From Polar Bears to People: The Role of Ethnic Genetic Variation in

Thermoregulation and Heat-Related Health Risk

Alexandra Baumann

1,∗ a

, Jakob Thiel

2,∗ b

, Nina Haffer

3,4 c

, Shailendra Gupta

1 d

and

Markus Wolﬁen

2,5 e

Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD

Dresden University of Technology, Dresden, Germany

Berlin Institute of Health at Charit

e - Universitatsmedizin Berlin, Germany

Charit

e - University Medicine Berlin, Germany

Center for Scalable Data Analytics and Artiﬁcial Intelligence (ScaDS.AI), Dresden/Leipzig, Dresden, Germany

ﬁ

Keywords:

Heat Illness, Heat Susceptibility, Climate Change, Ethnicity, Genetic Association.

Abstract:

As climate change increases the frequency and severity of acute heat events, it is crucial to determine fac-

tors for appropriate healthcare strategies and predictive models. Previously, it was stated that socioeconomic

factors primarily play a role in heat-related illness risk. Analogous to the polar bear’s unique adaptations

to the cold, humans exhibit distinct genetic traits shaped by their migration to diverse climates. This posi-

tion paper hypothesizes that genetic differences among human ethnic groups, in addition to socioeconomic

and other factors, also contribute to variations in thermoregulation and inﬂuence susceptibility to heat-related

diseases. To understand genetic adaptations across human ethnicities (initially European and African), we

propose a genetic association analysis of single nucleotide polymorphisms (SNPs) in genes associated with

thermoregulation. An assessment of changes in thermoregulation gene regulation networks will be possible

by conducting a functional pathway analysis. Expected outcomes include identifying differences in SNP dis-

tributions of thermoregulation-associated genes across ethnicities. Challenges such as the underrepresentation

of African populations in genomic databases must also be addressed. This research aims to provide a foun-

dational understanding of genetic contributions to heat adaptation, guiding the development of personalized,

equitable healthcare responses to climate-induced heat stress.

1 BACKGROUND

1.1 The Polar Bear’s Struggle with

Climate Change

Climate change is driving widespread environmental

shifts, notably the melting of the ice caps and an over-

all increase of global temperatures. The polar bear,

native to arctic regions, faces challenges as its habi-

tat warms beyond its evolutionary adaptations. Un-

https://orcid.org/0009-0003-8946-8741

https://orcid.org/0009-0007-1951-1678

https://orcid.org/0000-0002-9541-2811

https://orcid.org/0000-0002-3470-3260

https://orcid.org/0000-0002-1887-4772

∗

Shared ﬁrst authorship.

like other bear species, the polar bear differentiates

by diverse characteristics, such as adaptation to colder

temperatures by dual-layered fur or slip-proof feet

(Welch et al., 2014). Imagine a polar bear visiting

black bears or grizzly bears in warmer regions, where

it lacks adaptations for heat. While black bear’s ears

are large for heat dissipation, the ears of the polar bear

are smaller and the they have a thick layer of subcuta-

neous fat (Rinker et al., 2019). The polar bear is also

bigger than the other bear species in warmer regions.

This can be explained by a rule proposed by the sci-

entist Carl Bergmann, who discovered that these size

variations relate to the surface area-to-body mass ra-

tio (Bergmann, 1848). Smaller body size is typical in

populations near the equator, while larger body size is

more common in colder regions.

Baumann, A., Thiel, J., Haffer, N., Gupta, S. and Wolﬁen, M.

From Polar Bears to People: The Role of Ethnic Genetic Variation in Thermoregulation and Heat-Related Health Risk.

DOI: 10.5220/0013256100003911

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 1, pages 655-660

ISBN: 978-989-758-731-3; ISSN: 2184-4305

655

1.2 Human Heat Adaptations Around

the Globe

This natural variation among bears serves as a vivid

analogy: just as bear species adapt to speciﬁc cli-

mates, human population may also exhibit diverse

adaptations in resilience to heat - an increasingly im-

portant consideration in our warming world. Cli-

mate adaptations affect not only physical character-

istics but also health factors, such as birth weight

and fetal growth, in populations across diverse cli-

mates (Lambert et al., 2008). As humans migrated

across different climates, it was necessary to slowly

adapt to the environment over multiple generations,

which led to phenotypic variation across ethnicities.

