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Journal of Peking University (Health Sciences) ›› 2025, Vol. 57 ›› Issue (3): 448-455. doi: 10.19723/j.issn.1671-167X.2025.03.007

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Identifying genetic etiology of ischemic stroke based on pleiotropy of obesity related genes: A sibling study

Kun WANG1, Huairong WANG1, Huan YU1, Ruotong YANG1, Liuyan ZHENG1, Jingxian WU1, Xueying QIN1,2, Tao WU1,2, Dafang CHEN1,2, Yiqun WU1,2,*(), Yonghua HU1,2   

  1. 1. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
    2. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
  • Received:2024-11-15 Online:2025-06-18 Published:2025-06-13
  • Contact: Yiqun WU
  • Supported by:
    the National Natural Science Foundation of China(81703291)

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Abstract:

Objective: To identify genetic etiology of ischemic stroke (IS) based on pleiotropy of obesity related genes. Methods: A discordant sib-pair study was designed based on the Fangshan family cohort in Beijing. Body mass index (BMI) polygenic risk score (PRS) was first constructed under different P values. Using the polygenic transmission disequilibrium test (pTDT), we then compared the actual BMI genetic risk of siblings with IS to their expected risk, to analyze whether higher BMI was over-transmitted to siblings with IS. The single nucleotide polymorphism (SNP) that comprised the PRS over-transmitted with IS and that corresponded to the highest heritability of IS were identified as a pleiotropy SNPs set between BMI and IS. This set was then utilized as a candidate set to identify and verify risk SNPs asso-ciated IS by transmission disequilibrium test. Finally, we identified independent genomic risk loci and mapped to genes, we then explored the biological function of the identified risk loci and genes by functional annotation and pathway enrichment. Results: A total of 541 participants were enrolled, with an average age of (58.4±8.1) years, including 326 discordant sib pairs of ischemic stroke. Compared with non-IS participants, IS participants with males, education level below junior high school, hypertension and hyperlipidemia accounted for a higher proportion (P < 0.05). For all the BMI PRS, we found that the actual genetic risk of BMI in siblings with IS was higher than their expectation, suggesting that genetic risk associated with high BMI was over-transmitted with IS. Compared with other SNP sets, the set (P < 5×10-4) corresponded to the best analytical statistics of pTDT and the highest heritability of IS and was identified as the pleiotropy SNP set between BMI and IS. Within this set, there were 45 SNPs having linkage and association with IS, which were located in 43 independent genomic risk loci and mapped to 40 genes. These genes were significantly enriched in the lipid metabolism pathway. The rs2232852 corrected by multiple tests was mapped to CYB5R1 and ADIPOR1, which were related to lipid metabolism and the ferroptosis pathway. Conclusion: Pleiotropy between BMI-related genes and IS was observed. Forty-five SNPs were found with linkage and association with IS in the pleiotropy gene set and mapped to 40 genes, which were functionally enriched in lipid metabolic pathways. The rs2232852 corrected by multiple tests during association analysis validation was mapped to CYB5R1 and ADIPOR1, which were related to lipid metabolism and the ferroptosis pathway, suggesting that lipid metabolism and ferroptosis played an important role in the development of IS.

Key words: Genetic association studies, Siblings, Ischemic stroke, Body mass index, Pleiotropy

CLC Number: 

  • R181.33

Table 1

Basic characteristics of participants"

Items Total (n=541) Participants with IS (n=276) Participants without IS (n=265) P value
Age/years, ${\bar x}$±s 58.4±8.1 60.1±7.4 56.6±8.5 < 0.001
Male, n (%) 310 (57.3) 171 (62.0) 139 (52.5) 0.032
Married, n (%) 468 (86.5) 236 (85.5) 232 (87.5) 0.872
Junior high school education or above, n (%) 290 (53.6) 136 (49.3) 154 (58.1) 0.048
Smoker, n (%) 287 (53.1) 147 (53.3) 140 (52.8) 0.957
Drinker, n (%) 243 (44.9) 131 (47.5) 112 (42.3) 0.227
Adequate exercise, n (%) 159 (29.4) 86 (31.2) 73 (27.5) 0.193
BMI/(kg/m2), ${\bar x}$±s 26.2±3.4 26.2±3.3 26.3±3.6 0.637
Diabetes, n (%) 172 (31.8) 94 (34.1) 78 (29.4) 0.288
Hypertension, n (%) 276 (51.0) 158 (57.2) 118 (44.5) 0.004
Hyperlipidemia, n (%) 179 (33.1) 114 (41.3) 65 (24.5) < 0.001
BMI-PRS, ${\bar x}$±s 0.040±0.162 0.047±0.157 0.032±0.166 0.284

