[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 35.175.120.59. Please contact the publisher to request reinstatement.
[Skip to Content Landing]
Figure.
Flowchart of the 2-Stage Consecutive Analyses
Flowchart of the 2-Stage Consecutive Analyses

Stage 1 was a sex-specific genome-wide association study (GWAS) that analyzed single-nucleotide polymorphisms (SNPs) and pathways. Stage 2 was a sex-specific polygenic risk score (PRS) analysis. CLHLS indicates Chinese Longitudinal Healthy Longevity Study; IDEAL, European Union Longevity Genetics Consortium; NECS, New England Centenarians Study; OR, odds ratio.

Table 1.  
The 11 Male-Specific Loci Associated With Longevity and Replicated in North and South Data Setsa
The 11 Male-Specific Loci Associated With Longevity and Replicated in North and South Data Setsa
Table 2.  
The 11 Female-Specific Loci Associated With Longevity And Replicated in North and South Data Setsa
The 11 Female-Specific Loci Associated With Longevity And Replicated in North and South Data Setsa
Table 3.  
Two Female-Specific Loci Associated With Longevity in the Han Chinese CLHLS Replicated in the US NECS or the European IDEAL
Two Female-Specific Loci Associated With Longevity in the Han Chinese CLHLS Replicated in the US NECS or the European IDEAL
Table 4.  
Polygenic Risk Score Analyses on the Joint Effects of Sex-Specific Loci’s Association With Longevity
Polygenic Risk Score Analyses on the Joint Effects of Sex-Specific Loci’s Association With Longevity
1.
Robine  JM, Saito  Y, Jagger  C.  The relationship between longevity and healthy life expectancy.  Qual Ageing Older Adults. 2009;10(2):5-14. doi:10.1108/14717794200900012Google ScholarCrossref
2.
Sebastiani  P, Solovieff  N, Dewan  AT,  et al.  Genetic signatures of exceptional longevity in humans.  PLoS One. 2012;7(1):e29848. doi:10.1371/journal.pone.0029848PubMedGoogle ScholarCrossref
3.
Deelen  J, Beekman  M, Uh  HW,  et al.  Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age.  Hum Mol Genet. 2014;23(16):4420-4432. doi:10.1093/hmg/ddu139PubMedGoogle ScholarCrossref
4.
Newman  AB, Walter  S, Lunetta  KL,  et al.  A meta-analysis of four genome-wide association studies of survival to age 90 years or older: the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.  J Gerontol A Biol Sci Med Sci. 2010;65(5):478-487. doi:10.1093/gerona/glq028PubMedGoogle ScholarCrossref
5.
Zeng  Y, Nie  C, Min  J,  et al.  Novel loci and pathways significantly associated with longevity.  Sci Rep. 2016;6:21243. doi:10.1038/srep21243PubMedGoogle ScholarCrossref
6.
Zeng  Y, Cheng  L, Chen  H,  et al.  Effects of FOXO genotypes on longevity: a biodemographic analysis.  J Gerontol A Biol Sci Med Sci. 2010;65(12):1285-1299. doi:10.1093/gerona/glq156PubMedGoogle ScholarCrossref
7.
Hughes  T, Adler  A, Merrill  JT,  et al; BIOLUPUS Network.  Analysis of autosomal genes reveals gene-sex interactions and higher total genetic risk in men with systemic lupus erythematosus.  Ann Rheum Dis. 2012;71(5):694-699. doi:10.1136/annrheumdis-2011-200385PubMedGoogle ScholarCrossref
8.
Gilks  WP, Abbott  JK, Morrow  EH.  Sex differences in disease genetics: evidence, evolution, and detection.  Trends Genet. 2014;30(10):453-463. doi:10.1016/j.tig.2014.08.006PubMedGoogle ScholarCrossref
9.
Mielke  MM, Vemuri  P, Rocca  WA.  Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences.  Clin Epidemiol. 2014;6:37-48. doi:10.2147/CLEP.S37929PubMedGoogle ScholarCrossref
10.
Pilling  LC, Kuo  CL, Sicinski  K,  et al.  Human longevity: 25 genetic loci associated in 389,166 UK biobank participants.  Aging (Albany NY). 2017;9(12):2504-2520.PubMedGoogle ScholarCrossref
11.
Cohen  J, Cohen  P, West  SG, Aiken  LS.  Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3rd ed. Routledge, UK: Abingdon-on-Thames Press; 2013.
12.
Little  J, Higgins  JP, Ioannidis  JP,  et al.  Strengthening the Reporting of Genetic Association Studies (STREGA)—an extension of the STROBE statement.  Eur J Clin Invest. 2009;39(4):247-266. doi:10.1111/j.1365-2362.2009.02125.xPubMedGoogle ScholarCrossref
13.
Xu  S, Yin  X, Li  S,  et al.  Genomic dissection of population substructure of Han Chinese and its implication in association studies.  Am J Hum Genet. 2009;85(6):762-774. doi:10.1016/j.ajhg.2009.10.015PubMedGoogle ScholarCrossref
14.
Jia  P, Wang  L, Fanous  AH, Pato  CN, Edwards  TL, Zhao  Z; International Schizophrenia Consortium.  Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.  PLoS Comput Biol. 2012;8(7):e1002587. doi:10.1371/journal.pcbi.1002587PubMedGoogle ScholarCrossref
15.
Beecham  AH, Patsopoulos  NA, Xifara  DK,  et al; International Multiple Sclerosis Genetics Consortium (IMSGC); Wellcome Trust Case Control Consortium 2 (WTCCC2); International IBD Genetics Consortium (IIBDGC).  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.  Nat Genet. 2013;45(11):1353-1360. doi:10.1038/ng.2770PubMedGoogle ScholarCrossref
16.
Purcell  S, Neale  B, Todd-Brown  K,  et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses.  Am J Hum Genet. 2007;81(3):559-575. doi:10.1086/519795PubMedGoogle ScholarCrossref
17.
Price  AL, Butler  J, Patterson  N,  et al.  Discerning the ancestry of European Americans in genetic association studies.  PLoS Genet. 2008;4(1):e236. doi:10.1371/journal.pgen.0030236PubMedGoogle ScholarCrossref
18.
Euesden  J, Lewis  CM, O’Reilly  PF.  PRSice: polygenic risk score software.  Bioinformatics. 2015;31(9):1466-1468. doi:10.1093/bioinformatics/btu848PubMedGoogle ScholarCrossref
19.
Zhang  K, Cui  S, Chang  S, Zhang  L, Wang  J.  i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study.  Nucleic Acids Res. 2010;38(web server issue)(suppl 2):W90-W95. doi:10.1093/nar/gkq324PubMedGoogle ScholarCrossref
20.
Purcell  SM, Wray  NR, Stone  JL,  et al; International Schizophrenia Consortium.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder.  Nature. 2009;460(7256):748-752.PubMedGoogle Scholar
21.
Natarajan  K, Xie  Y, Baer  MR, Ross  DD.  Role of breast cancer resistance protein (BCRP/ABCG2) in cancer drug resistance.  Biochem Pharmacol. 2012;83(8):1084-1103. doi:10.1016/j.bcp.2012.01.002PubMedGoogle ScholarCrossref
22.