Research on thermoregulation in different ethnici-

ties suggests both genotypic and phenotypic adapta-

tions to heat (Taylor, 2006; Lim, 2020). Since 2006,

genetic variation has been proposed as an adaptive

mechanism for heat tolerance, with regulatory pro-

cesses controlling individual heat responses (Taylor,

2006). Observable adaptations include skeletal mor-

phology and traits like nose shape, hair type, and

lip structure, with one of the most prominent being

skin and eye color (Lambert et al., 2008). Often,

multiple genetic variants collectively inﬂuence a sin-

gle phenotype, highlighting the polygenic nature of

these adaptations (Cort

es et al., 2020). The study by

Huang et al. identiﬁed 299 single nucleotide poly-

morphisms (SNPs) differing across populations, pri-

marily in genes inﬂuencing skin and eye color, sug-

gesting a genetic adaptation to environmental factors

such as increased heat and UV exposure (Huang et al.,

2015). For instance, melanin in the skin provides UV

protection for people living in regions closer to the

equator, while depigmentation enhances vitamin D3

synthesis in populations living farther north (Lambert

et al., 2008). These adaptations also impact metabolic

traits. Metabolic rates vary across populations, and

epigenetic factors may contribute to thermoregula-

tory differences (Cramer et al., 2022). Cold tolerance

appears to be higher among individuals from colder

regions, while those from tropical regions may ex-

hibit a reduced response to heat. Natural selection

likely favored thermoregulatory mutations, such as

those supporting heat production in northern popula-

tions (Lambert et al., 2008). Differences in metabolic

rate, subcutaneous fat levels, thyroid activity, and mi-

tochondrial DNA (mtDNA), may all contribute to in-

creased metabolic heat production in colder climates

(Lambert et al., 2008). Furthermore, molecular differ-

ences can inﬂuence protein functions and even drug

responses across populations with implications for

treatments related to heat-induced health conditions

(Duello et al., 2021). Studying these diverse adap-

tations may help inform healthcare practices that ad-

dress climate-related health risks more effectively.

1.3 Impact of Ethnicity and

Socioeconomics on Heat-Related

Illness

Apart from region-speciﬁc heat adaptations, climate

change is increasing the frequency and intensity of

acute heat events (WHO, 2023). Heat impacts health

in various ways, causing both direct and indirect ef-

fects. Directly, exposure to high temperatures can

result in heat-related illnesses, such as heat stroke,

dehydration, or heat collapse (Xu et al., 2023). In-

directly, heat can increase the risk of severe events

like heart attacks and is associated with higher mor-

tality rates among vulnerable populations, including

cancer patients (H

using et al., 2024). The rise in

acute heat events also places a considerable strain on

healthcare systems (WHO, 2023). One response to

this challenge is the development of predictive mod-

els to forecast hospital resource needs, combining cli-

mate and medical data (Thiel et al., 2024). Adding

patient-speciﬁc characteristics as risk factors, such as

age and gender, can improve the accuracy of these

models (Cheng et al., 2019).) Studies by Berberian

et al. (2022) and Jackson et al. (2022) indicate that

people with darker skin may be at higher risk of heat-

related illnesses (Berberian et al., 2022; Jackson et al.,

2022). Both studies highlight that socioeconomic fac-

tors often underlie this increased risk. However, ge-

netic factors inﬂuencing thermoregulation may also

contribute to these differences, as discussed above.

This suggests that ethnic background could be an es-

sential factor in developing predictive models for heat

illnesses, providing a more comprehensive approach

in protecting diverse populations.

This position paper presents the hypothesis that it

would be valuable to investigate whether genetic dif-

ferences between individuals of different ethnicities

can be associated with thermoregulation and, conse-

quently, the risk of heat-related illnesses. By investi-

gating these genetic factors, the study aims to identify

potential contributors to heat-related illness suscepti-

bility, ultimately supporting the development of pre-

cise prevention measures and predictive models.

