Resequencing and phenotyping of the first highly inbred eggplant multiparent population reveal SmLBD13 as a key gene associated with root morphology

Andrea Arrones , Virginia Baraja-Fonseca , Andrea Solana , Mariola Plazas , Salvador Soler , Jaime Prohens , Santiago Vilanova , Pietro Gramazio

Horticulture Research ›› 2025, Vol. 12 ›› Issue (9) : 167

PDF (1480KB)
Horticulture Research ›› 2025, Vol. 12 ›› Issue (9) :167 DOI: 10.1093/hr/uhaf167
Article
research-article
Resequencing and phenotyping of the first highly inbred eggplant multiparent population reveal SmLBD13 as a key gene associated with root morphology
Author information +
History +
PDF (1480KB)

Abstract

The MEGGIC (Magic EGGplant InCanum) population here presented is the first highly inbred eggplant (Solanum melongena) multiparent advanced generation intercross (MAGIC) population developed so far, derived from seven cultivated accessions and one wild Solanum incanum from arid regions. The final 325 S5 lines were high-throughput genotyped using low-coverage whole-genome sequencing (lcWGS) at 3X, yielding 293 783 high-quality SNPs after stringent filtering. Principal component analysis (PCA) and neighbor-joining clustering revealed extensive genetic diversity driven by the unique genetic profile of the wild founder, and lack of genetic structure, suggesting a well-mixed population with a high degree of recombination. The eight founders and a core subset of 212 lines were phenotyped for above-and belowground traits, revealing wide phenotypic diversity. Root morphology traits displayed moderate heritability values, and strong correlation were found between root and aerial traits, suggesting that a well-developed root system supports greater aboveground growth. Genome-wide association studies (GWAS) identified a genomic region on chromosome 6 associated with root biomass (RB), total root length (RL), and root surface area (SA). Within this region, SmLBD13, an LOB-domain protein involved in lateral root development, was identified as a candidate gene. The S. incanum haplotype in this region was linked to reduced lateral root branching density, a trait that may enhance deeper soil exploration and resource uptake. These findings provide key insights into root genetics in eggplant, demonstrating MEGGIC potential for high-resolution trait mapping. Furthermore, they highlight the role of exotic wild germplasm in breeding more resilient cultivars and rootstocks with improved root architecture and enhanced nutrient uptake efficiency.

Cite this article

Download citation ▾
Andrea Arrones, Virginia Baraja-Fonseca, Andrea Solana, Mariola Plazas, Salvador Soler, Jaime Prohens, Santiago Vilanova, Pietro Gramazio. Resequencing and phenotyping of the first highly inbred eggplant multiparent population reveal SmLBD13 as a key gene associated with root morphology. Horticulture Research, 2025, 12(9): 167 DOI:10.1093/hr/uhaf167

登录浏览全文

4963

注册一个新账户 忘记密码

Acknowledgements

This work has been funded by grants PID2021-128148OB-I00 funded by MICIU/AEI/10.13039/501100011033/ and by ERDF/EU, PDC2022-133513-I00 funded by MICIU/AEI/10.13039/501100011033/ and European Union Next Generation EU/PRTR, CIPROM/2021/020 from Conselleria d’Educació, Cultura, Universitats i Ocupació (Generalitat Valenciana), and by the Horizon Europe programme, project number 101094738 (Promoting a Plant Genetic Resource Community for Europe; PRO-GRACE). V.B.-F. is grateful to Conselleria d’Educació, Cultura, Universitats i Ocupació (Generalitat Valenciana), for a predoctoral contract (CIACIF/2023/238). A.S. is grateful to MICIU/AEI/10.13039/501100011033/ and FSE+ for a predoctoral grant (PRE2022-102368). P.G. is grateful for the postdoctoral grant RYC2021-031999-I funded by MICIU/AEI/10.13039/501100011033 and the European Union through NextGenerationEU/PRTR.

Author contributions

M.P., S.S., J.P., S.V., and P.G. conceived the idea and supervised the manuscript. A.A. and A.S. performed the root phenotyping trials. A.A., V.B.-F., P.G., and S.V. performed the bioinformatic analysis and analyzed the data. A.A. prepared a first draft of the manuscript. All other authors reviewed and edited the manuscript.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/,PRJNA392603 and PRJNA1174391.

