Mapping quantitative trait loci underlying body weight changes that act at different times during high-fat diet challenge in collaborative cross mice

Abstract

Over one billion people worldwide suffer from obesity, and the number is continually rising. This epidemic is partly caused by the modern lifestyle. Animal models, especially mouse models, are crucial to identifying the genetic components of complex disorders and exploring the potential applications of these genetic findings. The body weight of the animals used in research is often measured regularly to monitor their health. Only endpoint measurements, such as ultimate body weight, are frequently examined in quantitative trait locus (QTL) studies; time series data, including weekly or biweekly body weight, are usually disregarded. QTL mapping using biweekly body weight measurements may be particularly intriguing in examining body weight gain in obesity research and identifying more genes associated with obesity and related metabolic disorders. This study is focused on identifying quantitative trait loci (QTLs) underlying body weight changes by analyzing biweekly weight measurements in collaborative cross (CC) mice maintained on a high-fat diet for 12 weeks. QTL analysis, utilizing 525 mice from 55 CC lines (308 males and 217 females), revealed genome-wide significant QTLs on different chromosomes for body weight changes over 12 weeks. This study unveiled 62 body weight QTLs, among which 28 novel QTLs associated with defined traits were observed and found not reported previously. In addition, 34 more QTLs were fine-mapped, as the genomic interval positions of these had been previously identified. These findings highlight genomic regions that influence body weight in CC mice, underscoring the value of time series data in identifying novel genetic factors.

Keywords

candidate genes / collaborative cross mice / high-fat diet / obesity / QTL mapping

References

[1] Schwartz MW, Seeley RJ, Zeltser LM, et al. Obesity pathogenesis: an endocrine society scientific statement. Endocr Rev. 2017; 38(4): 267-296.

[2] Samblas M, Milagro FI, Martínez A. DNA methylation markers in obesity, metabolic syndrome, and weight loss. Epigenetics. 2019; 14(5): 421-444.

[3] Yehia R, Lone IM, Yehia I, Iraqi FA. Studying the pharmagenomic effect of Portulaca oleracea extract on anti-diabetic therapy using the collaborative cross mice. Phytomed Plus. 2023; 3(1):100394.

[4] Ghnaim A, Lone IM, Nun NB, Iraqi FA. Unraveling the host genetic background effect on internal organ weight influenced by obesity and diabetes using collaborative cross mice. Int J Mol Sci. 2023; 24(9): 1-18.

[5] Lone IM, Midlej K, Nun NB, Iraqi FA. Intestinal cancer development in response to oral infection with high-fat diet-induced type 2 diabetes (T2D) in collaborative cross mice under different host genetic background effects. Mamm Genome. 2023; 34(1): 56-75.

[6] Abu-Toamih-Atamni HJ, Lone IM, Binenbaum I, et al. Mapping novel QTL and fine mapping of previously identified QTL associated with glucose tolerance using the collaborative cross mice. Mamm Genome. 2024; 2023(35): 31-55.

[7] Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015; 518(7538): 197-206.

[8] Novelli G, Cassadonte C, Sbraccia P, Biancolella M. Genetics: a starting point for the prevention and the treatment of obesity. Nutrients. 2023; 15(12): 1-16.

[9] Cuciureanu M, Caratașu C-C, Gabrielian L, et al. 360-degree perspectives on obesity. Medicina (Kaunas). 2023; 59(6): 1-51.

[10] Lone IM, Iraqi FA. Genetics of murine type 2 diabetes and comorbidities. Mamm Genome. 2022; 33(3): 421-436.

[11] Yosief RHS, Lone IM, Nachshon A. Identifying genetic susceptibility to Aspergillus fumigatus infection using collaborative cross mice and RNA-seq approach. Animal Model Exp Med. 2024; 7(1): 36-47.

[12] Binenbaum I, Atamni HA-T, Fotakis G, et al. Container-aided integrative QTL and RNA-seq analysis of collaborative cross mice supports distinct sex-oriented molecular modes of response in obesity. BMC Genomics. 2020; 21(1):761.

[13] Lone IM, Nun NB, Ghnaim A, Schaefer AS, Houri-Haddad Y, Iraqi FA. High-fat diet and oral infection induced type 2 diabetes and obesity development under different genetic backgrounds. Animal Model Exp Med. 2023; 6(2): 131-145.

[14] Lone IM, Zohud O, Midlej K, Brenner C. System genetic analysis of intestinal cancer and periodontitis development as influenced by aging and diabesity using collaborative cross mice. Animal Model Exp Med. 2025; 8: 758-770.

[15] Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ, Mouse Genome Informatics Group. Mouse genome informatics: an integrated knowledgebase system for the laboratory mouse. Genetics. 2024; 227(1): 1-31.

[16] Delpero M, Korkuć P, Arends D, Brockmann GA, Hesse D. Identification of additional body weight QTLs in the Berlin Fat Mouse BFMI861 lines using time series data. Sci Rep. 2024; 14:6159. https://doi.org/10.1038/s41598-024-56097-z

[17] Krupke DM, Begley DA, Sundberg JP, Richardson JE, Neuhauser SB, Bult CJ. The mouse tumor biology database: a comprehensive resource for mouse models of human cancer. Cancer Res. 2017; 77(21): e67-e70.

[18] Koutsandreas T, Binenbaum I, Pilalis E, Valavanis I, Papadodima O, Chatziioannou A. Analyzing and visualizing genomic complexity for the derivation of the emergent molecular networks. Int J Monit Surveill Technol Res. 2016; 4(2): 30-49.

[19] Wang M, Li B, Qin F, Ye J, Jin L. Obesity induced Ext1 reduction mediates the occurrence of NAFLD. Biochem Biophys Res Commun. 2022; 589: 123-130.

[20] Zhu Y, Li N, Huang M, et al. Adipose tissue hyaluronan production improves systemic glucose homeostasis and primes adipocytes for CL 316,243-stimulated lipolysis. Nat Commun. 2021; 12(1):4829.

[21] Hanada R, Teranishi H, Pearson JT, et al. Neuromedin U has a novel anorexigenic effect independent of the leptin signaling pathway. Nat Med. 2004; 10(10): 1067-1073.

[22] Hainerová I, Torekov SS, Ek J, et al. Association between neuromedin U gene variants and overweight and obesity. J Clin Endocrinol Metab. 2006; 91(12): 5057-5063.

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2026 The Author(s). Animal Models and Experimental Medicine published by John Wiley & Sons Australia, Ltd on behalf of The Chinese Association for Laboratory Animal Sciences.