Moruzzi  S, Guarini  P, Udali  S,  et al.  One-carbon genetic variants and the role of MTHFD1 1958G>A in liver and colon cancer risk according to global DNA methylation.  PLoS One. 2017;12(10):e0185792. doi:10.1371/journal.pone.0185792PubMedGoogle ScholarCrossref
23.
Fillmore  CM, Gupta  PB, Rudnick  JA,  et al.  Estrogen expands breast cancer stem-like cells through paracrine FGF/Tbx3 signaling.  Proc Natl Acad Sci U S A. 2010;107(50):21737-21742. doi:10.1073/pnas.1007863107PubMedGoogle ScholarCrossref
24.
Klein  SL, Jedlicka  A, Pekosz  A.  The Xs and Y of immune responses to viral vaccines.  Lancet Infect Dis. 2010;10(5):338-349. doi:10.1016/S1473-3099(10)70049-9PubMedGoogle ScholarCrossref
25.
Goetzl  EJ, Huang  MC, Kon  J,  et al.  Gender specificity of altered human immune cytokine profiles in aging.  FASEB J. 2010;24(9):3580-3589. doi:10.1096/fj.10-160911PubMedGoogle ScholarCrossref
26.
Hewagama  A, Patel  D, Yarlagadda  S, Strickland  FM, Richardson  BC.  Stronger inflammatory/cytotoxic T-cell response in women identified by microarray analysis.  Genes Immun. 2009;10(5):509-516. doi:10.1038/gene.2009.12PubMedGoogle ScholarCrossref
27.
Marttila  S, Jylhävä  J, Nevalainen  T,  et al.  Transcriptional analysis reveals gender-specific changes in the aging of the human immune system.  PLoS One. 2013;8(6):e66229. doi:10.1371/journal.pone.0066229PubMedGoogle ScholarCrossref
28.
Bonafè  M, Olivieri  F, Cavallone  L,  et al.  A gender-dependent genetic predisposition to produce high levels of IL-6 is detrimental for longevity.  Eur J Immunol. 2001;31(8):2357-2361. doi:10.1002/1521-4141(200108)31:8<2357::AID-IMMU2357>3.0.CO;2-XPubMedGoogle ScholarCrossref
29.
Agrawal  A, Agrawal  S, Cao  JN, Su  H, Osann  K, Gupta  S.  Altered innate immune functioning of dendritic cells in elderly humans: a role of phosphoinositide 3-kinase-signaling pathway.  J Immunol. 2007;178(11):6912-6922. doi:10.4049/jimmunol.178.11.6912PubMedGoogle ScholarCrossref
30.
Melkamu  T, Kita  H, O’Grady  SM.  TLR3 activation evokes IL-6 secretion, autocrine regulation of Stat3 signaling and TLR2 expression in human bronchial epithelial cells.  J Cell Commun Signal. 2013;7(2):109-118. doi:10.1007/s12079-012-0185-zPubMedGoogle ScholarCrossref
31.
Varthaman  A, Moreau  HD, Maurin  M, Benaroch  P.  TLR3-induced maturation of murine dendritic cells regulates CTL responses by modulating PD-L1 trafficking.  PLoS One. 2016;11(12):e0167057. doi:10.1371/journal.pone.0167057PubMedGoogle ScholarCrossref
32.
Kong  KF, Delroux  K, Wang  X,  et al.  Dysregulation of TLR3 impairs the innate immune response to West Nile virus in the elderly.  J Virol. 2008;82(15):7613-7623. doi:10.1128/JVI.00618-08PubMedGoogle ScholarCrossref
33.
Collino  S, Montoliu  I, Martin  FPJ,  et al.  Metabolic signatures of extreme longevity in northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism.  PLoS One. 2013;8(3):e56564. doi:10.1371/journal.pone.0056564PubMedGoogle ScholarCrossref
34.
McGaha  TL, Huang  L, Lemos  H,  et al.  Amino acid catabolism: a pivotal regulator of innate and adaptive immunity.  Immunol Rev. 2012;249(1):135-157. doi:10.1111/j.1600-065X.2012.01149.xPubMedGoogle ScholarCrossref
35.
Colegio  OR, Chu  NQ, Szabo  AL,  et al.  Functional polarization of tumour-associated macrophages by tumour-derived lactic acid.  Nature. 2014;513(7519):559-563. doi:10.1038/nature13490PubMedGoogle ScholarCrossref
36.
Cantó  C, Gerhart-Hines  Z, Feige  JN,  et al.  AMPK regulates energy expenditure by modulating NAD+ metabolism and SIRT1 activity.  Nature. 2009;458(7241):1056-1060. doi:10.1038/nature07813PubMedGoogle ScholarCrossref
37.
Gomes  AP, Price  NL, Ling  AJY,  et al.  Declining NAD(+) induces a pseudohypoxic state disrupting nuclear-mitochondrial communication during aging.  Cell. 2013;155(7):1624-1638. doi:10.1016/j.cell.2013.11.037PubMedGoogle ScholarCrossref
38.
Dudbridge  F.  Power and predictive accuracy of polygenic risk scores.  PLoS Genet. 2013;9(3):e1003348. doi:10.1371/journal.pgen.1003348PubMedGoogle ScholarCrossref
39.
National Bureau of Statistics of China.  Population Census Office Under the State Council and Department of Population, Social, Science and Technology Statistics, National Bureau of Statistics of the People’s Republic of China. Tabulations of the 2000 Census of China. Beijing, China: China Statistics Press; 2002.
40.
Broer  L, Buchman  AS, Deelen  J,  et al.  GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy.  J Gerontol A Biol Sci Med Sci. 2015;70(1):110-118. doi:10.1093/gerona/glu166PubMedGoogle ScholarCrossref
41.
Ober  C, Loisel  DA, Gilad  Y.  Sex-specific genetic architecture of human disease.  Nat Rev Genet. 2008;9(12):911-922. doi:10.1038/nrg2415PubMedGoogle ScholarCrossref
42.
Moon  H, Lopez  KL, Lin  GI, Chen  JJ.  Sex-specific genomic biomarkers for individualized treatment of life-threatening diseases.  Dis Markers. 2013;35(6):661-667. doi:10.1155/2013/393020PubMedGoogle ScholarCrossref
43.
Kajinami  K, Brousseau  ME, Ordovas  JM, Schaefer  EJ.  Polymorphisms in the multidrug resistance-1 (MDR1) gene influence the response to atorvastatin treatment in a gender-specific manner.  Am J Cardiol. 2004;93(8):1046-1050. doi:10.1016/j.amjcard.2004.01.014PubMedGoogle ScholarCrossref
44.
Zhang  W, Press  OA, Haiman  CA,  et al.  Association of methylenetetrahydrofolate reductase gene polymorphisms and sex-specific survival in patients with metastatic colon cancer.  J Clin Oncol. 2007;25(24):3726-3731. doi:10.1200/JCO.2007.11.4710PubMedGoogle ScholarCrossref
45.
Jameson  JL,  et al.  Precision medicine—personalized, problematic, and promising.  Obstet Gynecol Surv. 2015;70(10):612-614. doi:10.1097/01.ogx.0000472121.21647.38PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    1 Comment for this article
    Explaining why women live longer than men
    Frederick Rivara, MD | University of Washington
    This is a fascinating study that helps us understand the genetic basis for longevity and the sex differences in it. It provides the scientific basis for further studies on sex specific genomics associated with long life.
    CONFLICT OF INTEREST: Editor in chief, JAMA Network Open
    Original Investigation
    Genetics and Genomics
    August 24, 2018