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656

2 PROPOSED APPROACH ON

ANALYZING THE IMPACT OF

ETHNICITY ON

THERMOREGULATION

The impact of ethnic genetic variation on heat-related

diseases has to be inspected to test the hypothesis. Ex-

isting literature on thermoregulation-associated genes

and gene regulation networks provides a foundation

for this investigation, offering insights into pathways

and mechanisms in heat resilience and adaptation.

Heat regulation plays a crucial role in cellular house-

keeping and homeostasis as well as in stress response

(Charlebois et al., 2018). Further processes and bi-

ological components that might be of interest are

thermo-receptors, thermo-sensitive neurons, or lipol-

ysis (Valero et al., 2014). Diverse databases offer

a systematic framework to identify genes related to

biological processes such as Gene Ontology (GO)

(Aleksander et al., 2023; Ashburner et al., 2000),

or even associations between diseases and variants

like Disgenet (Pinero et al., 2017). Relevant GO

terms, amongst others, include ”heat acclimation”

(GO:0010286), ”response to heat” (GO:0009408),

”cellular response to heat” (GO:0034605), ”heat

generation” (GO:0009409), ”temperature homeosta-

sis” (GO:0001659), ”circadian temperature home-

ostasis” (GO:0003052), or ”sweat gland develop-

ment” (GO:0061114). For example, the GO term

”heat acclimation” is linked to 366 genes and gene

products across all organisms in the database, of

which six are human genes. Similarly, ”temperature

homeostasis” is associated with 264 genes across all

organisms, including 34 human genes. As a start-

ing point, a genetic association analysis could be con-

ducted utilizing common SNP databases such as db-

SNP (Sherry et al., 1999) or gnomAD (Karczewski

et al., 2020) to identify SNPs in thermoregulation-

related genes, with allele frequencies analyzed ac-

cording to ethnicity. Initially, the primary comparison

groups will be Europeans (non-Finnish) and Africans

(/African Americans), as these ethnicities are already

represented in common SNP databases, enabling a

robust baseline analysis of allele frequency differ-

ences. To predict the potential effect of each vari-

ant on gene function, including regulatory roles, the

Ensembl Variant Effect Predictor (VEP) tool will be

applied (McLaren et al., 2016). The main focus will

be on SNPs in protein-coding regions. A preliminary

analysis focusing on 40 genes associated with the GO

terms ”heat acclimation” and ”temperature homeosta-

sis” revealed 288 variants from the gnomAD v4.1.0

database with allele frequency differences greater

than 0.5 % between the two before-mentioned pop-

ulations. First ﬁltering steps excluded variants in in-

tronic (not splice-relevant) and UTR regions or vari-

ants with an allele frequency below 0.5 % in the pop-

ulation with the maximum allele frequency. Ten vari-

ants with highest difference between the two popu-

lation allele frequencies in Europeans (non-Finnish)

and Africans (/African Americans) are depicted in

(Table 1).

There are some noticeable differences between

variant allele frequencies across the two populations

in genes associated to thermoregulation. Both popu-

lations are represented with either higher or lower al-

lele frequencies for the variants. Synonymous as well

as missense variants or splice-associated ones can be

found in this ﬁrst view. The variants have to inspected

for their relevance in gene functions. In this ﬁrst

consideration, only exonic and splice region variants

were investigated. However, as SNPs in intronic and

intergenic regions can inﬂuence transcription regula-

tion or affect regulatory RNAs (such as micro RNAs),

those SNPs will be examined as well. This analysis

will help to simulate and assess changes in gene reg-

ulation networks involved in thermoregulation. Ad-

ditionally, functional pathway analysis tools, such as

KEGG (Kanehisa and Goto, 2000) or Reactome (Fab-

regat et al., 2017), could be applied to map these SNPs

within thermoregulation pathways, deepening our un-

derstanding of how variations in these pathways may

contribute to heat resilience or susceptibility. While

the initial focus will be on European and African eth-

nicity, future analyses could be expanded to include

other ethnic groups (such as East Asian or Indigenous

populations) to achieve a more comprehensive view

on thermoregulatory adaptations across diverse envi-

ronments. Incorporating environmental data, such as

historic climate conditions associated with each pop-

ulation, could further contextualize observed genetic

variations as adaptive responses to different climates.

