Evaluating intra- and inter-individual variation in the human placental transcriptome
Tóm tắt
Gene expression variation is a phenotypic trait of particular interest as it represents the initial link between genotype and other phenotypes. Analyzing how such variation apportions among and within groups allows for the evaluation of how genetic and environmental factors influence such traits. It also provides opportunities to identify genes and pathways that may have been influenced by non-neutral processes. Here we use a population genetics framework and next generation sequencing to evaluate how gene expression variation is apportioned among four human groups in a natural biological tissue, the placenta. We estimate that on average, 33.2%, 58.9%, and 7.8% of the placental transcriptome is explained by variation within individuals, among individuals, and among human groups, respectively. Additionally, when technical and biological traits are included in models of gene expression they each account for roughly 2% of total gene expression variation. Notably, the variation that is significantly different among groups is enriched in biological pathways associated with immune response, cell signaling, and metabolism. Many biological traits demonstrate correlated changes in expression in numerous pathways of potential interest to clinicians and evolutionary biologists. Finally, we estimate that the majority of the human placental transcriptome exhibits expression profiles consistent with neutrality; the remainder are consistent with stabilizing selection, directional selection, or diversifying selection. We apportion placental gene expression variation into individual, population, and biological trait factors and identify how each influence the transcriptome. Additionally, we advance methods to associate expression profiles with different forms of selection.
Tài liệu tham khảo
Lewontin RC, Krakauer J. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics. 1973;74:175–95.
Akey JM, Zhang G, Zhang K, Jin L, Shriver MD. Interrogating a high-density SNP map for signatures of natural selection. Genome Res. 2002;12:1805–14.
International HapMap Consortium. A haplotype map of the human genome. Nature. 2005;437:1299–320.
Watkins WS, Ricker CE, Bamshad MJ, Carroll ML, Nguyen SV, Batzer MA, et al. Patterns of ancestral human diversity: an analysis of Alu-insertion and restriction-site polymorphisms. Am J Hum Genet. 2001;68:738–52.
Watkins WS, Rogers AR, Ostler CT, Wooding S, Bamshad MJ, Brassington A-ME, et al. Genetic variation among world populations: inferences from 100 Alu insertion polymorphisms. Genome Res. 2003;13:1607–18.
Bamshad MJ, Wooding S, Watkins WS, Ostler CT, Batzer MA, Jorde LB. Human population genetic structure and inference of group membership. Am J Hum Genet. 2003;72:578–89.
Beaumont MA, Balding DJ. Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol. 2004;13:969–80.
Myles S, Tang K, Somel M, Green RE, Kelso J, Stoneking M. Identification and analysis of genomic regions with large between-population differentiation in humans. Ann Hum Genet. 2008;72:99–110.
Grossman SR, Andersen KG, Shlyakhter I, Tabrizi S, Winnicki S, Yen A, et al. Identifying recent adaptations in large-scale genomic data. Cell. 2013;152:703–13.
Colonna V, Ayub Q, Chen Y, Pagani L, Luisi P, Pybus M, et al. Human genomic regions with exceptionally high levels of population differentiation identified from 911 whole-genome sequences. Genome Biol. 2014;15:R88.
Whitlock MC. Evolutionary inference from QST. Mol Ecol. 2008;17:1885–96.
Relethford JH. Apportionment of global human genetic diversity based on craniometrics and skin color. Am J Phys Anthropol. 2002;118:393–8.
Myles S, Somel M, Tang K, Kelso J, Stoneking M. Identifying genes underlying skin pigmentation differences among human populations. Hum Genet. 2007;120:613–21.
Darwin C. The Descent of Man. 1st ed. London: John Murray; 1871.
Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al. Population genomics of human gene expression. Nat Genet. 2007;39:1217–24.
Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG. Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet. 2007;39:226–31.
Storey JD, Madeoy J, Strout JL, Wurfel M, Ronald J, Akey JM. Gene-expression variation within and among human populations. Am J Hum Genet. 2007;80:502–9.
Zhang W, Duan S, Kistner EO, Bleibel WK, Huang RS, Clark TA, et al. Evaluation of genetic variation contributing to differences in gene expression between populations. Am J Hum Genet. 2008;82:631–40.
Stranger BE, Montgomery SB, Dimas AS, Parts L, Stegle O, Ingle CE, et al. Patterns of Cis regulatory variation in diverse human populations. PLoS Genet. 2012;8:e1002639 EP.
Price AL, Patterson N, Hancks DC, Myers S, Reich D, Cheung VG, et al. Effects of cis and trans genetic ancestry on gene expression in African Americans. PLoS Genet. 2008;4:e1000294.
Lappalainen T, Sammeth M, Friedländer MR, THoen PAC, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–11.
International HapMap Consortium. The International HapMap Project. Nature. 2003;426:789–96.
Dausset J, Cann H, Cohen D, Lathrop M, Lalouel JM, White R. Centre d’etude du polymorphisme humain (CEPH): collaborative genetic mapping of the human genome. Genomics. 1990;6:575–7.
Idaghdour Y, Czika W, Shianna KV, Lee SH, Visscher PM, Martin HC, et al. Geographical genomics of human leukocyte gene expression variation in southern Morocco. Nat Genet. 2010;42:62–7.
