The impact of sex on gene expression across human tissues

doi:10.1126/science.aba3066

. 2020 Sep 11;369(6509):eaba3066.

doi: 10.1126/science.aba3066.

The impact of sex on gene expression across human tissues

Meritxell Oliva^#^{1

2

3}, Manuel Muñoz-Aguirre^#^{4

5}, Sarah Kim-Hellmuth^#^{6

7

8}, Valentin Wucher⁴, Ariel D H Gewirtz⁹, Daniel J Cotter¹⁰, Princy Parsana¹¹, Silva Kasela^{7

8}, Brunilda Balliu¹², Ana Viñuela¹³, Stephane E Castel^{7

8}, Pejman Mohammadi¹⁴, François Aguet¹⁵, Yuxin Zou¹⁶, Ekaterina A Khramtsova^{17

18}, Andrew D Skol^{17

2

19

20}, Diego Garrido-Martín⁴, Ferran Reverter²¹, Andrew Brown²², Patrick Evans²³, Eric R Gamazon^{23

24}, Anthony Payne²⁵, Rodrigo Bonazzola¹⁷, Alvaro N Barbeira¹⁷, Andrew R Hamel^{15

26}, Angel Martinez-Perez²⁷, José Manuel Soria²⁷; GTEx Consortium; Brandon L Pierce³, Matthew Stephens^{16

28}, Eleazar Eskin²⁹, Emmanouil T Dermitzakis¹³, Ayellet V Segrè^{15

26}, Hae Kyung Im¹⁷, Barbara E Engelhardt^{30

31}, Kristin G Ardlie¹⁵, Stephen B Montgomery^{10

32}, Alexis J Battle^{11

33}, Tuuli Lappalainen^{7

8}, Roderic Guigó^{4

34}, Barbara E Stranger^{1

2

19

35}

Collaborators, Affiliations

Collaborators

GTEx Consortium:
François Aguet, Shankara Anand, Kristin G Ardlie, Stacey Gabriel, Gad A Getz, Aaron Graubert, Kane Hadley, Robert E Handsaker, Katherine H Huang, Seva Kashin, Xiao Li, Daniel G MacArthur, Samuel R Meier, Jared L Nedzel, Duyen T Nguyen, Ayellet V Segrè, Ellen Todres, Brunilda Balliu, Alvaro N Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D Brown, Stephane E Castel, Donald F Conrad, Daniel J Cotter, Nancy Cox, Sayantan Das, Olivia M de Goede, Emmanouil T Dermitzakis, Jonah Einson, Barbara E Engelhardt, Eleazar Eskin, Tiffany Y Eulalio, Nicole M Ferraro, Elise D Flynn, Laure Fresard, Eric R Gamazon, Diego Garrido-Martín, Nicole R Gay, Michael J Gloudemans, Roderic Guigó, Andrew R Hame, Yuan He, Paul J Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B Montgomery, Manuel Muñoz-Aguirre, Daniel C Nachun, Andrew B Nobel, Meritxell Oliva, YoSon Park, Yongjin Park, Princy Parsana, Abhiram S Rao, Ferran Reverter, John M Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E Stranger, Benjamin J Strober, Nicole A Teran, Ana Viñuela, Gao Wang, Xiaoquan Wen, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Mary E Barcus, Debra Bradbury, Tanya Krubit, Jeffrey A McLean, Liqun Qi, Karna Robinson, Nancy V Roche, Anna M Smith, Leslie Sobin, David E Tabor, Anita Undale, Jason Bridge, Lori E Brigham, Barbara A Foster, Bryan M Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F Leinweber, Alisa McDonald, Michael T Moser, Kevin Myer, Kimberley D Ramsey, Brian Roe, Saboor Shad, Jeffrey A Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D Jewell, Daniel C Rohrer, Dana R Valley, David A Davis, Deborah C Mash, Philip A Branton, Laura K Barker, Heather M Gardiner, Maghboeba Mosavel, Laura A Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W James Kent, Christopher M Lee, Conner C Powell, Kate R Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J Trevanion, Daniel R Zerbino, Nathan S Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A Doherty, Andrew P Feinberg, Kasper D Hansen, Peter F Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E Linder, Brandon L Pierce, Lindsay F Rizzardi, Andrew D Skol, Kevin S Smith, Michael Snyder, John Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J Carithers, Ping Guan, Susan E Koester, A Roger Little, Helen M Moore, Concepcion R Nierras, Abhi K Rao, Jimmie B Vaught, Simona Volpi