3 EXPECTED INFLUENCES AND

CHALLENGES

We expect to ﬁnd differences in SNP distributions

between the two ethnicities under study, speciﬁcally

within genes associated with thermoregulation. These

SNP variations may contribute to observed differ-

ences in thermoregulation among diverse ethnicities,

particularly between Europeans and Africans in this

analysis. It is also anticipated that some SNPs will

appear in intronic or non-coding regions, which may

not directly impact gene function and could be con-

sidered incidental ”byproducts.” Nevertheless, it re-

From Polar Bears to People: The Role of Ethnic Genetic Variation in Thermoregulation and Heat-Related Health Risk

657

Table 1: Top ten variants in thermoregulation-related genes with highest allele frequency (AF) differences between

African/African American and European (non-Finnish) populations. HGVS nomenclature of variant consequence on cDNA

(c.) or protein (p.) level. Variant Effect Predictor (VEP) annotation of variant region and consequence. Contents extracted

and adapted from gnomAD v4.1.0 (Karczewski et al., 2020).

Gene HGVS Consequence VEP Annotation

AF African/

African American

AF European

(non-Finnish)

STAT3 c.1601-8dup splice region variant 0.6847 0.1937

NAPEPLD p.Asp389Asn missense variant 0.5586 0.9989

DRD2 p.Pro319Pro synonymous variant 0.1168 0.5468

MC3R p.Val44Ile missense variant 0.4284 0.0839

DRD2 p.His313His synonymous variant 0.3622 0.7057

HSPA1A p.Glu110Asp missense variant 0.4176 0.1059

TRPM2 p.Asp1360Asp synonymous variant 0.2808 0.0004

TRPV1 p.Thr469Ile missense variant 0.0741 0.3517

ADRB2 p.Glu27Gln missense variant 0.8239 0.5570

RBBP7 p.Arg37His missense variant 0.1899 0.4445

mains essential to investigate the intronic and non-

coding SNPs, too. They can be located in regula-

tory regions with an effect on transcription rate and

transcript stability or can lead to alternative splicing

(Vaz-Drago et al., 2017). Furthermore, micro RNAs

with key functions in pathway regulations are often

located in intronic regions (Vaz-Drago et al., 2017).

A key challenge, therefore, will be ﬁltering out those

SNPs with a meaningful impact on thermoregulation.

A limitation of this proposed approach is the over-

representation of European populations in genomic

databases, while African populations remain com-

paratively under-studied. This imbalance may affect

the generalizability of the ﬁndings. Additionally, ge-

nomic ancestry alone does not fully capture socioe-

conomic diversity within or between African and Eu-

ropean populations. Socioeconomic factors can in-

ﬂuence health outcomes through epigenetic modiﬁca-

tions, which may impact thermoregulation and other

heat-related traits. Further challenges include ac-

counting for factors such as sex, age, and pre-existing

conditions, which are likely to inﬂuence thermoregu-

latory responses and the risk of heat-related illnesses.

Analyzing male and female participants separately

may reveal sex-speciﬁc effects on thermoregulation,

and considering age groups and health status will be

essential for an in-depth interpretation of the results.

Lastly, the applicability of these ﬁndings to clinical

settings may require long-term studies, as epigen-

tic and genetic inﬂuences on thermoregulation likely

evolve over time.

4 IMPLICATIONS AND

CONCLUSION

This study underscores the importance of examin-

ing genetic diversity in understanding thermoregula-

tion and susceptibility to heat-related illnesses in ad-

dition to socioeconomic factors. By identifying ge-

netic variations associated with heat resilience and

mapping them across diverse ethnicites, we can de-

velop more precise predictive models and preventive

measures tailored to individual needs. The proposed

approach will support health equity, offering insights

that help to mitigate the disproportionate impact of

climate change.