Conflict of interests

The authors declare no conflicts of interest.

Supplementary data

Supplementary data is available at Horticulture Research online.

References

[1]

Yang Y, Tilman D, Jin Z. et al. Climate change exacerbates the environmental impacts of agriculture. Science. 2024;385: eadn3747

[2]

Abbas M, Abid MA, Meng Z. et al. Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway. Physiol Plant. 2022; 174:e13787

[3]

Qaim M. Role of new plant breeding technologies for food security and sustainable agricultural development. Appl Econ Perspect Policy. 2020; 42:129-50

[4]

Maqbool S, Hassan MA, Xia X. et al. Root system architecture in cereals: progress, challenges and perspective. Plant J. 2022; 110: 23-42

[5]

Den Herder G, Van Isterdael G, Beeckman T. et al. The roots of a new green revolution. Trends Plant Sci. 2010; 15:600-7

[6]

Uga Y. Challenges to design-oriented breeding of root system architecture adapted to climate change. Breed Sci. 2021; 71:3-12

[7]

Teramoto S, Uga Y. Improving the efficiency of plant root sys-tem phenotyping through digitization and automation. Breed Sci. 2022; 72:48-55

[8]

FAOSTAT. “Food and Agriculture Organization of the United Nations Database of Agricultural Production.” FAO Statistical Databases 2025.

[9]

Gramazio P, Alonso D, Arrones A. et al. Conventional and new genetic resources for an eggplant breeding revolution. JExp Bot. 2023; 74:6285-305

[10]

Gramazio P, Prohens J, Plazas M. et al. Development and genetic characterization of advanced backcross materials and an intro-gression line population of Solanum incanum in a S. Melongena background. Front Plant Sci. 2017; 8:1477

[11]

Mangino G, Arrones A, Plazas M. et al. Newly developed MAGIC population allows identification of strong associations and candidate genes for anthocyanin pigmentation in eggplant. Front Plant Sci. 2022; 13:847789

[12]

Scott MF, Ladejobi O, Amer S. et al. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity (Edinb). 2020; 125:396-416

[13]

Cavanagh C, Morell M, Mackay I. et al. From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol. 2008; 11:215-21

[14]

Huang BE, Verbyla KL, Verbyla AP. et al. MAGIC populations in crops: current status and future prospects. Theor Appl Genet. 2015; 128:999-1017

[15]

Mackay I, Powell W. Methods for linkage disequilibrium map-ping in crops. Trends Plant Sci. 2007; 12:57-63

[16]

Abdelraheem A, Thyssen GN, Fang DD. et al. GWAS reveals consistent QTL for drought and salt tolerance in a MAGIC popu-lation of 550 lines derived from intermating of 11 upland cotton (Gossypium hirsutum) parents. Mol Gen Genomics. 2021; 296: 119-29

[17]

Diaz S, Ariza-Suarez D, Izquierdo P. et al. Genetic mapping for agronomic traits in a MAGIC population of common bean (Phaseolus vulgaris L.). BMC Genomics. 2020; 21:799

[18]

Ravelombola W, Shi A, Huynh BL. Loci discovery, network-guided approach, and genomic prediction for drought toler-ance index in a multi-parent advanced generation intercross (MAGIC) cowpea population. Hortic Res. 2021; 8:24

[19]

Ravelombola W, Shi A, Huynh BL. et al. Genetic architecture of salt tolerance in a multi-parent advanced generation inter-cross (MAGIC) cowpea population. BMC Genomics. 2022; 23:1-22

[20]

Sharma V, Mahadevaiah SS, Latha P. et al. Dissecting genomic regions and underlying candidate genes in groundnut MAGIC population for drought tolerance. BMC Plant Biol. 2024; 24: 1044

[21]

ThudiM, SamineniS, LiW. et al. Whole genome resequenc-ing and phenotyping of MAGIC population for high resolu-tion mapping of drought tolerance in chickpea. Plant Genome. 2023; 17:e20333

[22]

Zhang Y, Ponce KS, Meng L. et al. QTL identification for salt tolerance related traits at the seedling stage in indica rice using a multi-parent advanced generation intercross (MAGIC) population. Plant Growth Regul. 2020; 92:365-73