Affiliations

¹ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA. meritxellop@uchicago.edu barbara.stranger@northwestern.edu.
² Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA.
³ Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
⁴ Centre for Genomic Regulation, Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.
⁵ Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain.
⁶ Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
⁷ New York Genome Center, New York, NY, USA.
⁸ Department of Systems Biology, Columbia University, New York, NY, USA.
⁹ Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
¹⁰ Department of Genetics, Stanford University, Stanford, CA, USA.
¹¹ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
¹² Department of Computational Medicine, University of California, Los Angeles, CA, USA.
¹³ Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
¹⁴ Department of Integrative Structural and Computational Biology, The Scripps Research Institute, Scripps Research Translational Institute, La Jolla, CA, USA.
¹⁵ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁶ Department of Statistics, University of Chicago, Chicago, IL, USA.
¹⁷ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
¹⁸ Computational Sciences, Janssen Pharmaceuticals, Spring House, PA, USA.
¹⁹ Center for Translational Data Science, University of Chicago, Chicago, IL, USA.
²⁰ Department of Pathology and Laboratory Medicine, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
²¹ Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain.
²² University of Dundee, Dundee, Scotland, UK.
²³ Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
²⁴ Clare Hall, University of Cambridge, Cambridge, UK.
²⁵ Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
²⁶ Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
²⁷ Genomics of Complex Diseases Group, Research Institute Hospital de la Sant Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain.
²⁸ Department of Human Genetics, University of Chicago, Chicago, IL, USA.
²⁹ Departments of Computational Medicine, Computer Science, and Human Genetics, University of California, Los Angeles, CA, USA.
³⁰ Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA.
³¹ Genomics plc, Oxford, UK.
³² Department of Pathology, Stanford University, Stanford, CA, USA.
³³ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
³⁴ Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
³⁵ Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Chicago, IL, USA.

^# Contributed equally.

PMID: 32913072
PMCID: PMC8136152
DOI: 10.1126/science.aba3066

The impact of sex on gene expression across human tissues

Meritxell Oliva et al. Science. 2020.

. 2020 Sep 11;369(6509):eaba3066.

doi: 10.1126/science.aba3066.

Authors

Collaborators

GTEx Consortium:
François Aguet, Shankara Anand, Kristin G Ardlie, Stacey Gabriel, Gad A Getz, Aaron Graubert, Kane Hadley, Robert E Handsaker, Katherine H Huang, Seva Kashin, Xiao Li, Daniel G MacArthur, Samuel R Meier, Jared L Nedzel, Duyen T Nguyen, Ayellet V Segrè, Ellen Todres, Brunilda Balliu, Alvaro N Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D Brown, Stephane E Castel, Donald F Conrad, Daniel J Cotter, Nancy Cox, Sayantan Das, Olivia M de Goede, Emmanouil T Dermitzakis, Jonah Einson, Barbara E Engelhardt, Eleazar Eskin, Tiffany Y Eulalio, Nicole M Ferraro, Elise D Flynn, Laure Fresard, Eric R Gamazon, Diego Garrido-Martín, Nicole R Gay, Michael J Gloudemans, Roderic Guigó, Andrew R Hame, Yuan He, Paul J Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B Montgomery, Manuel Muñoz-Aguirre, Daniel C Nachun, Andrew B Nobel, Meritxell Oliva, YoSon Park, Yongjin Park, Princy Parsana, Abhiram S Rao, Ferran Reverter, John M Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E Stranger, Benjamin J Strober, Nicole A Teran, Ana Viñuela, Gao Wang, Xiaoquan Wen, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Mary E Barcus, Debra Bradbury, Tanya Krubit, Jeffrey A McLean, Liqun Qi, Karna Robinson, Nancy V Roche, Anna M Smith, Leslie Sobin, David E Tabor, Anita Undale, Jason Bridge, Lori E Brigham, Barbara A Foster, Bryan M Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F Leinweber, Alisa McDonald, Michael T Moser, Kevin Myer, Kimberley D Ramsey, Brian Roe, Saboor Shad, Jeffrey A Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D Jewell, Daniel C Rohrer, Dana R Valley, David A Davis, Deborah C Mash, Philip A Branton, Laura K Barker, Heather M Gardiner, Maghboeba Mosavel, Laura A Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W James Kent, Christopher M Lee, Conner C Powell, Kate R Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J Trevanion, Daniel R Zerbino, Nathan S Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A Doherty, Andrew P Feinberg, Kasper D Hansen, Peter F Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E Linder, Brandon L Pierce, Lindsay F Rizzardi, Andrew D Skol, Kevin S Smith, Michael Snyder, John Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J Carithers, Ping Guan, Susan E Koester, A Roger Little, Helen M Moore, Concepcion R Nierras, Abhi K Rao, Jimmie B Vaught, Simona Volpi