Just as polar bears are uniquely adapted to cold en-

vironments but face challenges as temperatures rise,

different human ethnicities may too require unique

adaptations as they confront a warming world. Un-

derstanding these genetic differences equips us to de-

velop strategies to support all individuals in the face

of changing climates, preserving health across the di-

versity of humanity.

ACKNOWLEDGEMENTS

We are particularly grateful for the ﬁnancial support

provided by the Federal Ministry of Education and

Research (BMBF: OLCIR - 02NUK082C, MiHUBx

- 01ZZ2101A).

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658

REFERENCES

Aleksander, S. A., Balhoff, J., Carbon, S., Cherry, J. M.,

Drabkin, H. J., Ebert, D., Feuermann, M., Gaudet, P.,

Harris, N. L., Hill, D. P., Lee, R., Mi, H., Moxon, S.,

Mungall, C. J., Muruganugan, A., Mushayahama, T.,

Sternberg, P. W., Thomas, P. D., Van Auken, K., Ram-

sey, J., Siegele, D. A., Chisholm, R. L., Fey, P., As-

promonte, M. C., Nugnes, M. V., Quaglia, F., Tosatto,

S., Giglio, M., Nadendla, S., Antonazzo, G., Attrill,

H., dos Santos, G., Marygold, S., Strelets, V., Tabone,

C. J., Thurmond, J., Zhou, P., Ahmed, S. H., Asanit-

thong, P., Luna Buitrago, D., Erdol, M. N., Gage,

M. C., Ali Kadhum, M., Li, K. Y. C., Long, M.,

Michalak, A., Pesala, A., Pritazahra, A., Saverimuttu,

S. C. C., Su, R., Thurlow, K. E., Lovering, R. C., Lo-

gie, C., Oliferenko, S., Blake, J., Christie, K., Cor-

bani, L., Dolan, M. E., Drabkin, H. J., Hill, D. P.,

Ni, L., Sitnikov, D., Smith, C., Cuzick, A., Seager, J.,

Cooper, L., Elser, J., Jaiswal, P., Gupta, P., Jaiswal,

P., Naithani, S., Lera-Ramirez, M., Rutherford, K.,

Wood, V., De Pons, J. L., Dwinell, M. R., Hay-

man, G. T., Kaldunski, M. L., Kwitek, A. E., Lauled-

erkind, S. J. F., Tutaj, M. A., Vedi, M., Wang, S.-J.,

D’Eustachio, P., Aimo, L., Axelsen, K., Bridge, A.,

Hyka-Nouspikel, N., Morgat, A., Aleksander, S. A.,

Cherry, J. M., Engel, S. R., Karra, K., Miyasato,

S. R., Nash, R. S., Skrzypek, M. S., Weng, S., Wong,

E. D., Bakker, E., Berardini, T. Z., Reiser, L., Auch-

incloss, A., Axelsen, K., Argoud-Puy, G., Blatter, M.-

C., Boutet, E., Breuza, L., Bridge, A., Casals-Casas,

C., Coudert, E., Estreicher, A., Livia Famiglietti, M.,

Feuermann, M., Gos, A., Gruaz-Gumowski, N., Hulo,

C., Hyka-Nouspikel, N., Jungo, F., Le Mercier, P.,

Lieberherr, D., Masson, P., Morgat, A., Pedruzzi, I.,

Pourcel, L., Poux, S., Rivoire, C., Sundaram, S., Bate-

man, A., Bowler-Barnett, E., Bye-A-Jee, H., Denny,

P., Ignatchenko, A., Ishtiaq, R., Lock, A., Lussi, Y.,

Magrane, M., Martin, M. J., Orchard, S., Raposo, P.,

Speretta, E., Tyagi, N., Warner, K., Zaru, R., Diehl,

A. D., Lee, R., Chan, J., Diamantakis, S., Raciti, D.,

Zarowiecki, M., Fisher, M., James-Zorn, C., Ponfer-

rada, V., Zorn, A., Ramachandran, S., Ruzicka, L., and

Westerﬁeld, M. (2023). The Gene Ontology knowl-

edgebase in 2023. Genetics, 224(1):iyad031.