    Sex Differences in Genetic Associations With Longevity

    Author Affiliations
    • 1Center for the Study of Aging and Human Development, Medical School of Duke University, Durham, North Carolina
    • 2Center for Healthy Aging and Development Studies, National School of Development, Raissun Institute for Advanced Studies, Peking University, Beijing, China
    • 3BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China
    • 4BGI–Shenzhen, Shenzhen, China
    • 5The First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China
    • 6Business School of Xiangtan University, Xiangtan, China
    • 7Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing, China
    • 8National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
    • 9Department of Sociology, Peking University, Beijing, China
    • 10School of Life Sciences, Fudan University, Shanghai, China
    • 11Department of Biomedicine, Aarhus University, Aarhus, Denmark
    • 12Duke Population Research Institute, Duke University, Durham, North Carolina
    • 13The MOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, China
    • 14School of Life Sciences, Peking University, Beijing, China
    • 15Boston University, Boston, Massachusetts
    • 16University of Bologna, Bologna, Italy
    • 17Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
    • 18French National Institute on Health and Medical Research and Ecole Pratique des Hautes Etudes, University of Montpellier, Montpellier, France
    • 19Max Planck Institute for Biology of Ageing, Cologne, Germany
    • 20Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
    • 21Molecular Physiology Institute, Medical Center, Duke University, Durham, North Carolina
    • 22Department of Neurology, Medical Center, Duke University, Durham, North Carolina
    • 23University of Southern Denmark, Odense, Denmark
    • 24Human Aging Research Institute and School of Life Science, Nanchang University, Jiangxi, China
    • 25James D. Watson Institute of Genome Sciences, Hangzhou, China
    • 26Max Planck Institute for Demographic Research, Rostock, Germany
    JAMA Netw Open. 2018;1(4):e181670. doi:10.1001/jamanetworkopen.2018.1670
    Key Points español 中文 (chinese)

    Question  Are there sex differences in genetic associations with longevity?

    Findings  In this case-control study of 2178 cases and 2299 controls who were Chinese with Han ethnicity, sex-specific genome-wide association study and sex-specific polygenic risk score analyses on longevity showed substantial and significant differences in genetic associations with longevity between men and women. Findings indicated that previously published genome-wide association studies on longevity identified some sex-independent genetic variants but missed sex-specific longevity loci and pathways.

    Meaning  These novel findings contribute to filling the gaps in the research literature, and further investigations may substantially contribute to individualized health care and more effective and targeted health interventions for male and female elderly individuals.

    Abstract

    Importance  Sex differences in genetic associations with human longevity remain largely unknown; investigations on this topic are important for individualized health care.

    Objective  To explore sex differences in genetic associations with longevity.

    Design, Setting, and Participants  This population-based case-control study used sex-specific genome-wide association study and polygenic risk score (PRS) analyses to examine sex differences in genetic associations with longevity. Five hundred sixty-four male and 1614 female participants older than 100 years were compared with a control group of 773 male and 1526 female individuals aged 40 to 64 years. All were Chinese Longitudinal Healthy Longevity Study participants with Han ethnicity who were recruited in 1998 and 2008 to 2014.

    Main Outcomes and Measures  Sex-specific loci and pathways associated with longevity and PRS measures of joint effects of sex-specific loci.

    Results  Eleven male-specific and 11 female-specific longevity loci (P < 10−5) and 35 male-specific and 25 female-specific longevity loci (10−5 ≤ P < 10−4) were identified. Each of these loci’s associations with longevity were replicated in north and south regions of China in one sex but were not significant in the other sex (P = .13-.97), and loci-sex interaction effects were significant (P < .05). The associations of loci rs60210535 of the LINC00871 gene with longevity were replicated in Chinese women (P = 9.0 × 10−5) and US women (P = 4.6 × 10−5) but not significant in Chinese and US men. The associations of the loci rs2622624 of the ABCG2 gene were replicated in Chinese women (P = 6.8 × 10−5) and European women (P = .003) but not significant in both Chinese and European men. Eleven male-specific pathways (inflammation and immunity genes) and 34 female-specific pathways (tryptophan metabolism and PGC-1α induced) were significantly associated with longevity (P < .005; false discovery rate < 0.05). The PRS analyses demonstrated that sex-specific associations with longevity of the 4 exclusive groups of 11 male-specific and 11 female-specific loci (P < 10−5) and 35 male-specific and 25 female-specific loci (10−5 ≤P < 10−4) were jointly replicated across north and south discovery and target samples. Analyses using the combined data set of north and south showed that these 4 groups of sex-specific loci were jointly and significantly associated with longevity in one sex (P = 2.9 × 10−70 to 1.3 × 10−39) but not jointly significant in the other sex (P = .11 to .70), while interaction effects between PRS and sex were significant (P = 4.8 × 10−50 to 1.2 × 10−16).

    Conclusion and Relevance  The sex differences in genetic associations with longevity are remarkable, but have been overlooked by previously published genome-wide association studies on longevity. This study contributes to filling this research gap and provides a scientific basis for further investigating effects of sex-specific genetic variants and their interactions with environment on healthy aging, which may substantially contribute to more effective and targeted individualized health care for male and female elderly individuals.

    Introduction

    Centenarian genomes may harbor genetic variants associated with longevity and health,1-5 supported by the fact that the proportion of genetic variants positively (or negatively) associated with longevity and health is significantly higher (or lower) among centenarians compared with middle-aged controls. This is because those who carry the longevity-favoring genetic variants have a better chance of surviving to age 100 years or older, while those with less favorable genetic variants may not reach 100 years. This relationship has been demonstrated empirically1-6 and proven mathematically.6 Hence, all of the genome-wide association studies (GWAS) on longevity use centenarians (and/or those aged ≥90 years or ≥85 years) as cases and younger adults as controls2-5 (eAppendix section S1 in the Supplement).

    The extant literature indicates that associations of some genetic variants with health outcomes differ significantly between men and women.7-9 A recent study using the phenotype of parental age at death as an outcome variable indicated that different genes may be associated with longevity in men and women.10 However, sex differences have been overlooked in all previously published GWAS on longevity that used male and female combined data sets adjusted for sex as a covariate.2-5 A few GWAS of longevity conducted sex-specific analyses on the significant loci that were replicated in the combined male and female discovery and evaluation stages, but none of those studies found that their replicated loci had significant sex differences in the association with longevity.2-5 This is because, statistically, if the tested variable is significant in one sex but not significant in the other sex, it cannot be significant and replicated in the combined data sets, as the results of 2 sexes offset each other in a combined data set of male and female results, while the sample size of either one of the sexes is usually not small enough to leave the overall results unaffected.11 In other words, all previously published GWAS on longevity identified sex-independent genetic variants, but the sex differences have been overlooked. The present study aims to fill this research gap and contribute to a better understanding of sex differences in genetic associations with longevity.

    Methods

    We analyzed Chinese Longitudinal Healthy Longevity Study (CLHLS) data sets of GWAS on longevity, with 564 male and 1614 female participants aged 100 years or older (mean [SD] age, 102.7 [3.49] years) as cases and 773 male and 1526 female participants aged 40 to 64 years (mean [SD] age, 48.4 [7.44] years) as controls. All were Chinese with Han ethnicity (eAppendix sections S2-S3 in the Supplement). The CLHLS GWAS has the largest sample size of centenarians in the world, 2.7 times as large as the next largest sample of centenarians of GWAS on longevity. The CLHLS GWAS includes 5.6 million single-nucleotide polymorphisms (SNPs) (0.82 million genotyped SNPs and 4.8 million imputed SNPs) for each of the centenarians and middle-aged controls (eAppendix section S3.1 in the Supplement).5 The CLHLS GWAS followed the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guideline for GWAS quality control,12 including genotyping errors, population stratification, and Hardy-Weinberg equilibrium, with a full quality item score of 12, indicating good quality and completeness.5 The Research Ethics Committees of Peking University and Duke University granted approval for the Protection of Human Subjects for the CLHLS, including collections of questionnaire data and DNA samples with written informed consent before participation.