Affiliations

¹ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA. meritxellop@uchicago.edu barbara.stranger@northwestern.edu.
² Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA.
³ Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
⁴ Centre for Genomic Regulation, Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.
⁵ Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain.
⁶ Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
⁷ New York Genome Center, New York, NY, USA.
⁸ Department of Systems Biology, Columbia University, New York, NY, USA.
⁹ Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
¹⁰ Department of Genetics, Stanford University, Stanford, CA, USA.
¹¹ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
¹² Department of Computational Medicine, University of California, Los Angeles, CA, USA.
¹³ Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
¹⁴ Department of Integrative Structural and Computational Biology, The Scripps Research Institute, Scripps Research Translational Institute, La Jolla, CA, USA.
¹⁵ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁶ Department of Statistics, University of Chicago, Chicago, IL, USA.
¹⁷ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
¹⁸ Computational Sciences, Janssen Pharmaceuticals, Spring House, PA, USA.
¹⁹ Center for Translational Data Science, University of Chicago, Chicago, IL, USA.
²⁰ Department of Pathology and Laboratory Medicine, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
²¹ Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain.
²² University of Dundee, Dundee, Scotland, UK.
²³ Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
²⁴ Clare Hall, University of Cambridge, Cambridge, UK.
²⁵ Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
²⁶ Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
²⁷ Genomics of Complex Diseases Group, Research Institute Hospital de la Sant Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain.
²⁸ Department of Human Genetics, University of Chicago, Chicago, IL, USA.
²⁹ Departments of Computational Medicine, Computer Science, and Human Genetics, University of California, Los Angeles, CA, USA.
³⁰ Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA.
³¹ Genomics plc, Oxford, UK.
³² Department of Pathology, Stanford University, Stanford, CA, USA.
³³ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
³⁴ Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
³⁵ Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Chicago, IL, USA.

^# Contributed equally.

PMID: 32913072
PMCID: PMC8136152
DOI: 10.1126/science.aba3066

Abstract

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.

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Figures

**Fig. 1.. Sample, data types, and discovery sets in the study of sex differences in GTEx v8.**
Tissue types (including 11 distinct brain regions and two cell lines) are illustrated, with sample numbers from GTEx v8 genotyped donors (females:males, in parentheses) and color coding indicated for each. This study included N = 44 tissue sources present in both sexes with ≥70 samples. Tissue sources comprised two cell lines, 40 tissues, and two additional replicates for brain cerebellum and cortex tissues. Tissue name abbreviations are shown in bold. See (9) for specific numbers of donors used in each analysis.

**Fig. 2.. Sex-differential gene expression.**
(A) Number of sex-differentially expressed genes (sex-biased genes) per tissue. Tissue colors are as in Fig. 1. (B) Sex-biased gene discovery (histogram, number of sex-biased genes) and characteristics of sex-biased genes (stacked bar plots) as a function of tissue sharing. Proportions of X-linked and autosomal sex-biased genes (Chr.) and of female- and male-biased genes (Sign) are indicated. (C) Hierarchical clustering of tissues based on gene expression (left) and the effect size of sex-biased genes (right). See (9) for further details.

**Fig. 3.. Regulatory mechanisms and biological functions of sex-biased genes.**
(A) Genomic position enrichment of sex-biased genes, as indicated by male-biased (blue) and female-biased (red) genes across all chromosomes (left) and chromosome X (right). The height of each rim represents the tissue sharing of the significant genomic enrichment signal and ranges from 1 to 44 (number of tissue sources). See (9) for further details. (B) Transcription factor binding site (TFBS) enrichment in promoter regions of sex-biased genes. Of 92 enriched TFBS profiles, the top 40 with the largest difference across all tissues in the enrichment profile derived from male-biased and female-biased genes are displayed. Values represent the TFBS enrichment ranking transformed to [0, 1] per tissue and per sex; a value of 1 corresponds to the highest enrichment. See (9) for further details. (C) Clusters (gray circles) of gene sets enriched for genes highly expressed (blue and red balloons) in females (red) or males (blue) across tissues. Balloon size corresponds to the P value for the across-tissue meta-analysis of GSEA. Faint lines connecting balloons correspond to shared leading-edge genes between gene sets. See (9) for further details.

