Xia and Nakayama ﻿Journal of Physiological Anthropology(2023) 42:25
https://doi.org/10.1186/s40101-023-00341-4

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open Access

Signatures of adaptation in myopia‑related
genes on the sunlight exposure hypothesis
Tian Xia1 and Kazuhiro Nakayama1*   

Abstract
Background Myopia is a common eye disorder that results from gene-environment interactions. The prevalenceof myopia varies across populations, and exposure to bright sunlight may prevent its development. We hypothesizethat local adaptation to light environments during human migration played a role in shaping the genetic basisof myopia, and we aim to investigate how the environment influences the genetic basis of myopia.
Method We utilized the whole-genome variant data of the 1000 Genomes Project for analysis. We searched myopiaassociated loci that were under selection in Europeans using population branch statistics and the number of segregating sites by length statistics. The outliers of these statistics were enriched in the Kyoto Encyclopedia of Genes
and Genomes pathways and the gene ontology biological process terms in searching for pathways that were
under selection. We applied Bayesian inference to estimate the correlation between environmental factors and allele
frequencies of the selected loci and performed causal inference of myopia using two-sample Mendelian randomization analysis.
Results We detected signatures of adaptation in vision and light perception pathways, supporting our hypothesisof sunlight adaptation. We discovered a strong correlation between latitude and allele frequencies in genes that areunder significant selection, and we found pleiotropic effects of pigmentation or circadian rhythm genes on myopia,
indicating that sunlight exposure influences the genetic diversity of myopia.
Conclusions Myopia genes involved in light perception showed signs of selection. Local adaptation during humanmigration shaped the genetic basis of myopia and may have influenced its global prevalence distribution.
Keywords Myopia, Adaptation, Gene-environment interaction
Introduction
Myopia, or nearsightedness, is widely acknowledged as
the leading cause of distance vision impairment worldwide [1]; since its emergence in the last century, it has
become a significant public health burden [2, 3] and isbelieved to result from gene-environment interactions[4, 5]. Previous studies have demonstrated that spending*Correspondence:
Kazuhiro Nakayama
knakayama@edu.k.u-tokyo.ac.jp
1
Department of Integrated Biosciences, Graduate School of Frontier
Sciences, The University of Tokyo, Kashiwa 277‑8562, Japan

more time outdoors and under bright sunlight can help
prevent the progression [6, 7]. This protective effect inboth human and animal models [8–10] suggests that thegene-environment interactions of sunlight in myopia are
evolutionarily conserved.
Findings in previous genetic studies of myopia [4, 11]suggested that genetic susceptibility to myopia was notdifferent between Europeans and East Asians. In contrast, the prevalence of myopia was significantly higher
in East Asia than in Europe (Supplementary Figure S1)[1, 12–15] despite adequate urbanization and educationlevels. Myopia is considered to be an evolutionary mismatch [16], which was hypothesized to be the result ofgenes that were advantageous in the ancient environment© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Xia and Nakayama ﻿Journal of Physiological Anthropology(2023) 42:25

and became deleterious in the modern environment [17].
Bright sunlight inhibits myopia progression and profoundly influences genetic and phenotypic diversity inhumans [18, 19]. We hypothesized that adaptations todifferent sunlight environments during modern humanmigration diversified the genetic basis of myopia worldwide and subsequently contributed to discrepancies in
the prevalence of myopia. Major large-scale genetic studies of myopia have been European centric, and the annual
sunshine duration varies drastically in Europe, making
Europeans ideal subjects for testing our hypothesis.
This study examined the signature of adaptation tomyopia-related loci in 1000 Genome Project (1KGP)
populations [20], aimed to infer drivers for the differentiation of the genetic basis of myopia. It investigated therole of sunlight exposure in the diversification of myopia
prevalence and deepen the understanding of gene-environment interactions in myopia.

Methods
This study aimed to detect selection signatures in myopia-associated genes within sunlight-related pathways.
Given the highly polygenic nature of myopia, we investigated selective sweeps based on allele frequencies andhaplotypes, as well as polygenic adaptation based on
subtle shifts in allele frequencies within certain pathways. We validated the correlation between the allele frequencies of identified loci and environmental factors andinferred a correlation between sunlight-related exposuresand myopia.
Genotype and summary statistics

The 1KGP phase 3 variant data was downloaded from theInternational Genome Sample Resource (IGSR) (ftp://​ftp.​
1000g​enomes.​ebi.​ac.​uk/​vol1/​ftp/​relea​se/​20130​502/). A
set of summary statistics of a meta-genome-wide association study (GWAS) of myopia [21] was downloaded
from the FTP site of the King’s College London (ftp://​twinr-​f tp.​kcl.​ac.​uk/​Refra​c tive_​Error_​MetaA​naly-- ​sis_​2020). The GWAS summary statistics used in the twosample Mendelian randomization [22] were obtained
from online sources (https://​gwas.​mrcieu.​ac.​uk/) [23].
Population statistics