Table 2

Association of BMI polygenic risk with IS"

P SNPn DEV (mean) PpTDT hSNPn2 R2
5×10-7 399 0.081 0.126 1×10-6 6.99×10-4
5×10-6 644 0.088 0.095 1×10-6 6.91×10-4
5×10-5 1 244 0.052 0.242 1×10-6 2.06×10-4
5×10-4 3 031 0.080 0.072 2×10-6 1.20×10-3
5×10-3 9 172 0.045 0.314 2×10-6 3.89×10-4

Table 3

SNP that are linked and associated with IS corresponded to genomic risk loci and mapped gene"

SNP Cytogenetic band Genomic risk loci (chromosome: start position-end position) Gene
rs118074101 1p13.3 1:110750031-110750031 KCNC4, SLC6A7
rs12093350 1q22 1: 155468732-155793969 ASH1L, MSTO1, YY1AP1, DAP3, GON4L
rs2232852 1q32.1 1:202924936-202938778 ADIPOR1, CYB5R1
rs34378983 1q32.3 1:214331416-214404556
rs75747776 2p24.1 2:20409638-20502198 SDC1, PUM2
rs12612799 2p16.1 2:56316415-56385145
rs147755783 2q24.1 2:158031410-158031410
rs1460669 2q24.3 2:164613829-164653060
rs141123347 2q24.3 2:169174780-169174780
rs17053757 3p21.1 3:54169266-54171774 CACNA2D3
rs10025110 4p12 4:44901237-45014151
rs59257388 4q21.22 4:83203517-83270007 HNRNPD
rs3811801 4q23 4:100244319-100336102 ADH1B, ADH1C, ADH7
rs76339045 4q32.3 4:167710607-167710607 SPOCK3
rs13177532 5p13.2 5:36728340-36783545
rs12656015 5q11.2 5:53089622-53160978
rs7723244 5q22.1 5:111261184-111443109 NREP
rs2043478 5q31.2 5:136498493-136498493 SPOCK1
rs181895 5q31.3 5:141769375-141822539
rs2062536 5q34 5:165795362-165795362
rs368399960 6p21.32 6:32300809-32331002 C6orf10
rs13202872 6p12.3 6:51189632-51264082
rs6947395 7q11.22 7:69403462-69406661 AUTS2
rs10268638 7q33 7:137100832-137129484 DKGI
rs6999964 8q24.22 8:132862920-132862920
rs10961656 9p22.3 9:14639666-14694602 ZDHHC21
rs12343952 9q33.1 9:122445599-122497319
rs74829026 10p15.3 10:2298746-2298746
rs117023276 10p12.31 10:21246955-21291331 NEBL
rs138468034 10q22.2 10:77321693-77366416 C10orf11
rs664706 10q23.31 10:89773567-89777068
rs192881652 11p15.1 11:18034532-18034532 TPH1, SERGEF
rs308754 11q14.2 11:88161676-88172516
rs1820460 12q23.3 12:107704705-107704705 BTBD11
rs4477562
rs2785821		 13q14.3 13:54091759-54272104
rs11631335 15q15.1 15:40395604-40421006 BMF
rs9646281 16q12.1 16:52264631-52264631
rs75659809 16q23.1 16:77018698-77018698
rs11877418 18q11.2 18:20748733-20787099 CABLES1, TMEM241
rs77247480
rs56080693		 18q12.1 18:27163063-27426754
rs148990504 18q21.31 18:54255607-54255607 TXNL1
rs769449 19q13.32 19:45410002-45428234 TOMM40, APOE, APOC1
rs117988645 22q11.21 22:20093967-20174270 DGCR8, TRMT2A, RANBP1, ZDHHC8, AC006547.14