**Fig. 4.. Sex-biased eQTLs (sb-eQTLs).**
(A) Number of sb-eQTLs discovered per tissue. Square-root transformation was applied to the x axis. See Fig. 1 for tissue abbreviations. (B) Association P values of the female-stratified (top) and male-stratified (bottom) cis-eQTLs in the *ADRA1A* locus in adipose subcutaneous tissue (upper panels; β_F = −0.78, P_F = 4.64 × 10⁻¹⁸, β_M = −0.47, P_M = 3.98 × 10⁻¹⁰, P_G×Sex = 1.05 × 10⁻⁵) and *C4BPB* locus in breast mammary tissue (lower panels; β_F = 0.40, P_F = 2.68 × 10⁻⁷, β_M = −0.02, P_M = 0.89, P_G×Sex = 7.22 × 10^–5). Linkage disequilibrium between loci is quantified by squared Pearson coefficient of correlation (r²). Diamond-shaped point represents the top significant eQTL variant across sex-stratified P values. (C) sb-eQTL mediation analysis of 261 breast sb-eQTLs. Point coordinates represent the effect size of G×Sex (x axis) and G×Epithelial cells (y axis) derived from a linear regression model with both interaction terms. Gray lines represent confidence intervals of the effect sizes of G×Sex (horizontal lines) and G×Epithelial cells (vertical lines). Point size represents sb-eQTL significance; color corresponds to mediation significance. See (9) for further details.

**Fig. 5.. Colocalization of sb-eQTLs with GWAS traits.**
(A) Posterior probability (PP4) of 74 colocalized gene-trait pairs where a GWAS shows evidence of colocalization with the female-stratified and/or male-stratified cis-eQTL signal (PP4 > 0.5). Numbers of colocalizing loci per tissue are shown in parentheses. (B) Numbers of colocalizing loci for female and male cis-eQTLs. (C) GWAS-eQTL colocalizing genes (PP4 > 0.5) color-labeled by eQTL tissue of origin according to labels in (A) (x axis) are categorized by the sex where the colocalization signal is maximized with the corresponding GWAS trait (y axis). Comparing the colocalization PP4 values for male and female cis-eQTL signals, the estimates can be maximum in females (red) or males (blue). (D) Genotype-phenotype association P values of the *CCDC88C* (left) and *HKDC1* (right) loci. For the *CCDC88C* locus, panels illustrate GWAS signal for breast cancer (top) and *CCDC88C* cis-eQTL signal for females (middle) and males (bottom) in breast mammary tissue. For the *HKDC1* locus, panels illustrate GWAS signal for birth weight (top) and *HKDC1* cis-eQTL signal for females (middle) and males (bottom) in liver. (E) Genotype-phenotype association P values of the *CLDN7* (left) and *DPYSL4* (right) loci. For the *CLDN7* locus, panels illustrate GWAS signal for birth weight (top) and *CLDN7* cis-eQTL signal for females (middle) and males (bottom) in breast mammary tissue. For the *DPYSL4* locus, panels illustrate GWAS signal for body fat (top) and *DPYSL4* cis-eQTL signal for females (middle) and males (bottom) in muscle skeletal tissue. In (D) and (E), linkage disequilibrium between loci is quantified by squared Pearson coefficient of correlation (r²). Diamond-shaped point represents the top significant cis-eQTL variant across sex-stratified P values.

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Comment in

Searching for sex differences.
Wilson MA. Wilson MA. Science. 2020 Sep 11;369(6509):1298-1299. doi: 10.1126/science.abd8340. Science. 2020. PMID: 32913086 No abstract available.
Reaching completion for GTEx.
Burgess DJ. Burgess DJ. Nat Rev Genet. 2020 Dec;21(12):717. doi: 10.1038/s41576-020-00296-7. Nat Rev Genet. 2020. PMID: 33060849 No abstract available.
Ubiquitous sex differences in tissue gene expression: the dawn of a new era for gender medicine.
Liuzzo G, Galiuto L. Liuzzo G, et al. Eur Heart J. 2020 Nov 7;41(42):4090-4091. doi: 10.1093/eurheartj/ehaa877. Eur Heart J. 2020. PMID: 33245328 No abstract available.