    The Chinese with Han ethnicity make up about 93% of the total population in China, with 53 Chinese minority groups making up 7% of the total population. The sample sizes of any minority group in the CLHLS data are too small for meaningful analysis, so we included Han Chinese samples only in the present study.5 Detailed descriptions of the CLHLS phenotype and genotype data sets are presented in eAppendix sections S2 and S3 in the Supplement.

    We adopted a stratification framework of north and south regions of China as discovery and evaluation samples (eAppendix section S3.2, eTable 1, and eFigures 1-4 in the Supplement), following most published case-control genetic studies using Chinese nationwide data sets and based on analyses of principal components, genetics (classic markers, microsatellite DNA markers, mitochondrial DNA, and Y chromosome SNP markers), anthropology, and linguistics, reported in the literature.13

    We conducted 2-stage consecutive analyses, with sex-specific GWAS to identify candidate sex-specific loci and sex-specific pathways in stage 1 and polygenic risk score (PRS) analysis in stage 2 (Figure). To avoid the high false-negative rate and to fully use the available independent GWAS data sets of north and south regions of China, we applied the bidirectional discovery and evaluation approach14 (eAppendix section S4 in the Supplement) in our sex-specific GWAS and PRS analyses. A priori thresholds of P < 10−5, P < 10−4, or P < 10−3 or higher were defined for selecting informative SNPs in the discovery step of recent GWAS or PRS studies depending on the circumstances of the research, while P < 5 × 10−8 is the standard for genome-wide significance.15 We aimed to identify groups of sex-specific SNPs that individually may have very small effects but may jointly have large effects. Thus, it is reasonable to choose a modest a priori threshold of P < 10−3 and P < .01 in the discovery step of sex-specific single SNP analysis. We performed sex-specific GWAS using PLINK (version 1.06).16 To minimize population stratification effects, we adjusted for the top 2 eigenvectors, which corrected nearly all of the stratification that can be corrected.17 In the combined north and south data analysis, we also adjusted for respective north and south regions.

    The best-fit P value cutoffs .0042 and .02 (calculated by PRSice software with the BEST_FIT command18) were used to select SNPs for pathway analyses in men and women, respectively. We implemented an improved gene set enrichment analysis for GWAS using the i-GSEA4GWAS database19 to map genes to pathways. Sex-specific pathway gene sets with P < .005 and false discovery rate (FDR) < 0.05 were regarded as significantly associated with longevity.

    We conducted PRS analyses in stage 2 based on 2 considerations. First, each of the candidate sex-specific loci identified in stage 1 had a very small effect, leading to further assessment of their joint effects by PRS analyses. Second, the candidate sex-specific loci selected in stage 1 were individually not significant (P > .05) in the other sex, but their joint effects could be large and significant in the other sex (eAppendix section S5 in the Supplement); PRS analyses allowed us to evaluate and filter out those loci that are not truly sex specific.

    Using PRSice software18 and standard methods,20 we constructed PRS scores as the sum of the number of risk allele copies of each of the selected loci multiplied by the log of the corresponding odds ratio of longevity, and then divided by the total number of selected loci for each of the centenarians and middle-aged controls. We conducted analysis including a PRS-sex interaction term based on the continuous PRS. We used the PRSice clumping method to select independent loci by excluding all SNPs with linkage disequilibrium (r2 > 0.1); only independent loci were used to calculate the PRS.

    Following standard procedures,20 we used the sex-specific odds ratios estimated based on the discovery sample of north (or south) region as weights to construct the PRS in the target sample of south (or north) region; we also conducted the PRS analysis on the sex-specific loci that were replicated across discovery and target samples, using the north-south combined data set.

    Results
    Analyses of Single SNPs

    Results in Table 1 indicate 11 independent male-specific loci (including the SNP rs1950902 in the MTHFD1 gene) associated with longevity that replicate in the male discovery and evaluation data sets of north and south regions (with P < 10−3 in the discovery step) and reached P < 10−5 and FDR < 10−4 in the male north-south combined data set, but were not significant (P = .17-.95) in the female north-south combined data set. The loci-sex interaction effects of these loci were significant (P = 8.40 × 10−6 to 8.45 × 10−4).

    As shown in Table 2, we identified 11 independent female-specific loci (including the SNP rs1027238 at the FAM19A1 gene and the SNP rs2161877 near TBX3) whose associations with longevity were replicated in female discovery and evaluation data sets of north and south regions (with P < 10−3 in the discovery step) and reached P < 10−5 and FDR < 10−4 in the female north-south combined data set, but were not significant (P = .13-.97) in the male north-south combined data set. The loci-sex interaction effects of these female-specific loci were significant (P = 2.8 × 10−4 to 2.5 × 10−2).

    Following the widely practiced approach in the PRS literature,18,20 in addition to the 11 male-specific and 11 female-specific loci outlined, we also identified candidate sex-specific loci with a more relaxed prior threshold for further PRS analyses. With a prior threshold of P < .01 in the discovery step, we found that additional 47 male-specific and 34 female-specific independent loci were associated with longevity and replicated across north and south samples, had a 10−5 ≤ P < 10−4 in one sex but were not significant in the other sex, and had P < .05 for the loci-sex interaction effects, using the north-south combined data set. As discussed earlier, the 11 male-specific and 11 female-specific loci (P < 10−5) and 47 male-specific and 34 female-specific loci (10−5 ≤ P < 10−4) are individually candidates of sex-specific longevity loci, and whether their joint effects are truly sex-specific was investigated in the PRS analyses.

    The Chinese sex-specific loci that were significant (P < 10−4) in one sex but not significant (P > .05) in the other sex and available in the New England Centenarians Study (NECS) and European Union Longevity Genetics Consortium (IDEAL) were tested for replication in NECS and IDEAL. The samples and data sources of GWAS on longevity from NECS and IDEAL are described by Sebastiani et al2 and Deelen et al.3 The results of comparisons across the Chinese CLHLS, the US NECS and European IDEAL presented in Table 3, show that rs60210535 of LINC00871 replicated between Chinese (P = 9.0 × 10−5) and American (P = 4.6 × 10−5) women, but was not significant in both Chinese and American men (P = .49-.69). Another female-specific locus, rs2622624 of ABCG2, had P = 6.8 × 10−5 in Chinese women and P = .003 in European women but was not significant in both Chinese and European men (P = .08-.59). ABCG2 is a well-known breast cancer resistance protein (BCRP).21LINC00871 is a noncoding RNA gene, and its function is uncertain.

    Sex-Specific Pathway Analysis

    Sex-specific differences were found in the biochemical pathways that influence human longevity. There are 11 pathways significantly associated with longevity in men (P < .005 and FDR < 0.05) (eTable 2 in the Supplement). These pathways are enriched mainly for immune and inflammatory responses, including immunity (TLR3) pathway, inflammatory cytokines and Toll-like receptor (TLR) signaling pathways, and the proinflammatory cytokine interleukin 6 (IL-6) pathway. In women, 34 pathways were enriched significantly (P < .005 and FDR < 0.05) and clustered to metabolic pathways (eTable 3 in the Supplement). The tryptophan metabolic pathway and the PPARγ coactivator-1α (PGC-1α) pathway were among the top pathways in this set.