Table 4

SNP verified by association analysis"

SNP Effect allele MAF OR(95%CI) P
rs2232852 C 0.387 1.53 (1.21-1.94) 4.582×10-5
rs1460669 T 0.415 1.37 (1.08-1.73) 0.010
rs12343952 T 0.247 1.42 (1.07-1.90) 0.017
rs75659809 T 0.009 0.25 (0.07-0.85) 0.026
rs141123347 G 0.013 0.32 (0.12-0.88) 0.027
rs11631335 G 0.236 0.77 (0.60-1.00) 0.049
1
Global, regional, and national burden of stroke and its risk factors, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet Neurol, 2021, 20(10): 795-820.
2
Campbell BCV , Khatri P . Stroke[J]. Lancet, 2020, 396 (10244): 129- 142.

doi: 10.1016/S0140-6736(20)31179-X
3
Black M , Wang W , Wang W . Ischemic stroke: From next generation sequencing and GWAS to community genomics?[J]. OMICS, 2015, 19 (8): 451- 460.

doi: 10.1089/omi.2015.0083
4
Dichgans M . Genetics of ischaemic stroke[J]. Lancet Neurol, 2007, 6 (2): 149- 161.

doi: 10.1016/S1474-4422(07)70028-5
5
Hu Y , Haessler JW , Manansala R , et al. Whole-genome sequencing association analyses of stroke and its subtypes in ancestrally diverse populations from trans-omics for precision medicine project[J]. Stroke, 2022, 53 (3): 875- 885.

doi: 10.1161/STROKEAHA.120.031792
6
Mishra A , Malik R , Hachiya T , et al. Stroke genetics informs drug discovery and risk prediction across ancestries[J]. Nature, 2022, 611 (7934): 115- 123.

doi: 10.1038/s41586-022-05165-3
7
Malik R , Chauhan G , Traylor M , et al. Multiancestry genome-wide association study of 520, 000 subjects identifies 32 loci associated with stroke and stroke subtypes[J]. Nat Genet, 2018, 50 (4): 524- 537.

doi: 10.1038/s41588-018-0058-3
8
Matarin M , Brown WM , Dena H , et al. Candidate gene polymorphisms for ischemic stroke[J]. Stroke, 2009, 40 (11): 3436- 3442.

doi: 10.1161/STROKEAHA.109.558015
9
Dichgans M , Markus HS . Genetic association studies in stroke: Methodological issues and proposed standard criteria[J]. Stroke, 2005, 36 (9): 2027- 2031.

doi: 10.1161/01.STR.0000177498.21594.9e
10
Holdt LM , Teupser D . Recent studies of the human chromosome 9p21 locus, which is associated with atherosclerosis in human populations[J]. Arterioscler Thromb Vasc Biol, 2012, 32 (2): 196- 206.

doi: 10.1161/ATVBAHA.111.232678
11
Stearns FW . One hundred years of pleiotropy: A retrospective[J]. Genetics, 2010, 186 (3): 767- 773.

doi: 10.1534/genetics.110.122549
12
Romero C , Werme J , Jansen PR , et al. Exploring the genetic overlap between twelve psychiatric disorders[J]. Nat Genet, 2022, 54 (12): 1795- 1802.

doi: 10.1038/s41588-022-01245-2
13
Lu H , Qiao J , Shao Z , et al. A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics[J]. BMC Med, 2021, 19 (1): 314.

doi: 10.1186/s12916-021-02186-z
14
Gong W , Guo P , Li Y , et al. Role of the gut-brain axis in the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders: A genome-wide pleiotropic analysis[J]. JAMA Psychiatry, 2023, 80 (4): 360- 370.

doi: 10.1001/jamapsychiatry.2022.4974
15
Wang J , Luo GY , Tian T , et al. Shared genetic basis and causality between schizophrenia and inflammatory bowel disease: Evidence from a comprehensive genetic analysis[J]. Psychol Med, 2024, 54 (10): 2658- 2668.

doi: 10.1017/S0033291724000771
16
Price AL , Patterson NJ , Plenge RM , et al. Principal components analysis corrects for stratification in genome-wide association studies[J]. Nat Genet, 2006, 38 (8): 904- 909.