[23]

Arrones A, Vilanova S, Plazas M. et al. The dawn of the age of multi-parent magic populations in plant breeding: novel powerful next-generation resources for genetic analysis and selection of recombinant elite material. Biology (Basel). 2020; 9:229

[24]

Krishnamurthy SL, Sharma PC, Dewan D. et al. Genome wide association study of MAGIC population reveals a novel QTL for salinity and sodicity tolerance in rice. Physiol Mol Biol Plants. 2022; 28:819-35

[25]

López-Malvar A, Butron A, Malvar RA. et al. Association map-ping for maize stover yield and saccharification efficiency using a multiparent advanced generation intercross (MAGIC) population. Sci Rep. 2021; 11:3425

[26]

Arrones A, Antar O, Pereira-Dias L. et al. A novel tomato interspecific (Solanum lycopersicum var. cerasiforme and Solanum pimpinellifolium) MAGIC population facilitates trait association and candidate gene discovery in untapped exotic germplasm. Hortic Res. 2024;11:uhae154

[27]

Fourquet L, Barber T, Campos-Mantello C. et al. An eight-founder wheat MAGIC population allows fine-mapping of flow-ering time loci and provides novel insights into the genetic control of flowering time. Theor Appl Genet. 2024; 137:277

[28]

Yuan G, Sun K, Yu W. et al. Development of a MAGIC population and high-resolution quantitative trait mapping for nicotine content in tobacco. Front Plant Sci. 2023; 13:1086950

[29]

Barchi L, Acquadro A, Alonso D. et al. Single primer enrichment technology (SPET) for high-throughput genotyping in tomato and eggplant germplasm. Front Plant Sci. 2019a; 10:1005

[30]

Sun Z, Zheng Z, Qi F. et al. Development and evaluation of the utility of GenoBaits Peanut 40K for a peanut MAGIC population. Mol Breed. 2023; 43:72

[31]

Kumar P, Choudhary M, Jat BS. et al. Skim sequencing: an advanced NGS technology for crop improvement. J Genet. 2021; 100:38

[32]

Scheben A, Batley J, Edwards D. Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J. 2017; 15: 149-61

[33]

Adhikari L, Shrestha S, Wu S. et al. A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations. Sci Rep. 2022; 12:17583

[34]

Bayer PE, Ruperao P, Mason AS. et al. High-resolution skim genotyping by sequencing reveals the distribution of crossovers and gene conversions in Cicer arietinum and Brassica napus. Theor Appl Genet. 2015; 128:1039-47

[35]

Clot CR, Wang X, Koopman J. et al. High-density linkage map constructed from a skim sequenced diploid potato population reveals transmission distortion and QTLs for tuber yield and pollen shed. Potato Res. 2024; 67:139-63

[36]

Gonda I, Ashrafi H, Lyon DA. et al. Sequencing-based bin map construction of a tomato mapping population, facilitating high-resolution quantitative trait loci detection. Plant Genome. 2019; 12:180010

[37]

Happ MM, Wang H, Graef GL. et al. Generating high density, low cost genotype data in soybean [Glycine max (L.) Merr.]. G 3 (Bethesda). 2019; 9:2153-60

[38]

Huang X, Feng Q, Qian Q. et al. High-throughput genotyping by whole-genome resequencing. Genome Res. 2009; 19:1068-76

[39]

Luo X, Xu L, Wang Y. et al. An ultra-high-density genetic map provides insights into genome synteny, recombination land-scape and taproot skin colour in radish (Raphanus sativus L.). Plant Biotechnol J. 2020; 18:274-86

[40]

Malmberg MM, Barbulescu DM, Drayton MC. et al. Evalua-tion and recommendations for routine genotyping using skim whole genome re-sequencing in canola. Front Plant Sci. 2018; 9:1809

[41]

Wang H, Xu X, Vieira FG. et al. The power of inbreeding: NGS-based GWAS of rice reveals convergent evolution during rice domestication. Mol Plant. 2016; 9:975-85

[42]

Gramazio P, Yan H, Hasing T. et al. Whole-genome resequencing of seven eggplant (Solanum melongena) and one wild rela-tive (S. incanum) accessions provides new insights and breed-ing tools for eggplant enhancement. Front Plant Sci. 2019; 10: 1220