    PRS Analyses to Assess Joint Effects of Groups of Sex-Specific Loci on Longevity

    The PRS analyses using the north (or south) data set as the discovery sample and the south (or north) data set as the target sample showed that sex-specific joint associations with longevity of the 11 male-specific and 11 female-specific loci were replicated across north and south samples. More specifically, either using the north sample as the discovery and the south sample as the target, or vice versa, the 11 male-specific and 11 female-specific loci were jointly and significantly associated with longevity in one sex (P = 7.2 × 10−22 to 4.0 × 10−12) but not jointly associated with longevity in the other sex (P = .15-.76); the PRS-sex interaction effects were significant (P = 5.6 × 10−20 to 6.5 × 10−8) (Table 4).

    As discussed in eAppendix section S5 in the Supplement, based on the additional 47 male-specific and 34 female-specific candidate loci outlined earlier, we applied the stepwise approach that has been used widely in the PRS literature18,20 and we used the PRSice method and software18 to select an ideal P value cutoff (PT) in the other sex to provide the best-fitting PRS; we further identified 35 north-south individually replicated male-specific loci with P < 10−4 in men but P > .25 in women and 25 female-specific loci with P < 10−4 in women but P > .35 in men (eTables 4 and 5 in the Supplement). The results indicate that the sex-specific joint associations with longevity of these 35 male-specific and 25 female-specific loci were replicated across north and south samples; namely, they were jointly and significantly associated with longevity in one sex (P = 5.4 × 10−35 to 1.8 × 10−26) but not jointly associated with longevity in the other sex (P = .07-.93), and the PRS-sex interaction effects were significant (P = 2.2 × 10−16 to 7.2 × 10−30), either using the north sample as the discovery and the south as the target, or vice versa (Table 4).

    Analyses using the north-south combined data set showed that the 11 male-specific and 11 female-specific loci (P < 10−5) and 35 male-specific and 25 female-specific loci (10−5 ≤ P < 10−4) were jointly and significantly associated with longevity in one sex (P = 2.9 × 10−70 to 1.3 × 10−39) but not jointly significant in the other sex (P = .11 to .70); PRS-sex interaction effects were significant (P = 4.8 × 10−50 to 1.2 × 10−16) (Table 4).

    Discussion

    Of the 11 male-specific loci associated with longevity, rs1950902 in the MTHFD1 gene is a nonsynonymous SNP that causes a C-to-T transition at nucleotide 401 resulting in an arginine-to-lysine substitution at amino acid 134 (C401T;R134K). MTHFD1 was found to be associated with a protective role for colon and liver cancer risks prevalent in men22 and is consistent with the present study that MTHFD1 is significantly and positively associated with longevity in men (P = 1.09 × 10−7) but not significant in women (P = .95) (Table 1).

    Among the 11 male-specific loci associated with longevity, the SNP rs1027238 at FAM19A1 was identified as a novel SNP that is significantly associated with longevity in women (P = 2.8 × 10−6) but not in men (P = .37). The SNP rs2161877 near TBX3 was significantly associated with longevity in women (P = 2.9 × 10−6) but not in men (P = .72), which is consistent with previous findings that TBX3 plays an important role in mammary gland development and breast cancer with a close relationship to estrogen.23

    Clinical data demonstrate that men and women differ regarding their innate, humoral, and cell-mediated responses to bacterial and viral challenge.24 For example, men develop lower antibody responses and show significantly lower vaccine efficacy than women. Moreover, it is well known that longevity is associated with sex-specific differences in the immune system, and that there is a progressive decline in immunity and dysregulated inflammatory response in men.25,26 Consistent with these trends, and with previous genetics findings,27,28 we found that the proinflammatory cytokine IL-6 pathway was significantly associated with longevity in men. Furthermore, we found that the TLR3 signaling pathway was the most significant pathway associated with male longevity. Others have also reported that the TLR3 signaling pathway is dysregulated in elderly humans.29 TLR3 signaling evokes IL-6 production,30 and it initiates innate immunity and facilitates adaptive immunity by promoting maturation of dendritic cells.30,31 It is reasonable to hypothesize that dysregulation of the IL-6 and TLR3 signaling pathways renders men more susceptible than women to bacterial and viral infections; conversely, in long-lived men, altered IL-6 and TLR3 signaling pathways may provide greater protection against these challenges.32

    Our findings regarding the female-specific tryptophan metabolic pathway reflect the documented significantly lower tryptophan levels in blood serum in female centenarians compared with the younger female controls (P < .001), but that the differences were not significant in male centenarians compared with younger male controls.33 Tryptophan metabolism contributes to a number of key processes, ranging from regulating innate and adaptive immunity34 to supporting intermediary metabolism via the provision of nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate to the biosynthesis of serotonin and related signaling molecules. PGC-1α is the master regulator of mitochondrial biogenesis and function because it promotes the expression of many of the more than 1000 nuclear-encoded mitochondrial genes and also participates in the regulation of innate immunity.35 One product of tryptophan metabolism, NAD+, is a cofactor for sirtuins, which have been implicated in inflammation, stress resistance, and aging. Coincidentally, sirtuin 1 deacetylates PGC-1α and enhances PGC-1α activity.36 Aging is associated with progressive mitochondrial dysfunction, and while the ultimate cause for this dysfunction is unknown, insufficient NAD+ availability and sirtuin 1 enzymatic activity may be contributing factors.36,37

    In considering the female and male longevity-associated pathways together, the potential involvement of the innate immune system in men and of the tryptophan and PGC-1 pathways in the regulation of immune-related pathways in women suggests that women and men have optimized different approaches for solving the same biological riddle.

    The estimates using QUANTO software version 1.1 (USC Biostats) indicate that both our male-specific GWAS and female-specific GWAS have acceptably good power (eTables 6a-6b in the Supplement). The estimates using the AVENGEME software38 indicate that power for both of our male-specific and female-specific PRS analyses is excellent: 0.997 to 0.999 for men and 1.00 for women (eTable 7 in the Supplement). As discussed in the Methods section, the sex-specific GWAS (stage 1) provides candidate sex-specific loci, and our conclusions of reconfirmed sex-specific longevity loci are mainly based on the PRS analyses (stage 2).

    One may question whether the findings that loci that are significantly associated with longevity in women but not significant in men (Table 2 and Table 4; eTable 5 in the Supplement) are due to the substantially smaller sample size of male centenarians compared with female centenarians, which is common to all studies on longevity involving centenarians. We do not think this is the case because male centenarians are much more stringently mortality selected than their female counterparts, given that there were 2.3 male centenarians per 1 million men compared with 7.8 female centenarians per 1 million women in China in the 1990s,39 and the death rates in men were significantly higher than those of women at younger and older ages. Consequently, the P values of loci-sex interaction effects for male-specific loci (Table 1; eTable 4 in the Supplement) are all substantially smaller (ie, more significant) than the P values of loci-sex interaction effects for female-specific loci (Table 2; eTable 5 in the Supplement). These phenomena reflect a function of the greater mortality selection of survival to ages 100 years and older for the male centenarians than the female centenarians. Clearly, the male centenarians’ more stringent mortality selection may partially offset the shortage of power due to their much smaller sample size compared with female centenarians.

