Sources of gene expression variation in a globally diverse human cohort
Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity 1 – 5 . Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evoluti...
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description | Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity
1
–
5
. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project
6
, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (
cis
-expression quantitative trait loci (eQTLs) and
cis
-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent ‘population-specific’ effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.
A new open-access RNA sequencing dataset, MAGE, of 731 individuals across geographically diverse human populations provides a valuable resource to study genetic diversity and evolution and expands the capacity to identify new genetic associations. |
doi_str_mv | 10.1038/s41586-024-07708-2 |
format | Article |
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1
–
5
. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project
6
, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (
cis
-expression quantitative trait loci (eQTLs) and
cis
-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent ‘population-specific’ effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.
A new open-access RNA sequencing dataset, MAGE, of 731 individuals across geographically diverse human populations provides a valuable resource to study genetic diversity and evolution and expands the capacity to identify new genetic associations.</description><identifier>ISSN: 0028-0836</identifier><identifier>ISSN: 1476-4687</identifier><identifier>EISSN: 1476-4687</identifier><identifier>DOI: 10.1038/s41586-024-07708-2</identifier><identifier>PMID: 39020179</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>38/91 ; 45/43 ; 631/181/457/649 ; 631/208/199 ; 631/208/729/743 ; 631/337/1645/1792 ; Bias ; Cell Line ; Cohort Studies ; Datasets as Topic ; Epigenomics ; Evolution, Molecular ; Evolutionary genetics ; Female ; Gene expression ; Gene Expression Regulation - genetics ; Gene mapping ; Gene sequencing ; Genealogy ; Genes ; Genetic diversity ; Genetic variance ; Genetic Variation ; Genetics, Population ; Genome, Human - genetics ; Genomes ; Genomics ; Humanities and Social Sciences ; Humans ; Inclusion ; Internationality ; Lymphoblastoid cell lines ; Lymphocytes - cytology ; Lymphocytes - metabolism ; Male ; multidisciplinary ; Nucleotide sequence ; Phenotypic variations ; Population genetics ; Population studies ; Populations ; Quantitative trait loci ; Quantitative Trait Loci - genetics ; Racial Groups - genetics ; RNA Splicing - genetics ; Science ; Science (multidisciplinary) ; Sequence Analysis, RNA ; Splicing ; X chromosomes</subject><ispartof>Nature (London), 2024-08, Vol.632 (8023), p.122-130</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>Copyright Nature Publishing Group Aug 1, 2024</rights><rights>The Author(s) 2024 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c312t-1bd62fc37cebfc2d559e1d53962ea0f54e2788dc8f07158b64cc2d81607e2523</cites><orcidid>0000-0002-5485-8155 ; 0000-0002-5287-627X ; 0000-0003-1861-1523 ; 0000-0001-5806-4494 ; 0000-0003-0615-146X ; 0000-0002-6880-465X ; 0000-0001-7206-8088</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39020179$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Taylor, Dylan J.</creatorcontrib><creatorcontrib>Chhetri, Surya B.</creatorcontrib><creatorcontrib>Tassia, Michael G.</creatorcontrib><creatorcontrib>Biddanda, Arjun</creatorcontrib><creatorcontrib>Yan, Stephanie M.</creatorcontrib><creatorcontrib>Wojcik, Genevieve L.</creatorcontrib><creatorcontrib>Battle, Alexis</creatorcontrib><creatorcontrib>McCoy, Rajiv C.</creatorcontrib><title>Sources of gene expression variation in a globally diverse human cohort</title><title>Nature (London)</title><addtitle>Nature</addtitle><addtitle>Nature</addtitle><description>Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity
1
–
5
. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project
6
, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (
cis
-expression quantitative trait loci (eQTLs) and
cis
-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent ‘population-specific’ effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.