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D.,

Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K.,

Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P.,

Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese,

J. C., Richardson, J. E., Ringwald, M., Rubin, G. M.,

and Sherlock, G. (2000). Gene Ontology: tool for the

uniﬁcation of biology. Nature genetics, 25(1):25–29.

Berberian, A. G., Gonzalez, D. J. X., and Cushing, L. J.

(2022). Racial Disparities in Climate Change-Related

Health Effects in the United States. Current Environ-

mental Health Reports, 9(3):451–464.

Bergmann, C. (1848).

Uber die Verh

altnisse der

arme

okonomie der Thiere zu ihrer Gr

osse. 3.

Charlebois, D. A., Hauser, K., Marshall, S., and Bal

azsi, G.

(2018). Multiscale effects of heating and cooling on

genes and gene networks. Proceedings of the National

Academy of Sciences, 115(45).

Cheng, J., Xu, Z., Bambrick, H., Prescott, V., Wang, N.,

Zhang, Y., Su, H., Tong, S., and Hu, W. (2019). Car-

diorespiratory effects of heatwaves: A systematic re-

view and meta-analysis of global epidemiological ev-

idence. Environmental Research, 177:108610.

Cort

es, A. J., L

opez-Hern

andez, F., and Osorio-Rodriguez,

D. (2020). Predicting Thermal Adaptation by Looking

Into Populations’ Genomic Past. Frontiers in Genet-

ics, 11.

Cramer, M. N., Gagnon, D., Laitano, O., and Crandall,

C. G. (2022). Human temperature regulation under

heat stress in health, disease, and injury. Physiologi-

cal Reviews, 102(4):1907–1989.

Duello, T. M., Rivedal, S., Wickland, C., and Weller, A.

(2021). Race and genetics versus ‘race’ in genetics: A

systematic review of the use of African ancestry in ge-

netic studies. Evolution, Medicine, and Public Health,

9(1):232.

Fabregat, A., Sidiropoulos, K., Viteri, G., Forner, O.,

Marin-Garcia, P., Arnau, V., D’Eustachio, P., Stein,

L., and Hermjakob, H. (2017). Reactome pathway

analysis: a high-performance in-memory approach.

BMC Bioinformatics, 18:142.

Huang, T., Shu, Y., and Cai, Y.-D. (2015). Genetic

differences among ethnic groups. BMC Genomics,

16(1):1093.

using, J., Beiderbeck, M., Berressem, S., Kaj

uter, H.,

Stang, A., and Zander, T. (2024). Hitzebedingte

Ubersterblichkeit unter in Nordrhein-Westfalen Kreb-

serkrankten im fortgeschrittenen Stadium. page Do-

cAbstr. 596. German Medical Science GMS Publish-

ing House.

Jackson, P., Larkin, D., Kinnie, K. R., and Aroke, E. N.

(2022). Heat Islands and Chronic Disease: Could

African Americans Be More Vulnerable to Heat-

Related Health Impacts? Journal of National Black

Nurses’ Association : JNBNA, 33(1):33.

Kanehisa, M. and Goto, S. (2000). KEGG: Kyoto Ency-

clopedia of Genes and Genomes. Nucleic Acids Re-

search, 28(1):27.

Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings,

B. B., Alf

oldi, J., Wang, Q., Collins, R. L., Laricchia,

K. M., Ganna, A., Birnbaum, D. P., Gauthier, L. D.,

Brand, H., Solomonson, M., Watts, N. A., Rhodes,

D., Singer-Berk, M., England, E. M., Seaby, E. G.,

Kosmicki, J. A., Walters, R. K., Tashman, K., Far-

joun, Y., Banks, E., Poterba, T., Wang, A., Seed, C.,

Whifﬁn, N., Chong, J. X., Samocha, K. E., Pierce-

Hoffman, E., Zappala, Z., O’Donnell-Luria, A. H.,

Minikel, E. V., Weisburd, B., Lek, M., Ware, J. S.,

Vittal, C., Armean, I. M., Bergelson, L., Cibulskis, K.,

Connolly, K. M., Covarrubias, M., Donnelly, S., Fer-

riera, S., Gabriel, S., Gentry, J., Gupta, N., Jeandet,

T., Kaplan, D., Llanwarne, C., Munshi, R., Novod, S.,

Petrillo, N., Roazen, D., Ruano-Rubio, V., Saltzman,

A., Schleicher, M., Soto, J., Tibbetts, K., Tolonen, C.,

Wade, G., Talkowski, M. E., Neale, B. M., Daly, M. J.,

and MacArthur, D. G. (2020). The mutational con-

straint spectrum quantiﬁed from variation in 141,456

humans. Nature, 581(7809):434–443.

From Polar Bears to People: The Role of Ethnic Genetic Variation in Thermoregulation and Heat-Related Health Risk

659

Lambert, M. I., Mann, T., and Dugas, J. P. (2008). Ethnic-

ity and Temperature Regulation. Medicine and sport

science, 53.

Lim, C. L. (2020). Fundamental Concepts of Human Ther-

moregulation and Adaptation to Heat: A Review in

the Context of Global Warming. International Jour-

nal of Environmental Research and Public Health,

17(21):7795.

McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie,

G. R. S., Thormann, A., Flicek, P., and Cunning-

ham, F. (2016). The Ensembl Variant Effect Predictor.

Genome Biology, 17(1):122.

Pinero, J., Bravo, A., Queralt-Rosinach, N., Gutierrez-

Sacrist

an, A., Deu-Pons, J., Centeno, E., Garcia-

Garcia, J., Sanz, F., and Furlong, L. I. (2017). Dis-

GeNET: a comprehensive platform integrating in-

formation on human disease-associated genes and

variants. Nucleic Acids Research, 45(Database

issue):D833–D839.

Rinker, D. C., Specian, N. K., Zhao, S., and Gibbons,

J. G. (2019). Polar bear evolution is marked by rapid

changes in gene copy number in response to dietary

shift. Proceedings of the National Academy of Sci-

ences of the United States of America, 116(27):13446.

Sherry, S. T., Ward, M., and Sirotkin, K. (1999).

dbSNP—Database for Single Nucleotide Polymor-

phisms and Other Classes of Minor Genetic Variation.

Genome Research, 9(8):677–679.

Taylor, N. A. S. (2006). Ethnic differences in thermoregu-

lation: Genotypic versus phenotypic heat adaptation.

Journal of Thermal Biology, 31(1):90–104.

Thiel, J., Seim, A., Grummt, S., Nesterow, I., Penesch, F.,

Sedlmayr, M., and Weidner, J. (2024). Tools for op-

timizing healthcare resource allocation in response to

climate impacts and heat action planning. Journal of

Public Health.

Valero, K. C. W., Pathak, R., Prajapati, I., Bankston, S.,

Thompson, A., Usher, J., and Isokpehi, R. D. (2014).

A candidate multimodal functional genetic network

for thermal adaptation. PeerJ, 2:e578.

Vaz-Drago, R., Cust

odio, N., and Carmo-Fonseca, M.

(2017). Deep intronic mutations and human disease.

Human Genetics, 136(9):1093–1111.

Welch, A. J., Bedoya-Reina, O. C., Carretero-Paulet, L.,

Miller, W., Rode, K. D., and Lindqvist, C. (2014). Po-

lar Bears Exhibit Genome-Wide Signatures of Bioen-

ergetic Adaptation to Life in the Arctic Environment.

Genome Biology and Evolution, 6(2):433.

WHO (2023). Climate change.

Xu, Z., Watzek, J. T., Phung, D., Oberai, M., Rutherford,

S., and Bach, A. J. E. (2023). Heat, heatwaves, and

ambulance service use: a systematic review and meta-

analysis of epidemiological evidence. International

Journal of Biometeorology, 67(10):1523–1542.

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