Public Health, Oslo, Norway
| | - Øyvind Helgeland
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Bo Jacobsson
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Håkon K Gjessing
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Centre for Fertility and Health (CeFH), Norwegian Institute of Public Health, Oslo, Norway
| | - Astanand Jugessur
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Centre for Fertility and Health (CeFH), Norwegian Institute of Public Health, Oslo, Norway
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