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References

1. Zheng D et al. , Sexual dimorphism in the incidence of human cancers. BMC Cancer 19, 684 (2019). doi: 10.1186/s12885-019-5902-z; pmid: 31299933 - DOI - PMC - PubMed
1. Anderson GD, Sex and racial differences in pharmacological response: Where is the evidence? Pharmacogenetics, pharmacokinetics, and pharmacodynamics. J. Womens Health 14, 19–29 (2005). doi: 10.1089/jwh.2005.14.19; pmid: 15692274 - DOI - PubMed
1. Kuan V et al. , A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit. Health 1, e63–e77 (2019). doi: 10.1016/S2589-7500(19)30012-3; pmid: 31650125 - DOI - PMC - PubMed
1. Ngo ST, Steyn FJ, McCombe PA, Gender differences in autoimmune disease. Front. Neuroendocrinol. 35, 347–369 (2014). doi: 10.1016/j.yfrne.2014.04.004; pmid: 24793874 - DOI - PubMed
1. Westergaard D, Moseley P, Sørup FKH, Baldi P, Brunak S, Population-wide analysis of differences in disease progression patterns in men and women. Nat. Commun. 10, 666 (2019). doi: 10.1038/s41467-019-08475-9; pmid: 30737381 - DOI - PMC - PubMed

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[1] Zheng D et al. , Sexual dimorphism in the incidence of human cancers. BMC Cancer 19, 684 (2019). doi: 10.1186/s12885-019-5902-z; pmid: 31299933 - DOI - PMC - PubMed

[2] Zheng D et al. , Sexual dimorphism in the incidence of human cancers. BMC Cancer 19, 684 (2019). doi: 10.1186/s12885-019-5902-z; pmid: 31299933 - DOI - PMC - PubMed

[3] Anderson GD, Sex and racial differences in pharmacological response: Where is the evidence? Pharmacogenetics, pharmacokinetics, and pharmacodynamics. J. Womens Health 14, 19–29 (2005). doi: 10.1089/jwh.2005.14.19; pmid: 15692274 - DOI - PubMed

[4] Anderson GD, Sex and racial differences in pharmacological response: Where is the evidence? Pharmacogenetics, pharmacokinetics, and pharmacodynamics. J. Womens Health 14, 19–29 (2005). doi: 10.1089/jwh.2005.14.19; pmid: 15692274 - DOI - PubMed

[5] Kuan V et al. , A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit. Health 1, e63–e77 (2019). doi: 10.1016/S2589-7500(19)30012-3; pmid: 31650125 - DOI - PMC - PubMed

[6] Kuan V et al. , A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit. Health 1, e63–e77 (2019). doi: 10.1016/S2589-7500(19)30012-3; pmid: 31650125 - DOI - PMC - PubMed

[7] Ngo ST, Steyn FJ, McCombe PA, Gender differences in autoimmune disease. Front. Neuroendocrinol. 35, 347–369 (2014). doi: 10.1016/j.yfrne.2014.04.004; pmid: 24793874 - DOI - PubMed

[8] Ngo ST, Steyn FJ, McCombe PA, Gender differences in autoimmune disease. Front. Neuroendocrinol. 35, 347–369 (2014). doi: 10.1016/j.yfrne.2014.04.004; pmid: 24793874 - DOI - PubMed

[9] Westergaard D, Moseley P, Sørup FKH, Baldi P, Brunak S, Population-wide analysis of differences in disease progression patterns in men and women. Nat. Commun. 10, 666 (2019). doi: 10.1038/s41467-019-08475-9; pmid: 30737381 - DOI - PMC - PubMed

[10] Westergaard D, Moseley P, Sørup FKH, Baldi P, Brunak S, Population-wide analysis of differences in disease progression patterns in men and women. Nat. Commun. 10, 666 (2019). doi: 10.1038/s41467-019-08475-9; pmid: 30737381 - DOI - PMC - PubMed

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The impact of sex on gene expression across human tissues

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The impact of sex on gene expression across human tissues

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