Quality control (QC) and formatting of the genotype
data were performed using PLINK v.1.9 [24]. The fixationindex (Fst) was calculated using vcftools [25] (https://​vcfto​ols.​github.​io/​man_​latest.​html). Population branch
statistics (PBS) [26] were calculated using pairwise Fst
in three combinations: Finnish in Finland (FIN), Toscani in Italy (TSI), and Yoruba in Ibadan, Nigeria (YRI);
FIN, Bengali in Bangladesh (BEB); and YRI, FIN, Han
Chinese in Beijing, China (CHB), and YRI. nSL [27] wasPage 2 of 8

computed using Selscan 2.0 (https://​github.​com/​szpie​ch/​selsc​an). Statistical analyses were performed using the R
4.3.0 software, implemented in RStudio 2023.03.1 + 446(https://​www.R-​proje​ct.​org/).
PBS selection index and detection of polygenic adaptation

A previously described method [28] was applied to detect
polygenic adaptation by constructing a SNP-based PBS
selection index.
To maximize the inclusion of informative SNPs associated with myopia, we extracted 9927 SNPs from myopia
meta-GWAS summary statistics [21] based on linkage
disequilibrium (LD) r2 < 0.1 in 1KGP Europeans (EUR)
and P < 5.0 × ­10−3 for myopia association. These SNPs
were annotated to 4006 genes using g:profile [29]. An
additional 64 loci, expressed in the retinal layers andassociated with common refractive errors [11, 21], wereadded in cases not captured by the g:profile annotation.
After removing redundancies, 4033 genes were identified.
The PBS selection index was computed for 4033 genes
to measure the likelihood that the mean PBS of randomly
selected SNPs would be greater than that of the observed
SNPs of the same number. To address the skewed genesize distribution, genes were binned by SNP counts at awindow size of 11, which was the mode of all gene sizes.
A permutation test with 100,000 iterations was performed to ensure the robustness of the bin PBS selection
index. These indices were adjusted using the false discovery rate (FDR). The per-gene PBS selection index was
the mean of the corresponding bins, with a PBS selection
index < 0.01 considered significant. This metric was notbiased by gene sizes (linear regression, R 2 =  − 2.4 × ­10−4,
F-statistic P = 0.8586).
A total of 4033 genes were mapped to Gene Ontology
Biological Process (GO BP) terms using QuickGO [30].
GO terms with gene counts below the median (n = 16)
were excluded, resulting in 302 terms. To minimize
redundancy, these terms were clustered using the GOSemSim R package [31] and pruned by cutting a Wardhierarchical clustering tree to a height of 0.5. Overlapping terms were removed by prioritizing gene counts,
resulting in 260 GO terms. A two-sample proportion
test was used to estimate the probability of nonrandom
occurrences of per-gene PBS selection index values < 0.01
for each GO term. Outliers indicate subtle shifts in allele
frequencies among the GO BP terms, implying polygenic
adaptation.
Gene‑environment correlation

Correlations between allele frequency and environmental factors were assessed by Bayenv [32], who described
the likelihood that the outliers of selected allele frequencies relative to a standardized set of allele frequencies

Xia and Nakayama ﻿Journal of Physiological Anthropology(2023) 42:25

were due to the selected environmental effect rather
than by chance.
Two-sample Mendelian randomization (MR) analyses were conducted using the R script at https://​mrcieu.​
github.​io/​TwoSa​mpleMR/​artic​les/​intro​ducti​on.​html.

Results
Signatures of selection in myopia‑associated loci

PBS estimates allelic frequency changes between two
populations by incorporating an outgroup population topolarize frequency shifts among the three branches basedon pairwise Fst. A highly positive PBS suggests the presence of potentially positive selection. In total, 6,404,423
SNPs were derived from the summary statistics of ameta-GWAS conducted on European populations [21]after QC. We retained the ­99th percentile from the three
PBS sets, FIN-TSI-YRI, FIN-BEB-YRI, and FIN-CHB-YRI(Supplementary Figure S2). We filtered their intersection using a threshold of P < 5.0 × ­10−3 for association
with myopia [21] meanwhile including more informative SNPs. This process yielded 535 SNPs, subsequently
annotated to genes using g:Profiler [29]. We identified 80
genes associated with myopia and showed high PBS values (Supplementary Table S1).
nSL is a modified version of the integrated haplotype score (iHS) that employs the count of segregating sites instead of genomic distance for the haplotype
length estimation [27]. This modification enhanced its
resilience to variations in recombination rates. WhennSL value is large (|nSL|≥ 2), it signifies positive selection for an SNP of interest. In total, 435 autosomal
loci of genome-wide significance from a myopia metaGWAS [21] were subjected to nSL filtering. Fourteen
loci, including 53 genes, remained at the top SNP ateach locus |nSL|≥ 2. These were combined with the 80
genes identified by PBS, resulting in 133 myopia-associated genes showing strong signatures in the positive
natural sections (Supplementary Table S1). A genelist enrichment analysis of these genes was performed
using KOBAS-i [33], revealing 19 Kyoto Encyclopediaof Genes and Genomes (KEGG) pathways with corrected P < 0.05 (Table 1). Notably, the KEGG pathway
term “phototransduction” piqued our interest. The
input genes for the enrichment analysis influencing
phototransduction were derived from RHO (rs7984 andrs2855558) and PDE6G/TSPAN10 (rs9747347), and the
expected population differences in allele frequencies
were observed, as summarized in Table 2.
Rhodopsin, encoded by RHO, is responsible for lightdetection in rod photoreceptor cells. Rhodopsin exhibits significant molecular diversity among mammals
based on its ecological niches and behavior [34–36]. The
haplotypes of SNPs near rs2855558 and rs7984 (Fig. 1)