[43]

Knapp S, Vorontsova MS, Prohens J. Wild relatives of the egg-plant (Solanum melongena L.: Solanaceae): new understand-ing of species names in a complex group. PLoS One. 2013; 8: e57039

[44]

Delfin EF, Drobnitch ST, Comas LH. Plant strategies for maxi-mizing growth during water stress and subsequent recovery in Solanum melongena L. (eggplant). PLoS One. 2021; 16:e0256342

[45]

Flores-Saavedra M, Gramazio P, Vilanova S. et al. Introgressed eggplant lines with the wild Solanum incanum evaluated under drought stress conditions. J Integr Agric. 2024; 24:2203-16

[46]

Yousefi F, Soltani F, Lalehparvar AR. et al. Genetic diversity of eggplant (Solanum melongena L.) accessions based on morpho-physiological characteristics and root system archi-tecture traits. J Agric Sci Technol. 2024; 26:387-401

[47]

Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. Preprint 2012;at arxiv.org/abs/1207.3907

[48]

Pook T, Mayer M, Geibel J. et al. Improving imputation quality in beagle for crop and livestock data. G3 (Bethesda). 2020; 10: 177-88

[49]

Diouf I, Pascual L. Multiparental population in crops: methods of development and dissection of genetic tratis. Methods Mol Biol. 2021; 2264:13-32

[50]

Satterlee JW, Alonso D, Gramazio P. et al. Convergent evolution of plant prickles by repeated gene co-option over deep time. Science. 2024;385:eado1663

[51]

He Y, Chen H, Zhou L. et al. Comparative transcription analysis of photosensitive and non-photosensitive eggplants to identify genes involved in dark regulated anthocyanin synthesis. BMC Genomics. 2019; 20:1-678

[52]

Shi S, Liu Y, He Y. et al. R2R3-MYB transcription factor SmMYB75 promotes anthocyanin biosynthesis in eggplant (Solanum melongena L.). Sci Hortic. 2021; 282:110020

[53]

Xu C, Luo F, Hochholdinger F. LOB domain proteins: beyond lateral organ boundaries. Trends Plant Sci. 2016; 21:159-67

[54]

Katuuramu DN, Wechter WP, Washington ML. et al. Pheno-typic diversity for root traits and identification of superior germplasm for root breeding in watermelon. HortScience. 2020; 55:1272-9

[55]

Wang H, Wei J, Li P. et al. Integrating GWAS and gene expression analysis identifies candidate genes for root morphology traits in maize at the seedling stage. Genes (Basel). 2019; 10:773

[56]

Flores-Saavedra M, Plazas M, Gramazio P. et al. Growth and antioxidant responses to water stress in eggplant MAGIC pop-ulation parents, F 1 hybrids and a subset of recombinant inbred lines. BMC Plant Biol. 2024b; 24:560

[57]

Fang DD, Thyssen GN, Wang M. et al. Genomic confirmation of Gossypium barbadense introgression into G. hirsutum and a subsequent MAGIC population. Mol Gen Genomics. 2023; 298: 143-52

[58]

Han Z, Hu G, Liu H. et al. Bin-based genome-wide association analyses improve power and resolution in QTL mapping and identify favorable alleles from multiple parents in a four-way MAGIC rice population. Theor Appl Genet. 2020; 133:59-71

[59]

Baraja-fonseca V, Arrones A, Vilanova S. et al. Benchmarking of low coverage sequencing workflows for precision genotyping in eggplant. Preprint 2024;at bioRxiv 2024.10.24.619843

[60]

Meisner J, Albrechtsen A. Inferring population structure and admixture proportions in low-depth NGS data. Genetics. 2018; 210:719-31

[61]

Gao Y, Yang Z, Yang W. et al. Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputa-tion. Nucleic Acids Res. 2021;49:D1480-8

[62]

Zook JM, Hansen NF, Olson ND. et al. A robust benchmark for detection of germline large deletions and insertions. Nat Biotechnol. 2020; 38:1347-55

[63]

Saripalli G, Adhikari L, Amos C. et al. Integration of genetic and genomics resources in einkorn wheat enables precision mapping of important traits. Commun Biol. 2023; 6:835

[64]