    Limitations

    While our findings are innovative, the present study has some important limitations warranting further investigation. Unanswered questions include whether the genetic association with longevity is stronger in women or men and what the sex differences are in the genetic variants that are positively or negatively associated with longevity. More replications, meta-analyses, functional validations, and investigations of the effects of interactions between sex-specific genetic variants and environmental factors on health outcomes remain to be explored. Such further investigations may substantially contribute to more effective and targeted individualized health care for male and female elderly populations.

    Conclusions

    The findings of the present study clearly indicate sex differences in genetic associations with longevity. Sex-specific associations with longevity of 4 exclusive groups of 11 male-specific and 11 female-specific loci (P < 10−5) and 35 male-specific and 25 female-specific loci (P < 10−4) are individually and jointly replicated across north and south discovery and target samples. Analyses using the north-south combined data set showed that these 4 groups of sex-specific loci are jointly and significantly associated with longevity in one sex (P = 2.9 × 10−70 to 1.3 × 10−39), but not jointly significant in the other sex (P = .11-.70), while interaction effects between PRS and sex are significant (P = 4.8 × 10−50 to 1.2 × 10−16). Although we recognize the large differences across ethnicities of different continents, it is noteworthy that 2 sex-specific loci were replicated between Chinese and US or European populations. We discovered that 11 male-specific pathways (inflammation and immunity genes) and 34 female-specific pathways (tryptophan metabolism and PGC-1α induced) are significantly associated with longevity.

    As shown in Table 1 and Table 2 and eTables 4 and 5 in the Supplement, if one estimated regressions using the male-female combined data set adjusted for sex as a covariate without a loci-sex interaction term as used in all previously published GWAS on longevity,2-5 the P values of all of the north-south replicated sex-specific longevity loci listed in Table 1 and Table 2 and eTables 4 and 5 in the Supplement would increase substantially, and they would all become nonsignificant with the given suggestive significance level of P < 10−5 or P < 10−4, and 2 male-specific longevity loci (P < 10−5) in Table 1 and 11 male-specific longevity loci (P < 10−4) in eTable 4 in the Supplement would even have a P > .05. This is because the associations of the sex-specific loci with longevity are substantially offset by the nonsignificance in the other sex if the male-female combined data set were used while adjusted for sex as a covariate. As reviewed in the Introduction section, all previously published GWAS on longevity identified sex-independent genetic variants (such as APOE, 5q33.3, IL6, FOXO1A, and FOXO3A)2-5,40 but missed sex-specific loci and pathways associated with longevity. This is consistent with the conclusion that “genetic studies that ignore sex-specific effects in their design and interpretation could fail to identify a significant proportion of the genes that contribute to risk for complex diseases.”41 The present study contributes to filling this gap and identifies significant sex differences in genetic association with longevity.

    Numerous studies have demonstrated sex differences in genetic variants’ reactions to the same nutritional intervention or drug treatment, steering away from the traditional view of one-size-fits-all health care and medicine.42-45 The present study provides a scientific basis for further investigations on sex-specific genetic variants associated with longevity and health to contribute to individualized health care. For example, the sex-specific loci and pathways significantly associated with longevity identified in the present study may serve as potential candidates of the sex-specific genomic biomarkers for in-depth research to be used in effective individualized health promotions and interventions.

    Back to top
    Article Information

    Accepted for Publication: May 15, 2018.

    Published: August 24, 2018. doi:10.1001/jamanetworkopen.2018.1670

    Correction: This article was corrected September 21, 2018, to fix an error in an academic degree in the byline.

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Zeng Y et al. JAMA Network Open.

    Corresponding Authors: Yi Zeng, PhD, Center for the Study of Aging and Human Development, School of Medicine, Box 3003, Room 1506, Busse Bldg, Duke University, Durham, NC 27710 (zengyi@nsd.pku.edu.cn) and Junxia Min, the First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China, 310058 (junxiamin@zju.edu.cn).

    Author Contributions: Dr Zeng, Mr Nie, and Dr Min had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Zeng, Mr Nie, and Dr Min contributed equally to the work. Dr Zeng and Mr Nie are co–first authors. Dr Zeng, Dr H. Chen, Ms Xiaomin Liu, Mr Ye, and Mr Z. Chen contributed equally in statistical analyses of the data.

    Concept and design: Zeng, Nie, Min, H. Chen, Ye, Z. Chen, Bai, Lv, Lu, Li, Bolund, Land, Yashin, Z. Yang, Gu, Xiao Liu, Xu, Tian, H. Yang, Vaupel.

    Acquisition, analysis, or interpretation of data: Zeng, Nie, Min, H. Chen, Xiaomin Liu, Ye, Z. Chen, Bai, E. Xie, Yin, Lv, Ni, Yashin, O'Rand, Sun, Z. Yang, Tao, Gurinovich, Franceschi, J. Xie, Hou, Robine, Deelen, Sebastiani, Slagboom, Perls, Hauser, Gottschalk, Tan, Christensen, Shi, Lutz.

    Drafting of the manuscript: Zeng, Nie, Min, H. Chen, Xiaomin Liu, Ye, Bai, Lu, Li, J. Xie, Sebastiani, Z. Yang.

    Critical revision of the manuscript for important intellectual content: Zeng, Nie, Min, H. Chen, Xiaomin Liu, Ye, Z. Chen, Bai, E. Xie, Yin, Lv, Ni, Bolund, Land, Yashin, O'Rand, Sun, Z. Yang, Tao, Gurinovich, Franceschi, J. Xie, Gu, Hou, Xiao Liu, Xu, Robine, Deelen, Sebastiani, Slagboom, Perls, Hauser, Gottschalk, Tan, Christensen, Shi, Lutz, Tian, H. Yang, Vaupel.

    Statistical analysis: Zeng, Nie, H. Chen, Xiaomin Liu, Ye, Z. Chen, Bai, E. Xie, Li, Land, Gurinovich, J. Xie, Deelen, Sebastiani, Tan, Shi, Lutz, Vaupel.

    Obtained funding: Zeng, Ni, Bolund, Z. Yang, Gu, Xu, Slagboom, Perls, Vaupel.

    Administrative, technical, or material support: Zeng, Nie, H. Chen, Xiaomin Liu, Z. Chen, Yin, Lv, O'Rand, Z. Yang, Hou, Xiao Liu, Xu, Shi.

    Supervision: Zeng, Nie, Min, Sun, Franceschi, Xiao Liu, Xu, Slagboom, H. Yang.

    Conflict of Interest Disclosures: Dr Bai reported grants from the Natural Science Foundation of China during the conduct of the study. Dr Ni reported grants from the National Basic Research Program of China during the conduct of the study. Dr Slagboom reported grants from government during the conduct of the study. Dr Gottschalk reported serving as a consultant for Zinfandel Pharmaceuticals Inc outside the submitted work and was supported by National Institute on Aging grant RO1 AG040370. No other disclosures were reported.

    Funding/Support: This study is supported by the National Natural Science Foundation of China (71490732; Dr Zeng), the US National Institute on Aging/National Institutes of Health (2P01AG031719, Dr Vaupel, Dr Christensen, Dr Zeng; P30AG028716, Dr Hauser; U19AG023122, Dr Perls, Dr Sebastiani; P30AG034424, Dr O’Rand), and the European Union 7th Framework Program (259679, Dr Slagboom).