Page 3 of 8

displayed markedly reduced heterogeneity in the 1KGP
Europeans (EUR), mixed Americans (AMR), and South
Asians (SAS), whereas East Asians (EAS) and Africans(AFR) exhibited more diverse haplotypes. The cluster ofhomogeneous haplotypes was predominantly shared by
the EUR populations, suggesting strong selection in these
populations (Fig. 1).
The haplotypes of TSPAN10 exhibited a more diverse
pattern (Fig. 1), and the EAS and AFR clusters differed from the EUR, AMR, and SAS clusters. Notably,rs9747347 showed a significant association with haircolor (P = 2.94 × 10 −21) [38]; meanwhile, hair color demonstrates a strong connection to skin pigmentation due
to the pleiotropic effect [39]. Given its significant association with hair color and myopia and the selection
favoring myopia at this locus (Table 2), we inferred thatthe selective pressure at TSPAN10 may originate from
adaptation events related to the light environment rather
than myopia. Moreover, the two SNPs in RHO did notshow genome-wide significance in the GWAS summary
statistics. Therefore, selective signatures in RHO andTSPAN10 may result from sunlight-related environmental factors, such as pigmentation, rather than selective
pressure on myopia (Supplementary Table S2).
Signatures of polygenic adaptation

Myopia is a complex trait with a highly polygenic architecture [11, 21]. However, methods used for selective
sweeps have limited power to detect polygenic adaptations [40, 41]. Using a previously described approach[28], we calculated the PBS selection index for genomewide myopia-associated genes. Genes with a mean
per-gene PBS selection index of less than 0.01 wereconsidered to have constant allele frequency shiftsthat were not by chance, indicating polygenic selection. These genes were subsequently enriched in GO
BP pathways, and we applied a two-sample proportion
test to search for polygenic allele frequency shift signatures. We highlighted “camera-type eye development”(GO:0043010, P = 2.33 × ­10−3) and “visual perception”(GO:0007601, P = 4.11 × ­10−3) out of 12 significant GO
BP terms (Table 1).
To locate the most significant genes from the identified GO terms, we filtered the genes with a PBS selection
index < 0.01, and minimized myopia association P-values yielded three loci: MED1, ARL6, and RPGRIP1L.
The most significant SNPs in ARL6 and RPGRIP1L wereenriched in PBS to close to zero (Fig. 2), indicating thatallele frequency shifts in these two genes are less likely
to be associated with myopia. Steady allele frequency
shifts were enriched in genome-wide significant SNPs in
MED1 (Fig. 2), which serves as a nuclear receptor coactivator and interacts significantly with RNA polymerase

Xia and Nakayama ﻿Journal of Physiological Anthropology(2023) 42:25

Page 4 of 8

Table 1 Significant terms of gene enrichment analysis
KEGG ID

KEGG term

Enrichment corrected P

hsa05416

Viral myocarditis

5.94E-03

hsa04612

Antigen processing and presentation

7.48E-03

hsa05321

Inflammatory bowel disease (IBD)