doi: 10.1038/ng1847
17
Ott J , Kamatani Y , Lathrop M . Family-based designs for genome-wide association studies[J]. Nat Rev Genet, 2011, 12 (7): 465- 474.

doi: 10.1038/nrg2989
18
Weiner DJ , Wigdor EM , Ripke S , et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders[J]. Nat Genet, 2017, 49 (7): 978- 985.

doi: 10.1038/ng.3863
19
Laird NM , Blacker D , Wilcox M . The sib transmission/disequilibrium test is a Mantel-Haenszel test[J]. Am J Hum Genet, 1998, 63 (6): 1915- 1916.
20
Meschia JF , Lojacono MA , Miller MJ , et al. Reliability of the questionnaire for verifying stroke-free status[J]. Cerebrovasc Dis, 2004, 17 (2/3): 218- 223.
21
O'Connell J , Gurdasani D , Delaneau O , et al. A general approach for haplotype phasing across the full spectrum of relatedness[J]. PLoS Genet, 2014, 10 (4): e1004234.

doi: 10.1371/journal.pgen.1004234
22
Howie BN , Donnelly P , Marchini J . A flexible and accurate genotype imputation method for the next generation of genome-wide association studies[J]. PLoS Genet, 2009, 5 (6): e1000529.

doi: 10.1371/journal.pgen.1000529
23
Auton A , Brooks LD , Durbin RM , et al. A global reference for human genetic variation[J]. Nature, 2015, 526 (7571): 68- 74.

doi: 10.1038/nature15393
24
Chen CY , Chen TT , Feng YCA , et al. Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits[J]. Cell Genom, 2023, 3 (12): 100436.

doi: 10.1016/j.xgen.2023.100436
25
Chang CC , Chow CC , Tellier LC , et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets[J]. Gigascience, 2015, 4, 7.
26
Yang J , Lee SH , Goddard ME , et al. GCTA: A tool for genome-wide complex trait analysis[J]. Am J Hum Genet, 2011, 88 (1): 76- 82.

doi: 10.1016/j.ajhg.2010.11.011
27
Watanabe K , Taskesen E , van Bochoven A , et al. Functional mapping and annotation of genetic associations with FUMA[J]. Nat Commun, 2017, 8 (1): 1826.

doi: 10.1038/s41467-017-01261-5
28
Kim MS , Kim WJ , Khera AV , et al. Association between adiposity and cardiovascular outcomes: An umbrella review and meta-analysis of observational and Mendelian randomization studies[J]. Eur Heart J, 2021, 42 (34): 3388- 3403.

doi: 10.1093/eurheartj/ehab454
29
Tan J , Li B , Cao J , et al. APOE gene polymorphism in ischemic stroke patients from Huizhou and its correlation with blood lipids and homocysteine[J]. J Stroke Cerebrovasc Dis, 2024, 33 (11): 107990.

doi: 10.1016/j.jstrokecerebrovasdis.2024.107990
30
Kulminski AM , Loika Y , Culminskaya I , et al. Independent associations of TOMM40 and APOE variants with body mass index[J]. Aging Cell, 2019, 18 (1): e12869.

doi: 10.1111/acel.12869
31
Wang C , Du Z , Ye N , et al. Hyperlipidemia and hypertension have synergistic interaction on ischemic stroke: Insights from a general population survey in China[J]. BMC Cardiovasc Disord, 2022, 22 (1): 47.

doi: 10.1186/s12872-022-02491-2
32
Rao W , Su Y , Yang G , et al. Cross-sectional associations between body mass index and hyperlipidemia among adults in Northeastern China[J]. Int J Environ Res Public Health, 2016, 13 (5): 516.

doi: 10.3390/ijerph13050516
33
Ryder E , Diez-Ewald M , Mosquera J , et al. Association of obesity with leukocyte count in obese individuals without metabolic syndrome[J]. Diabetes Metab Syndr, 2014, 8 (4): 197- 204.

doi: 10.1016/j.dsx.2014.09.002
34
Schmidt FM , Weschenfelder J , Sander C , et al. Inflammatory cytokines in general and central obesity and modulating effects of physical activity[J]. PLoS One, 2015, 10 (3): e0121971.