Somel M, Khaitovich P, Bahn S, Pääbo S, Lachmann M. Gene expression becomes heterogeneous with age. Curr Biol. 2006;16:R359–60.
Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, et al. Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci U S A. 2003;100:1896–901.
Sood R, Zehnder JL, Druzin ML, Brown PO. Gene expression patterns in human placenta. Proc Natl Acad Sci U S A. 2006;103:5478–83.
Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013;498:236–40.
Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–9.
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.
Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, et al. Genetic structure of human populations. Science. 2002;298:2381–5.
López Herráez D, Bauchet M, Tang K, Theunert C, Pugach I, Li J, et al. Genetic variation and recent positive selection in worldwide human populations: evidence from nearly 1 million SNPs. PLoS One. 2009;4:e7888.
Xing J, Watkins WS, Shlien A, Walker E, Huff CD, Witherspoon DJ, et al. Toward a more uniform sampling of human genetic diversity: a survey of worldwide populations by high-density genotyping. Genomics. 2010;96:199–210.
Roden J, King B, Trout D, Mortazavi A, Wold B, Hart C. Mining gene expression data by interpreting principal components. BMC Bioinformatics. 2006;7:1–22.
Goldinger A, Henders AK, McRae AF, Martin NG, Gibson G, Montgomery GW, et al. Genetic and nongenetic variation revealed for the principal components of human gene expression. Genetics. 2013;195:1117–28.
Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, Ramachandran S, et al. Worldwide human relationships inferred from genome-wide patterns of variation. Science. 2008;319:1100–4.
Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics. 2013;14:254.
Whitehead A, Crawford DL. Neutral and adaptive variation in gene expression. Proc Natl Acad Sci U S A. 2006;103:5425–30.
Leinonen T, McCairns RJS, O’Hara RB, Merilä J. QST–FST comparisons: evolutionary and ecological insights from genomic heterogeneity. Nat Rev Genet. 2013;14:179–90.
Khaitovich P, Weiss G, Lachmann M, Hellmann I, Enard W, Muetzel B, et al. A neutral model of transcriptome evolution. PLoS Biol. 2004;2:E132.
Tajima F. Evolutionary relationship of DNA sequences in finite populations. Genetics. 1983;105:437–60.
Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW. Evolutionary rate in the protein interaction network. Science. 2002;296:750–2.
Gallego Romero I, Pai AA, Tung J, Gilad Y. RNA-Seq: impact of RNA degradation on transcript quantification. BMC Biol. 2014;12:42.
Bjørge T, Sørensen HT, Grotmol T, Engeland A, Stephansson O, Gissler M, et al. Fetal growth and childhood cancer: a population-based study. Pediatrics. 2013;132:e1265–75.
Milne E, Greenop KR, Metayer C, Schüz J, Petridou E, Pombo-de-Oliveira MS, et al. Fetal growth and childhood acute lymphoblastic leukemia: findings from the childhood leukemia international consortium. Int J Cancer. 2013;133:2968–79.
Boedigheimer MJ, Wolfinger RD, Bass MB, Bushel PR, Chou JW, Cooper M, et al. Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC Genomics. 2008;9:285.
Tamura K, Ono A, Miyagishima T, Nagao T, Urushidani T. Comparison of gene expression profiles among papilla, medulla and cortex in rat kidney. J Toxicol Sci. 2006;31:449–69.
Summers K, Crespi B. Cadherins in maternal-foetal interactions: red queen with a green beard? Proc Biol Sci. 2005;272:643–9.
Crawford M. Placental delivery of arachidonic and docosahexaenoic acids: implications for the lipid nutrition of preterm infants. Am J Clin Nutr. 2000;71:275S–84.
Munn DH, Zhou M, Attwood JT, Bondarev I, Conway SJ, Marshall B, et al. Prevention of allogeneic fetal rejection by tryptophan catabolism. Science. 1998;281:1191–3.
Umbers AJ, Aitken EH, Rogerson SJ. Malaria in pregnancy: small babies, big problem. Trends Parasitol. 2011;27:168–75.
Brabin BJ, Romagosa C, Abdelgalil S, Menéndez C, Verhoeff FH, McGready R, et al. The sick placenta-the role of malaria. Placenta. 2004;25:359–78.
Kircher M, Stenzel U, Kelso J. Improved base calling for the Illumina Genome Analyzer using machine learning strategies. Genome Biol. 2009;10:R83.
Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.
Giger T, Excoffier L, Day PJR, Champigneulle A, Hansen MM, Powell R, et al. Life history shapes gene expression in salmonids. Curr Biol. 2006;16:R281–2.
Weir B, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38:1358–70.
Nuzhdin SV, Wayne ML, Harmon KL, McIntyre LM. Common pattern of evolution of gene expression level and protein sequence in Drosophila. Mol Biol Evol. 2004;21:1308–17.
Rifkin SA, Kim J, White KP. Evolution of gene expression in the Drosophila melanogaster subgroup. Nat Genet. 2003;33:138–44.
Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:R14.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014. [http://www.R-project.org/]
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological). 1995;57:289–300.