3.59E-02

hsa04145

Phagosome

3.59E-02

hsa05150

Staphylococcus aureus infection

3.59E-02

hsa05140

Leishmaniasis

3.59E-02

hsa05164

Influenza A

3.59E-02

hsa05152

Tuberculosis

3.98E-02

hsa04964

Proximal tubule bicarbonate reclamation

3.98E-02

hsa04658

Th1 and Th2 cell differentiation

4.16E-02

hsa05169

Epstein-Barr virus infection

4.19E-02

hsa04744

Phototransduction

4.19E-02

hsa05168

Herpes simplex virus 1 infection

4.19E-02

hsa05310

Asthma

4.19E-02

hsa04659

Th17 cell differentiation

4.19E-02

hsa05145

Toxoplasmosis

4.55E-02

hsa04714

Thermogenesis

4.60E-02

hsa04152

AMPK signaling pathway

4.75E-02

hsa05330

Allograft rejection

4.75E-02

GO term

GO nameTwo-sample proportions test P

GO:0001889

Liver development

6.75E-09

GO:0007420

Brain development

9.68E-06

GO:0050896

Response to stimulus

3.37E-04

GO:0007608

Sensory perception of smell

5.94E-04

GO:0006357

Regulation of transcription by RNA polymerase II

7.74E-04

GO:0006606

Protein import into nucleus

8.83E-04

GO:0060271

Cilium assembly

1.07E-03

GO:0001933

Negative regulation of protein phosphorylation

1.74E-03

GO:0007368

Determination of left/right symmetry

2.33E-03

GO:0043010

Camera-type eye development

2.33E-03

GO:0001701

In utero embryonic development

3.27E-03

GO:0007601

Visual perception

4.11E-03

II, influencing the expression of protein-coding genes[42]. MED1 is associated with mammalian circadianrhythms [43–45].
Gene‑environment interactions

Consistent with rs1426654 (SLC24A5), a marker forskin pigmentation [18], a robust correlation was foundbetween latitude and allele frequencies of RHO across
1KGP populations (Bayes factor = 37.62 ± 2.7; Supplementary Table S2), supporting its interactions with sunlight exposure. The diminished correlation in TSPAN10(Bayes factor = 5.72 ± 0.4; Supplementary Table S2) did
not negate its interaction with sunlight exposure regarding function and pleiotropic effect from skin pigmentation genes. The inflated Bayes factor for temperature andlongitude could result from covariance with latitude. At
the same time, the poor relationship with sunshine duration may be due to confounding factors such as measurement methods.
Our two-sample MR analysis (Supplementary Table S3)
investigated the relationship between sunlight-related
exposure and myopia, as sunlight exposure inhibits the
progression of myopia, and adaptive signatures wereidentified in light-related pathways. The examined exposures included ease of skin tanning, time spent outdoors,and sleep duration. A significant correlation was foundbetween sleep duration and myopia (MR Egger, P = 0.01,
Supplementary Table S3), with the causal inference that
longer sleep leads to a higher risk of myopia. This finding supports the selective signature of MED1. However,Xia and Nakayama ﻿Journal of Physiological Anthropology(2023) 42:25

Table 2 Allele frequencies of highlighted SNPs detected by
selective sweeps
Gene

RHO

RHO

TSPAN10rsid

rs7984

rs2855558

rs9747347

A1/A2

G/A

G/A

T/C

A1 effect size

− 0.01552− 0.01385− 0.03339

GWAS P-val

6.47 × ­10−5

3.14 × ­10−4

2.22 × ­10−50

FIN nSL− 2.58406− 2.41519

2.0204

Phenotypic con‑sequence of theselected allele

Hyperopic

Hyperopic

MyopicA1 frequencies in PBS populations    FIN-TSI-YRI

0.07, 0.23, 0.97 0.07, 0.23, 0.97 0.42, 0.34, 0.07    FIN-BEB-YRI

0.07, 0.47, 0.97 0.07, 0.46, 0.97 0.42, 0.20, 0.07    FIN-CHB-YRI

0.07, 0.58, 0.97 0.07, 0.58, 0.97 0.42, 0.005, 0.07

A1 represents the effect allele, and A2 represents the alternative allele. The A1
effect size (diopters) and P-value were derived from the summary statistics of amyopia genome-wide association study (GWAS) [21]. FIN nSL denotes selection
intensity, and a positive value indicates the extension of haplotypes favoring A1.
Population branch statistics (PBS) were calculated using FIN Finnish in Finland,
TSI Toscani in Italy, YRI Yoruba in Ibadan, Nigeria, BEB Bengali in Bangladesh; andCHB Han Chinese in Beijing, China

a significant pleiotropic effect (P = 0.0184, Supplementary Table S3) between genes of sleep duration and myopia suggested that this causal relationship could be a

Page 5 of 8

false-positive result. Marginally, significant pleiotropy(P = 0.0506, Supplementary Table S3) was observed in
the time spent outdoors in summer, implying a shared
genetic background between myopia and these sunlightrelated traits.

Discussion
School myopia, or simple myopia, is the main form in the
recent epidemic. It is typically characterized by a mean
spherical equivalent ranging from − 0.5 to − 6 diopters.
High myopia, defined as less than − 6 diopters, involves
extreme axial elongations and is distinct from school
myopia in terms of etiology and genetics [4, 21, 46].
Given that school myopia is the major form of myopia
and is largely influenced by environmental factors, ourstudy concentrates on gene-environment interactions,and it should be noted that our findings and implications
are specifically applicable to school myopia.
We identified significant selection signatures in myopia-associated genes related to sunlight, including RHO,TSPAN10, and MED1, and we validated the interaction
between these genes and the light environment. Thesefindings may provide insights into the genetic basis anddiversity of the protective effect of bright sunlight exposure on the risk of myopia incidence.