Wang M, Qi Z, Thyssen GN. et al. Genomic interrogation of a MAGIC population highlights genetic factors controlling fiber quality traits in cotton. Commun Biol. 2022; 5:60

[65]

Hashemi SM, Perry G, Rajcan I. et al. SoyMAGIC: an unprece-dented platform for genetic studies and breeding activities in soybean. Front Plant Sci. 2022; 13:945471

[66]

Barchi L, Lanteri S, Portis E. et al. Segregation distortion and linkage analysis in eggplant (Solanum melongena L.). Genome. 2010; 53:805-15

[67]

Lefebvre V, Pflieger S, Thabuis A. et al. Towards the saturation of the pepper linkage map by alignment of three intraspecific maps including known-function genes. Genome. 2002; 45:839-54

[68]

Mathew I, Shimelis H. Genetic analyses of root traits: implica-tions for environmental adaptation and new variety develop-ment: a review. Plant Breed. 2022; 141:695-718

[69]

Schuster A, Santana AS, Uberti A. et al. Genetic diversity, rela-tionships among traits and selection of tropical maize inbred lines for low-P tolerance based on root and shoot traits at seedling stage. Front Plant Sci. 2024; 15:1429901

[70]

Zhang Y, Wu X, Wang X. et al. Crop root system architecture in drought response. J Genet Genomics. 2024; 52:4-13

[71]

Neyhart JL, Lorenz AJ, Smith KP. Multi-trait improvement by predicting genetic correlations in breeding crosses. G3 (Bethesda). 2019; 9:3153-65

[72]

Weigelt A, Mommer L, Andraczek K. et al. An integrated frame-work of plant form and function: the belowground perspective. New Phytol. 2021; 232:42-59

[73]

Frary A, Frary A, Daunay MC. et al. QTL hotspots in eggplant (Solanum melongena) detected with a high resolution map and CIM analysis. Euphytica. 2014; 197:211-28

[74]

Barchi L, Pietrella M, Venturini L. et al. A chromosome-anchored eggplant genome sequence reveals key events in Solanaceae evolution. Sci Rep. 2019b; 9:11769

[75]

Moglia A, Francesco EF, Sergio I. et al. Identification of a new R3 MYB type repressor and functional characterization of the members of the MBW transcriptional complex involved in anthocyanin biosynthesis in eggplant (S. melongena L.). PLoS One. 2020; 15:e0232986

[76]

Zhou L, He Y, Li J. et al. CBFs function in anthocyanin biosyn-thesis by interacting with MYB113 in eggplant (Solanum mel-ongena L.). Plant Cell Physiol. 2020; 61:416-26

[77]

Petroni K, Tonelli C. Recent advances on the regulation of anthocyanin synthesis in reproductive organs. Plant Sci. 2011; 181:219-29

[78]

Liu H, Wang S, Yu X. et al. ARL1, a LOB-domain protein required for adventitious root formation in rice. Plant J. 2005; 43: 47-56

[79]

Taramino G, Sauer M, Stauffer JL. et al. The maize (Zea mays L.) RTCS gene encodes a LOB domain protein that is a key regulator of embryonic seminal and post-embryonic shoot-borne root initiation. Plant J. 2007; 50:649-59

[80]

Cho C, Jeon E, Pandey SK. et al. LBD13 positively regulates lateral root formation in Arabidopsis. Planta. 2019; 249:1251-8

[81]

Lynch JP. Harnessing root architecture to address global chal-lenges. Plant J. 2022; 109:415-31

[82]

Vilanova S, Alonso D, Gramazio P. et al. SILEX: a fast and inexpensive high-quality DNA extraction method suitable for multiple sequencing platforms and recalcitrant plant species. Plant Methods. 2020; 16:110

[83]

Aronesty E. Comparison of sequencing utility programs. Open Bioinformatics J. 2013; 7:1-8

[84]

Li H. Aligning sequence reads, clone sequences and assem-bly contigs with BWA-MEM. Preprint 2013;at https://arxiv.org/abs/1303.3997v2

[85]

Li H. A statistical framework for SNP calling, mutation dis-covery, association mapping and population genetical param-eter estimation from sequencing data. Bioinformatics. 2011; 27: 2987-93

[86]