doi: 10.1371/journal.pone.0121971
35
Fantuzzi G , Mazzone T . Adipose tissue and atherosclerosis: Exploring the connection[J]. Arterioscler Thromb Vasc Biol, 2007, 27 (5): 996- 1003.

doi: 10.1161/ATVBAHA.106.131755
36
Singer G , Granger DN . Inflammatory responses underlying the microvascular dysfunction associated with obesity and insulin resis-tance[J]. Microcirculation, 2007, 14 (4/5): 375- 387.
37
Liao W , Wen Y , Zeng C , et al. Integrative analyses and validation of ferroptosis-related genes and mechanisms associated with cerebrovascular and cardiovascular ischemic diseases[J]. BMC Genomics, 2023, 24 (1): 731.

doi: 10.1186/s12864-023-09829-w
38
Yamase Y , Horibe H , Ueyama C , et al. Association of TOMM40 and SLC22A4 polymorphisms with ischemic stroke[J]. Biomed Rep, 2015, 3 (4): 491- 498.

doi: 10.3892/br.2015.457
39
Cheng X , Ferino E , Hull H , et al. Smoking affects gene expression in blood of patients with ischemic stroke[J]. Ann Clin Transl Neurol, 2019, 6 (9): 1748- 1756.

doi: 10.1002/acn3.50876
40
Chang CT , Liao HY , Chang CM , et al. Oxidized ApoC1 on MALDI-TOF and glycated-ApoA1 band on gradient gel as potential diagnostic tools for atherosclerotic vascular disease[J]. Clin Chim Acta, 2013, 420, 69- 75.

doi: 10.1016/j.cca.2012.10.017
41
Ciuffetelli Alamo IV , Kwartler CS , Regalado ER , et al. Grange syndrome due to homozygous YY1AP1 missense rare variants[J]. Am J Med Genet A, 2019, 179 (12): 2500- 2505.

doi: 10.1002/ajmg.a.61379
42
Yin S , Pang A , Liu C , et al. Peptide OM-LV20 protects astrocytes against oxidative stress via the PAC1R/JNK/TPH1 axis[J]. J Biol Chem, 2022, 298 (10): 102429.

doi: 10.1016/j.jbc.2022.102429
43
Stelzer G , Rosen N , Plaschkes I , et al. The GeneCards suite: From gene data mining to disease genome sequence analyses[J]. Curr Protoc Bioinformatics, 2016, 54, 1.30.1- 1.30.33.
44
Tai LM , Ghura S , Koster KP , et al. APOE-modulated Aβ-induced neuroinflammation in Alzheimer' s disease: Current landscape, novel data, and future perspective[J]. J Neurochem, 2015, 133 (4): 465- 488.

doi: 10.1111/jnc.13072
45
Tai L M , Thomas R , Marottoli FM , et al. The role of APOE in cerebrovascular dysfunction[J]. Acta Neuropathol, 2016, 131 (5): 709- 723.

doi: 10.1007/s00401-016-1547-z
46
Yan B , Ai Y , Sun Q , et al. Membrane damage during ferroptosis is caused by oxidation of phospholipids catalyzed by the oxidoreductases POR and CYB5R1[J]. Mol Cell, 2021, 81 (2): 355-369. e10.

doi: 10.1016/j.molcel.2020.11.024
47
Dixon SJ , Lemberg KM , Lamprecht MR , et al. Ferroptosis: An iron-dependent form of nonapoptotic cell death[J]. Cell, 2012, 149 (5): 1060- 1072.

doi: 10.1016/j.cell.2012.03.042
48
Zhang Y , Xin L , Xiang M , et al. The molecular mechanisms of ferroptosis and its role in cardiovascular disease[J]. Biomed Pharmacother, 2022, 145, 112423.

doi: 10.1016/j.biopha.2021.112423
49
Hu X , Bao Y , Li M , et al. The role of ferroptosis and its mechanism in ischemic stroke[J]. Exp Neurol, 2024, 372, 114630.

doi: 10.1016/j.expneurol.2023.114630
50
Guo J , Tuo QZ , Lei P . Iron, ferroptosis, and ischemic stroke[J]. J Neurochem, 2023, 165 (4): 487- 520.

doi: 10.1111/jnc.15807
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