    Role of the Funder/Sponsor: The organizations and sponsors who provided funding for this study had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: We are grateful to Yuzhi Liu, BA, Chunyuan Zhang, BA, and Yun Zhou, PhD, from Peking University; Zhenyu Xiao, BA, Liqun Tao, BA, Qin Xu, BA, and Ye Yuan, BA, from the Chinese Center for Aging Science Research; and Zhenzhen Zheng, PhD, and Jie Zhan, BA, from the China Social Science Academy for their contributions to the Chinese Longitudinal Healthy Longevity Surveys and the DNA sample collections; we thank all interviewees and their families for their voluntary participation in the CLHLS study. We thank Xiaopan Liu, BA, Qiuyan Liao, BA, Jingzhi Chen, BA, Hongcheng Zhou, BA, and Mingrui Li, BA, from the China National GeneBank-Shenzhen for their contributions of sample storage, DNA extractions, and genotyping. We are grateful for the thoughtful comments provided by Jessica Sautter, PhD, from the University of Sciences; Lene Christiansen, PhD, from the University of Southern Denmark; Joseph H. Lee, DrPH, from Columbia University; Danyu Lin, PhD, from the University of North Carolina; Lei Feng, PhD, and Qiushi Feng, PhD, from the National University of Singapore; Lingguo Cheng, PhD, from Nanjing University; Muqi Guo, MA, from Harvard University; and Hanmo Yang, MA, from Peking University. We are also grateful to all of the members from the European Union Longevity Genetics Consortium (including Genetics for Healthy Aging project consortium) for their contributions to the European Union longevity GWAS data set that was used for the comparative analysis across Chinese, US, and European populations in this article. None of these individuals were compensated for their contributions.