Fig. 1 Haplotype distribution of a ~ 30-kb window near the SNPs of interest. Each row represents a haplotype, each column represents an SNP,
the black dot indicates a derived allele, and the white dot represents the ancestral allele. The color band on the left indicates the 1KGP population,and the thick group of rows represents a high-frequency haplotype. Left: ~ 30-kb window at RHO, including rs7984 and rs2855558. Right: ~ 30-kb
window at TSPAN10, including rs9747347. EUR, European; EAS, East Asian; AMR, admixed American; SAS, South Asian; AFR, African. The haplotype
structures were generated by haplostrips [37]Xia and Nakayama ﻿Journal of Physiological Anthropology(2023) 42:25

Page 6 of 8

Fig. 2 PBS distribution of representative genes in GO term “camera-type eye development” and “visual perception.” A, B, and C illustrate the physicalposition-dependent distribution of the population branch statistics (PBS) within a 2-kb window upstream and downstream of the ARL6, RPGGRIP1L,
and MED1 coding region, respectively. PBS values were calculated using the FIN, TSI, and YRI populations. Circles and colors represent allele
frequencies and P-values of each SNPs, respectively, in a previous myopia GWAS in Europeans [21]. FIN, Finnish in Finland; TSI, Toscani in Italy; YRI,
Yoruba in Ibadan, Nigeria

We proposed TSPAN10 as evidence of evolutionary
mismatch in myopia. We examined the top 64 myopiaassociated loci that were expressed in the retinal layers and associated with common refractive errors [11,21], SNPs representing TSPAN10, KCNJ5, TFAP2B, andFBN2 had |nSL|> 2, which were under strong selection. In
contrast, selection favoring the risk allele associated withmyopia predisposition was only observed in TSPAN10(Table 2) and FBN2 (P = 8.63 × ­10−11 for rs6860901) [4,21]. Such loci are considered as evidence of evolutionary mismatches. However, a comprehensive explanation or quantitative approach is lacking to address how
these mismatches contribute to the disparity in myopia
prevalence.
Most of the top KEGG pathways enriched in myopiaassociated genes, with evidence for selective sweeps, wererelated to communicable diseases and the immune system (Table 1). These identified pathways may be involvedin the rapid evolution of immune system during the dispersal of northern Europeans [47]. Myopia is driven by
genes participating in the development of all componentsof the eye [21]. The eye is unique in that it possesses
immune privilege [48, 49]; meanwhile, the ocular layer is
exposed to the external environment that has developed
specific strategies to defend against microbial pathogens[50]. Myopia has also been reported to be related to theimmune system, inflammation plays a crucial role in thedevelopment of myopia, and HLA genes were reported
involving genetic diversity of myopia [51, 52]. Natural
selection acting on the immune system, either in the eyeor in other organs, may have contributed to the formation of genetic susceptibility to myopia. On the other
hand, the outliers of the GO terms in the test for polygenic adaptation (Table 1) are consistent with previous
findings that myopia-associated genes are enriched insignal transduction pathways[4, 21]. Meanwhile, PBS

was calculated using FIN-TSI-YRI, which reflects tracesof recent adaptations of Europeans involving diet, pigmentation, immunity, and body morphology (Table 1), as
reported in a previous study [47]. The difference between
the two pathway sets is due to the methods used, PBS andnSL were used to search for selective sweeps in KEGG
pathways, while the PBS selection index was used tosearch for subtle allele frequency shifts in GO terms. Our
results intuitively show that communicable diseases andpathogens are often fatal, while selective pressure fromlifestyle changes is mild and long-lasting.
The prevalence of myopia has surged over the past
century, paralleling the advent of modern lifestyles.
Consequently, this phenotype is unlikely to have been
adaptive in ancient environments. There is consensus
in the literature that biological pathways related to perception play a critical role in the development of myopia. Signatures of natural selection found at RHO andother light perception genes indicate that the evolutionof such pathways contributed to shaping the genetic
background of myopia. Additionally, the selection signature at MED1 and the potential causal relationshipbetween sleep duration and myopia may suggest that
adaptative changes in the circadian rhythm pathways
also participated into this process. The evolution of thegenes in these pathways would be related with adaptation to sunlight environment of northern Europe.
Moreover, the broad pathways identified in the top
selective signatures provide additional evidence that
adaptation of the immune system during the migration to Europe has profoundly and systematically
impacted the genetic background of myopia. However,it remains unclear which phenotypes natural selection
at myopia-related genes favored in ancestral European
populations, and selection patterns may have varied
depending on local environments over time.