A new open-access RNA sequencing dataset, MAGE, of 731 individuals across geographically diverse human populations provides a valuable resource to study genetic diversity and evolution and expands the capacity to identify new genetic associations.</description><subject>38/91</subject><subject>45/43</subject><subject>631/181/457/649</subject><subject>631/208/199</subject><subject>631/208/729/743</subject><subject>631/337/1645/1792</subject><subject>Bias</subject><subject>Cell Line</subject><subject>Cohort Studies</subject><subject>Datasets as Topic</subject><subject>Epigenomics</subject><subject>Evolution, Molecular</subject><subject>Evolutionary genetics</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Regulation - genetics</subject><subject>Gene mapping</subject><subject>Gene sequencing</subject><subject>Genealogy</subject><subject>Genes</subject><subject>Genetic diversity</subject><subject>Genetic variance</subject><subject>Genetic Variation</subject><subject>Genetics, Population</subject><subject>Genome, Human - 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genetics</topic><topic>Gene mapping</topic><topic>Gene sequencing</topic><topic>Genealogy</topic><topic>Genes</topic><topic>Genetic diversity</topic><topic>Genetic variance</topic><topic>Genetic Variation</topic><topic>Genetics, Population</topic><topic>Genome, Human - genetics</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Inclusion</topic><topic>Internationality</topic><topic>Lymphoblastoid cell lines</topic><topic>Lymphocytes - cytology</topic><topic>Lymphocytes - metabolism</topic><topic>Male</topic><topic>multidisciplinary</topic><topic>Nucleotide sequence</topic><topic>Phenotypic variations</topic><topic>Population genetics</topic><topic>Population studies</topic><topic>Populations</topic><topic>Quantitative trait loci</topic><topic>Quantitative Trait Loci - genetics</topic><topic>Racial Groups - genetics</topic><topic>RNA Splicing - genetics</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Sequence Analysis, RNA</topic><topic>Splicing</topic><topic>X chromosomes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Taylor, Dylan J.</creatorcontrib><creatorcontrib>Chhetri, Surya B.</creatorcontrib><creatorcontrib>Tassia, Michael G.</creatorcontrib><creatorcontrib>Biddanda, Arjun</creatorcontrib><creatorcontrib>Yan, Stephanie M.</creatorcontrib><creatorcontrib>Wojcik, Genevieve L.</creatorcontrib><creatorcontrib>Battle, Alexis</creatorcontrib><creatorcontrib>McCoy, Rajiv C.</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nature (London)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taylor, Dylan J.</au><au>Chhetri, Surya B.</au><au>Tassia, Michael G.</au><au>Biddanda, Arjun</au><au>Yan, Stephanie M.</au><au>Wojcik, Genevieve L.</au><au>Battle, Alexis</au><au>McCoy, Rajiv C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sources of gene expression variation in a globally diverse human cohort</atitle><jtitle>Nature (London)</jtitle><stitle>Nature</stitle><addtitle>Nature</addtitle><date>2024-08-01</date><risdate>2024</risdate><volume>632</volume><issue>8023</issue><spage>122</spage><epage>130</epage><pages>122-130</pages><issn>0028-0836</issn><issn>1476-4687</issn><eissn>1476-4687</eissn><abstract>Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity
1
–
5
. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project
6
, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (
cis
-expression quantitative trait loci (eQTLs) and
cis
-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent ‘population-specific’ effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.
A new open-access RNA sequencing dataset, MAGE, of 731 individuals across geographically diverse human populations provides a valuable resource to study genetic diversity and evolution and expands the capacity to identify new genetic associations.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>39020179</pmid><doi>10.1038/s41586-024-07708-2</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-5485-8155</orcidid><orcidid>https://orcid.org/0000-0002-5287-627X</orcidid><orcidid>https://orcid.org/0000-0003-1861-1523</orcidid><orcidid>https://orcid.org/0000-0001-5806-4494</orcidid><orcidid>https://orcid.org/0000-0003-0615-146X</orcidid><orcidid>https://orcid.org/0000-0002-6880-465X</orcidid><orcidid>https://orcid.org/0000-0001-7206-8088</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 38/91 45/43 631/181/457/649 631/208/199 631/208/729/743 631/337/1645/1792 Bias Cell Line Cohort Studies Datasets as Topic Epigenomics Evolution, Molecular Evolutionary genetics Female Gene expression Gene Expression Regulation - genetics Gene mapping Gene sequencing Genealogy Genes Genetic diversity Genetic variance Genetic Variation Genetics, Population Genome, Human - genetics Genomes Genomics Humanities and Social Sciences Humans Inclusion Internationality Lymphoblastoid cell lines Lymphocytes - cytology Lymphocytes - metabolism Male multidisciplinary Nucleotide sequence Phenotypic variations Population genetics Population studies Populations Quantitative trait loci Quantitative Trait Loci - genetics Racial Groups - genetics RNA Splicing - genetics Science Science (multidisciplinary) Sequence Analysis, RNA Splicing X chromosomes |
title | Sources of gene expression variation in a globally diverse human cohort |
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