    References
    1.
    Robine  JM, Saito  Y, Jagger  C.  The relationship between longevity and healthy life expectancy.  Qual Ageing Older Adults. 2009;10(2):5-14. doi:10.1108/14717794200900012Google ScholarCrossref
    2.
    Sebastiani  P, Solovieff  N, Dewan  AT,  et al.  Genetic signatures of exceptional longevity in humans.  PLoS One. 2012;7(1):e29848. doi:10.1371/journal.pone.0029848PubMedGoogle ScholarCrossref
    3.
    Deelen  J, Beekman  M, Uh  HW,  et al.  Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age.  Hum Mol Genet. 2014;23(16):4420-4432. doi:10.1093/hmg/ddu139PubMedGoogle ScholarCrossref
    4.
    Newman  AB, Walter  S, Lunetta  KL,  et al.  A meta-analysis of four genome-wide association studies of survival to age 90 years or older: the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.  J Gerontol A Biol Sci Med Sci. 2010;65(5):478-487. doi:10.1093/gerona/glq028PubMedGoogle ScholarCrossref
    5.
    Zeng  Y, Nie  C, Min  J,  et al.  Novel loci and pathways significantly associated with longevity.  Sci Rep. 2016;6:21243. doi:10.1038/srep21243PubMedGoogle ScholarCrossref
    6.
    Zeng  Y, Cheng  L, Chen  H,  et al.  Effects of FOXO genotypes on longevity: a biodemographic analysis.  J Gerontol A Biol Sci Med Sci. 2010;65(12):1285-1299. doi:10.1093/gerona/glq156PubMedGoogle ScholarCrossref
    7.
    Hughes  T, Adler  A, Merrill  JT,  et al; BIOLUPUS Network.  Analysis of autosomal genes reveals gene-sex interactions and higher total genetic risk in men with systemic lupus erythematosus.  Ann Rheum Dis. 2012;71(5):694-699. doi:10.1136/annrheumdis-2011-200385PubMedGoogle ScholarCrossref
    8.
    Gilks  WP, Abbott  JK, Morrow  EH.  Sex differences in disease genetics: evidence, evolution, and detection.  Trends Genet. 2014;30(10):453-463. doi:10.1016/j.tig.2014.08.006PubMedGoogle ScholarCrossref
    9.
    Mielke  MM, Vemuri  P, Rocca  WA.  Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences.  Clin Epidemiol. 2014;6:37-48. doi:10.2147/CLEP.S37929PubMedGoogle ScholarCrossref
    10.
    Pilling  LC, Kuo  CL, Sicinski  K,  et al.  Human longevity: 25 genetic loci associated in 389,166 UK biobank participants.  Aging (Albany NY). 2017;9(12):2504-2520.PubMedGoogle ScholarCrossref
    11.
    Cohen  J, Cohen  P, West  SG, Aiken  LS.  Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3rd ed. Routledge, UK: Abingdon-on-Thames Press; 2013.
    12.
    Little  J, Higgins  JP, Ioannidis  JP,  et al.  Strengthening the Reporting of Genetic Association Studies (STREGA)—an extension of the STROBE statement.  Eur J Clin Invest. 2009;39(4):247-266. doi:10.1111/j.1365-2362.2009.02125.xPubMedGoogle ScholarCrossref
    13.
    Xu  S, Yin  X, Li  S,  et al.  Genomic dissection of population substructure of Han Chinese and its implication in association studies.  Am J Hum Genet. 2009;85(6):762-774. doi:10.1016/j.ajhg.2009.10.015PubMedGoogle ScholarCrossref
    14.
    Jia  P, Wang  L, Fanous  AH, Pato  CN, Edwards  TL, Zhao  Z; International Schizophrenia Consortium.  Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia.  PLoS Comput Biol. 2012;8(7):e1002587. doi:10.1371/journal.pcbi.1002587PubMedGoogle ScholarCrossref
    15.
    Beecham  AH, Patsopoulos  NA, Xifara  DK,  et al; International Multiple Sclerosis Genetics Consortium (IMSGC); Wellcome Trust Case Control Consortium 2 (WTCCC2); International IBD Genetics Consortium (IIBDGC).  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.  Nat Genet. 2013;45(11):1353-1360. doi:10.1038/ng.2770PubMedGoogle ScholarCrossref
    16.
    Purcell  S, Neale  B, Todd-Brown  K,  et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses.  Am J Hum Genet. 2007;81(3):559-575. doi:10.1086/519795PubMedGoogle ScholarCrossref
    17.
    Price  AL, Butler  J, Patterson  N,  et al.  Discerning the ancestry of European Americans in genetic association studies.  PLoS Genet. 2008;4(1):e236. doi:10.1371/journal.pgen.0030236PubMedGoogle ScholarCrossref
    18.
    Euesden  J, Lewis  CM, O’Reilly  PF.  PRSice: polygenic risk score software.  Bioinformatics. 2015;31(9):1466-1468. doi:10.1093/bioinformatics/btu848PubMedGoogle ScholarCrossref
    19.
    Zhang  K, Cui  S, Chang  S, Zhang  L, Wang  J.  i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study.  Nucleic Acids Res. 2010;38(web server issue)(suppl 2):W90-W95. doi:10.1093/nar/gkq324PubMedGoogle ScholarCrossref
    20.
    Purcell  SM, Wray  NR, Stone  JL,  et al; International Schizophrenia Consortium.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder.  Nature. 2009;460(7256):748-752.PubMedGoogle Scholar
    21.
    Natarajan  K, Xie  Y, Baer  MR, Ross  DD.  Role of breast cancer resistance protein (BCRP/ABCG2) in cancer drug resistance.  Biochem Pharmacol. 2012;83(8):1084-1103. doi:10.1016/j.bcp.2012.01.002PubMedGoogle ScholarCrossref
    22.
    Moruzzi  S, Guarini  P, Udali  S,  et al.  One-carbon genetic variants and the role of MTHFD1 1958G>A in liver and colon cancer risk according to global DNA methylation.  PLoS One. 2017;12(10):e0185792. doi:10.1371/journal.pone.0185792PubMedGoogle ScholarCrossref
    23.
    Fillmore  CM, Gupta  PB, Rudnick  JA,  et al.  Estrogen expands breast cancer stem-like cells through paracrine FGF/Tbx3 signaling.  Proc Natl Acad Sci U S A. 2010;107(50):21737-21742. doi:10.1073/pnas.1007863107PubMedGoogle ScholarCrossref
    24.
    Klein  SL, Jedlicka  A, Pekosz  A.  The Xs and Y of immune responses to viral vaccines.  Lancet Infect Dis. 2010;10(5):338-349. doi:10.1016/S1473-3099(10)70049-9PubMedGoogle ScholarCrossref
    25.
    Goetzl  EJ, Huang  MC, Kon  J,  et al.  Gender specificity of altered human immune cytokine profiles in aging.  FASEB J. 2010;24(9):3580-3589. doi:10.1096/fj.10-160911PubMedGoogle ScholarCrossref
    26.
    Hewagama  A, Patel  D, Yarlagadda  S, Strickland  FM, Richardson  BC.  Stronger inflammatory/cytotoxic T-cell response in women identified by microarray analysis.  Genes Immun. 2009;10(5):509-516. doi:10.1038/gene.2009.12PubMedGoogle ScholarCrossref
    27.
    Marttila  S, Jylhävä  J, Nevalainen  T,  et al.  Transcriptional analysis reveals gender-specific changes in the aging of the human immune system.  PLoS One. 2013;8(6):e66229. doi:10.1371/journal.pone.0066229PubMedGoogle ScholarCrossref
    28.
    Bonafè  M, Olivieri  F, Cavallone  L,  et al.  A gender-dependent genetic predisposition to produce high levels of IL-6 is detrimental for longevity.  Eur J Immunol. 2001;31(8):2357-2361. doi:10.1002/1521-4141(200108)31:8<2357::AID-IMMU2357>3.0.CO;2-XPubMedGoogle ScholarCrossref
    29.
    Agrawal  A, Agrawal  S, Cao  JN, Su  H, Osann  K, Gupta  S.  Altered innate immune functioning of dendritic cells in elderly humans: a role of phosphoinositide 3-kinase-signaling pathway.  J Immunol. 2007;178(11):6912-6922. doi:10.4049/jimmunol.178.11.6912PubMedGoogle ScholarCrossref
    30.
    Melkamu  T, Kita  H, O’Grady  SM.  TLR3 activation evokes IL-6 secretion, autocrine regulation of Stat3 signaling and TLR2 expression in human bronchial epithelial cells.  J Cell Commun Signal. 2013;7(2):109-118. doi:10.1007/s12079-012-0185-zPubMedGoogle ScholarCrossref
    31.
    Varthaman  A, Moreau  HD, Maurin  M, Benaroch  P.  TLR3-induced maturation of murine dendritic cells regulates CTL responses by modulating PD-L1 trafficking.  PLoS One. 2016;11(12):e0167057. doi:10.1371/journal.pone.0167057PubMedGoogle ScholarCrossref
    32.
    Kong  KF, Delroux  K, Wang  X,  et al.  Dysregulation of TLR3 impairs the innate immune response to West Nile virus in the elderly.  J Virol. 2008;82(15):7613-7623. doi:10.1128/JVI.00618-08PubMedGoogle ScholarCrossref
    33.
    Collino  S, Montoliu  I, Martin  FPJ,  et al.  Metabolic signatures of extreme longevity in northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism.  PLoS One. 2013;8(3):e56564. doi:10.1371/journal.pone.0056564PubMedGoogle ScholarCrossref
    34.
    McGaha  TL, Huang  L, Lemos  H,  et al.  Amino acid catabolism: a pivotal regulator of innate and adaptive immunity.  Immunol Rev. 2012;249(1):135-157. doi:10.1111/j.1600-065X.2012.01149.xPubMedGoogle ScholarCrossref
    35.
    Colegio  OR, Chu  NQ, Szabo  AL,  et al.  Functional polarization of tumour-associated macrophages by tumour-derived lactic acid.  Nature. 2014;513(7519):559-563. doi:10.1038/nature13490PubMedGoogle ScholarCrossref
    36.
    Cantó  C, Gerhart-Hines  Z, Feige  JN,  et al.  AMPK regulates energy expenditure by modulating NAD+ metabolism and SIRT1 activity.  Nature. 2009;458(7241):1056-1060. doi:10.1038/nature07813PubMedGoogle ScholarCrossref
    37.
    Gomes  AP, Price  NL, Ling  AJY,  et al.  Declining NAD(+) induces a pseudohypoxic state disrupting nuclear-mitochondrial communication during aging.  Cell. 2013;155(7):1624-1638. doi:10.1016/j.cell.2013.11.037PubMedGoogle ScholarCrossref
    38.
    Dudbridge  F.  Power and predictive accuracy of polygenic risk scores.  PLoS Genet. 2013;9(3):e1003348. doi:10.1371/journal.pgen.1003348PubMedGoogle ScholarCrossref
    39.
    National Bureau of Statistics of China.  Population Census Office Under the State Council and Department of Population, Social, Science and Technology Statistics, National Bureau of Statistics of the People’s Republic of China. Tabulations of the 2000 Census of China. Beijing, China: China Statistics Press; 2002.
    40.
    Broer  L, Buchman  AS, Deelen  J,  et al.  GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy.  J Gerontol A Biol Sci Med Sci. 2015;70(1):110-118. doi:10.1093/gerona/glu166PubMedGoogle ScholarCrossref
    41.
    Ober  C, Loisel  DA, Gilad  Y.  Sex-specific genetic architecture of human disease.  Nat Rev Genet. 2008;9(12):911-922. doi:10.1038/nrg2415PubMedGoogle ScholarCrossref
    42.
    Moon  H, Lopez  KL, Lin  GI, Chen  JJ.  Sex-specific genomic biomarkers for individualized treatment of life-threatening diseases.  Dis Markers. 2013;35(6):661-667. doi:10.1155/2013/393020PubMedGoogle ScholarCrossref
    43.
    Kajinami  K, Brousseau  ME, Ordovas  JM, Schaefer  EJ.  Polymorphisms in the multidrug resistance-1 (MDR1) gene influence the response to atorvastatin treatment in a gender-specific manner.  Am J Cardiol. 2004;93(8):1046-1050. doi:10.1016/j.amjcard.2004.01.014PubMedGoogle ScholarCrossref
    44.
    Zhang  W, Press  OA, Haiman  CA,  et al.  Association of methylenetetrahydrofolate reductase gene polymorphisms and sex-specific survival in patients with metastatic colon cancer.  J Clin Oncol. 2007;25(24):3726-3731. doi:10.1200/JCO.2007.11.4710PubMedGoogle ScholarCrossref
    45.
    Jameson  JL,  et al.  Precision medicine—personalized, problematic, and promising.  Obstet Gynecol Surv. 2015;70(10):612-614. doi:10.1097/01.ogx.0000472121.21647.38PubMedGoogle ScholarCrossref
    ×