Xia and Nakayama ﻿Journal of Physiological Anthropology(2023) 42:25

Our inferences were derived exclusively from GWAS
summary statistics based on European populations.
Comprehensive inference of gene-environment interactions remains challenging owing to insufficient population diversity in Europeans. While we focused mainly onlight-induced pathways, morphological and immunological pathways were more robust among outliers, implying
an underestimated systematic pleiotropic effect on thegenetic basis of myopia.

Conclusion
We reported significant selection signatures enriched inpathways related to vision and light perception in 1KGP
Europeans. These findings suggest that variations in myopia prevalence among populations can be attributed to local
adaptation to the light environment and the relevant pleiotropic effects of other biological pathways, such as immune
function. We infer that the geographic diversity of the genetic
predisposition to myopia is substantial. However, this hypothesis necessitates further corroboration from parallel studies.
Notably, GWAS of myopia with power equivalent to those
conducted in Europeans are conspicuously lacking in otherpopulations, especially in high susceptibility regions such as
East Asia. Our findings contribute to a better understanding of the global disparity in myopia prevalence and provide
insights into the implementation of population-targeted strategies from the perspective of evolutionary medicine.
Abbreviations
1KGP	1000 Genomes Project
GWAS	Genome-wide association studyMR	Mendelian randomization
Fst	Fixation index
PBS	Population branch statistics
FIN	Finnish in Finland
TSI	Toscani in Italy
YRI	Yoruba in Ibadan, Nigeria
BEB	Bengali in Bangladesh
CHB	Han Chinese in Beijing, ChinanSL	Number of segregating sites by length
SNP	Single-nucleotide polymorphism
LD	Linkage disequilibrium
EUR	1KGP Europeans
FDR	False discovery rate
GO BP	Gene Ontology Biological Process
KEGG	Kyoto Encyclopedia of Genes and Genomes
RHO	Rhodopsin
TSPAN10	Tetraspanin 10
EAS	1KGP East Asian
AMR	1KGP admixed AmericanSAS	1KGP South Asian
AFR	1KGP African
MED1	Mediator complex subunit 1
ARL6	ADP ribosylation factor like GTPase 6
RPGRIP1L	Retinitis pigmentosa GTPase regulator interacting protein 1
SLC24A5	Solute carrier family 24 member 5
KCNJ5	Potassium inwardly rectifying channel subfamily J member 5
TFAP2B	Transcription factor AP-2 beta
FBN2	Fibrillin 2
HLA	Human leukocyte antigen

Page 7 of 8

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​023-​00341-4.
Additional file 1: Figure S1. The myopia prevalence distribution. Global
myopia prevalence by region. Myopia prevalence records were extracted
from previous studies1,2, and re-arranged by continents, comprising
175 studies and 64 countries. Figure S2. Population branch statistics(PBS) of myopia-associated SNPs from a myopia GWAS3. (A) PBS of FIN,
TSI, and YRI. (B) PBS of FIN, BEB, and YRI. (C) PBS of FIN, CHB, and YRI. FIN.
Genotype data from 1000 genome project (1KGP)4. FIN: Finnish in Finland;
TSI: Toscani in Italia; BEB: Bengali from Bangladesh; CHB: Han Chinese in
Beijing, China; YRI: Yoruba in Ibadan, Nigeria. Figure S3. Plots of Twosample Mendelian Randomization analysis5 between sleep duration andmyopia. Analysis results see Table S2. (A) Scatter plot representing effects
of exposure (sleep duration) to outcome (myopia). (B) Forest plot, both MR
Egger and IVW showing positive correlation of sleep duration with myopia(binary, OR) that longer sleep duration leads to higher risk of myopia.
(C) Leave-one-out sensitivity analysis, measures whether the thorougheffect was biased by a single SNP of large effect; not all the error bars arelarger than 0 stands for influence from SNPs of large effect sizes. Table S1.
133 gene symbols resulted from KOBAS-i gene-list enrichment analysis.
Table S2. Correlations of allele frequencies and environmental factors inall 26 1000 Genomes Project populations4. Table S3. Two-sample Mendelian randomization analyses in myopia and related factors.
Acknowledgements
Not applicable.
Authors’ contributions
TX and KN designed the research, TX performed research and analyzed data,
and TX and KN wrote the paper.
Funding
This study was partly supported by JSPS KAKENHI Grant number J21411010, a
Grant-in-Aid for a Scientific Research program provided by the Japan Society
for the Promotion of Science.
Availability of data and materials
Not applicable.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 28 August 2023 Accepted: 13 October 2023

References
1. Holden BA, Fricke TR, Wilson DA, Jong M, Naidoo KS, Sankaridurg P, et al.
Global prevalence of myopia and high myopia and temporal trends from2000 through 2050. Ophthalmology. 2016;123:1036–42.
2. Morgan IG, French AN, Ashby RS, Guo X, Ding X, He M, et al. The epidemics
of myopia: aetiology and prevention. Prog Retin Eye Res. 2018;62:134–49.
3. Williams KM, Bertelsen G, Cumberland P, Wolfram C, Verhoeven VJM, Anastasopoulos E, et al. Increasing prevalence of myopia in Europe and the impactof education. Ophthalmology. 2015;122:1489–97.

