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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
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