Xia and Nakayama ﻿Journal of Physiological Anthropology

4.
5.
6.
7.

8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.

22.
23.
24.
25.
26.
27.
28.

(2023) 42:25

Tedja MS, Haarman AEG, Meester-Smoor MA, Kaprio J, Mackey DA, Guggenheim JA, et al. IMI – Myopia genetics report. Invest Ophthalmol Vis Sci.
2019;60:M89–105.
Cai X-B, Shen S-R, Chen D-F, Zhang Q, Jin Z-B. An overview of myopia genetics. Exp Eye Res. 2019;188: 107778.
Xiong S, Sankaridurg P, Naduvilath T, Zang J, Zou H, Zhu J, et al. Time spentin outdoor activities in relation to myopia prevention and control: a metaanalysis and systematic review. Acta Ophthalmol. 2017;95:551–66.
Sherwin JC, Reacher MH, Keogh RH, Khawaja AP, Mackey DA, Foster PJ.
The association between time spent outdoors and myopia in children
and adolescents: a systematic review and meta-analysis. Ophthalmology.
2012;119:2141–51.
Smith EL III, Hung L-F, Huang J. Protective effects of high ambient lighting
on the development of form-deprivation myopia in rhesus monkeys. Invest
Ophthalmol Vis Sci. 2012;53:421–8.
Ashby R, Ohlendorf A, Schaeffel F. The effect of ambient illuminance on thedevelopment of deprivation myopia in chicks. Invest Ophthalmol Vis Sci.
2009;50:5348–54.
Muralidharan AR, Lança C, Biswas S, Barathi VA, Wan Yu Shermaine L, SeangMei S, et al. Light and myopia: from epidemiological studies to neurobiological mechanisms. Ophthalmol Eye Dis. 2021;13:25158414211059246.
Tedja MS, Wojciechowski R, Hysi PG, Eriksson N, Furlotte NA, Verhoeven
VJM, et al. Genome-wide association meta-analysis highlights light-induced
signaling as a driver for refractive error. Nat Genet. 2018;50:834–48.
Hagen LA, Gjelle JVB, Arnegard S, Pedersen HR, Gilson SJ, Baraas RC. Prevalence and possible factors of myopia in Norwegian adolescents. Sci Rep.
2018;8:13479.
Pärssinen O. The increased prevalence of myopia in Finland. Acta Ophthalmol. 2012;90:497–502.
Pärssinen O, Kauppinen M. Associations of near work time, watching TV,
outdoors time, and parents’ myopia with myopia among school children
based on 38-year-old historical data. Acta Ophthalmol. 2022;100:e430–8.
Morgan IG, Ohno-Matsui K, Saw S-M. Myopia. The Lancet. 2012;379:1739–48.
Long E. Evolutionary medicine: why does prevalence of myopia significantly
increase? Evolution, Medicine, and Public Health. 2018;2018:151–2.
Lea AJ, Clark AG, Dahl AW, Devinsky O, Garcia AR, Golden CD, et al. Evolutionary mismatch and the role of GxE interactions in human disease. arXiv.
2023. http://​arxiv.​org/​abs/​2301.​05255. Accessed 26 May 2023.
BasuMallick C, Iliescu FM, Möls M, Hill S, Tamang R, Chaubey G, et al. The
light skin allele of SLC24A5 in South Asians and Europeans shares identity
by descent. PLoS Genet. 2013;9: e1003912.
Rees JS, Castellano S, Andrés AM. The genomics of human local adaptation.
Trends Genet. 2020;36:415–28.
Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature.
2015;526:68–74.
Hysi PG, Choquet H, Khawaja AP, Wojciechowski R, Tedja MS, Yin J, et al.
Meta-analysis of 542,934 subjects of European ancestry identifies new
genes and mechanisms predisposing to refractive error and myopia. Nat
Genet. 2020;52:401–7.
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The
MR-base platform supports systematic causal inference across the humanphenome. Loos R, editor. eLife. 2018;7:e34408.
Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J, et al. The MRC
IEU OpenGWAS data infrastructure. bioRxiv. 2020. https://​www.​biorx​iv.​org/​conte​nt/​10.​1101/​2020.​08.​10.​24429​3v1. Accessed 27 July 2023.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience.
2015;4:s13742–015–0047–8.
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The
variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.
Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZXP, Pool JE, et al. Sequencing of 50
human exomes reveals adaptation to high altitude. Science. 2010;329:75–8.
Ferrer-Admetlla A, Liang M, Korneliussen T, Nielsen R. On detecting incomplete soft or hard selective sweeps using haplotype structure. Mol Biol Evol.
2014;31:1275–91.
Bergey CM, Lopez M, Harrison GF, Patin E, Cohen JA, Quintana-Murci L, et al.
Polygenic adaptation and convergent evolution on growth and cardiac
genetic pathways in African and Asian rainforest hunter-gatherers. Proc Natl
Acad Sci. 2018;115:E11256–63.

Page 8 of 8

29. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a
web server for functional enrichment analysis and conversions of gene lists(2019 update). Nucleic Acids Res. 2019;47:W191–8.
30. Binns D, Dimmer E, Huntley R, Barrell D, O’Donovan C, Apweiler R.
QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics.
2009;25:3045–6.
31. Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S. GOSemSim: an R package for measuring
semantic similarity among GO terms and gene products. Bioinformatics.
2010;26:976–8.
32. Günther T, Coop G. Robust identification of local adaptation from allele
frequencies. Genetics. 2013;195:205–20.
33. Bu D, Luo H, Huo P, Wang Z, Zhang S, He Z, et al. KOBAS-i: intelligent
prioritization and exploratory visualization of biological functions for geneenrichment analysis. Nucleic Acids Res. 2021;49:W317–25.
34. Gai Y, Tian R, Liu F, Mu Y, Shan L, Irwin DM, et al. Diversified mammalianvisual adaptations to bright- or dim-light environments. Mol Biol Evol.
2023;40:msad063.
35. Dungan SZ, Chang BSW. Ancient whale rhodopsin reconstructs dim-lightvision over a major evolutionary transition: implications for ancestral diving
behavior. Proc Natl Acad Sci. 2022;119: e2118145119.
36. Yamaguchi K, Koyanagi M, Sato K, Terakita A, Kuraku S. Whale shark rhodopsin adapted to deep-sea lifestyle by a substitution associated with humandisease. Proc Natl Acad Sci. 2023;120: e2220728120.
37. Marnetto D, Huerta-Sánchez E. Haplostrips: revealing population structure
through haplotype visualization. Methods Ecol Evol. 2017;8:1389–92.
38. Morgan MD, Pairo-Castineira E, Rawlik K, Canela-Xandri O, Rees J, Sims D,
et al. Genome-wide study of hair colour in UK Biobank explains most of the
SNP heritability. Nat Commun. 2018;9:5271.
39. Stern AJ, Speidel L, Zaitlen NA, Nielsen R. Disentangling selection on
genetically correlated polygenic traits via whole-genome genealogies. Am J
Human Genetics. 2021;108:219–39.
40. Pritchard JK, Di Rienzo A. Adaptation – not by sweeps alone. Nat Rev Genet.
2010;11:665–7.
41. Wellenreuther M, Hansson B. Detecting polygenic evolution: problems,
pitfalls, and promises. Trends Genet. 2016;32:155–64.
42. Taatjes DJ. The human mediator complex: a versatile, genome-wide regulator of transcription. Trends Biochem Sci. 2010;35:315–22.
43. Takahashi JS. Transcriptional architecture of the mammalian circadian clock.
Nat Rev Genet. 2017;18:164–79.
44. Lande-Diner L, Boyault C, Kim JY, Weitz CJ. A positive feedback loop links
circadian clock factor CLOCK-BMAL1 to the basic transcriptional machinery.
Proc Natl Acad Sci. 2013;110:16021–6.
45. Misra N, Damara M, Ye T, Chambon P. The circadian demethylation of aunique intronic deoxymethylCpG-rich island boosts the transcription of
its cognate circadian clock output gene. Proc Natl Acad Sci. 2023;120:e2214062120.
46. Morgan IG, Rose KA. Myopia: is the nature-nurture debate finally over? Clin
Exp Optom. 2019;102:3–17.
47. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA,
et al. Eight thousand years of natural selection in Europe. bioRxiv. 2015.
https://​www.​biorx​iv.​org/​conte​nt/​10.​1101/​01647​7v2. Accessed 27 July 2023.
48. Jiang LQ, Streilein JW, McKinney C. Immune privilege in the eye: an evolutionary adaptation. Dev Comp Immunol. 1994;18:421–31.
49. Niederkorn JY. The eye sees eye to eye with the immune system: the 2019
Proctor Lecture. Invest Ophthalmol Vis Sci. 2019;60:4489–95.
50. Akpek EK, Gottsch JD. Immune defense at the ocular surface. Eye.
2003;17:949–56.
51. Baker RS, Rand LI, Krolewski AS, Maki T, Warram JH, Aiello LM. Influence of
HLA-DR phenotype and myopia on the risk of nonproliferative and proliferative diabetic retinopathy. Am J Ophthalmol. 1986;102:693–700.
52. Lin H-J, Wei C-C, Chang C-Y, Chen T-H, Hsu Y-A, Hsieh Y-C, et al. Role ofchronic inflammation in myopia progression: clinical evidence and experimental validation. eBioMedicine. 2016;10:269–81.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.