Browning BL, Browning SR. Genotype imputation with millions of reference samples. Am J Hum Genet. 2016; 98:116-26

[87]

RCoreTeam. R: A Language and Environment for Statistical Com-puting. R Foundation for Statistical Computing, Vienna 2023. https://www.R-project.org/

[88]

Wickham H.Getting Started with ggplot2. In:ggplot2 elegant graphics for data analysis. Cham: Springer International Publish-ing. 2016;11-31

[89]

Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987; 4: 406-25

[90]

Letunic I, Bork P. Interactive tree of life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47: W256-9

[91]

Pook T, Schlather M, De Campos G. et al. Haploblocker: creation of subgroup-specific haplotype blocks and libraries. Genetics. 2019; 212:1045-61

[92]

Ranil RHG, Niran HML, Plazas M. et al. Improving seed germi-nation of the eggplant rootstock Solanum torvum by testing multiple factors using an orthogonal array design. Sci Hortic. 2015; 193:174-81

[93]

Hoagland DR, Arnon DI. Preparing the nutrient solution. The water-culture method for growing plants without soil. In Circ -Calif Agric Exp Stn, Ann Arbor, MI, USA: University of Michigan Library. 1950; 347:29-31

[94]

Easlon HM, Bloom AJ. Easy leaf area: automated digital image analysis for rapid and accurate measurement of leaf area. Appl Plant Sci. 2014; 2:1400033

[95]

Abràmoff MD, Magalhães PJ, Ram SJ. Image processing with image. J Biophotonics Int. 2004; 11:36-41

[96]

Seethepalli A, Dhakal K, Griffiths M. et al. RhizoVision explorer: open-source software for root image analysis and measure-ment standardization. AoB Plants. 2021;13:plab056

[97]

Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 1988; 75:800-2

[98]

Pearson K. Correlation coefficient. Royal Soc Proc. 1895; 58:214

[99]

Revelle WR. Psych: Procedures for Personality and Psychological Research Software. Evanston, IL, USA: Northwestern University, 2017

[100]

Wei T, Simko V, Levy M. et al. Visualization of a correlation matrix. R package “corrplot”. Statistician. 2017; 56:316-24

[101]

Clark SA, van der Werf J. Genomic best linear unbiased pre-diction (gBLUP) for the estimation of genomic breeding values. Methods Mol Biol. 2013; 1019:321-30

[102]

Covarrubias-Pazaran G. Genome-assisted prediction of quan-titative traits using the R package sommer. PLoS One. 2016; 11: e0156744

[103]

VanRaden PM. Efficient methods to compute genomic predic-tions. J Dairy Sci. 2008; 91:4414-23

[104]

Amadeu RR, Cellon C, Olmstead JW. et al. AGHmatrix: R pack-age to construct relationship matrices for autotetraploid and diploid species: a blueberry example. Plant Genome. 2016;9: plantgenome2016.01.0009

[105]

Barchi L, Lanteri S, Portis E. et al. A RAD tag derived marker based eggplant linkage map and the location of QTLs deter-mining anthocyanin pigmentation. PLoS One. 2012; 7:e43740

[106]

Cericola F, Portis E, Lanteri S. et al. Linkage disequilibrium and genome-wide association analysis for anthocyanin pigmenta-tion and fruit color in eggplant. BMC Genomics. 2014; 15:896

[107]

Toppino L, Barchi L, Mercati F. et al. A new intra-specific and high-resolution genetic map of eggplant based on a ril pop-ulation, and location of QTLS related to plant anthocyanin pigmentation and seed vigour. Genes (Basel). 2020; 11:745

[108]

Bradbury PJ, Zhang Z, Kroon DE. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007; 23:2633-5

[109]

Price AL, Patterson NJ, Plenge RM. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006; 38:904-9

[110]

Holm S. A simple sequentially rejective multiple test proce-dure. Scand J Stat. 1979; 6:65-70

[111]

Thissen D, Steinberg L, Kuang D. Quick and easy implementa-tion of the Benjamini-Hochberg procedure for controlling the false positive rate in multiple comparisons. J Educ Behav Stat. 2002; 27:77-83

[112]

Cingolani P, Platts A, Wang LL. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012; 6:80-92

[113]

Robinson JT, Thorvaldsdottir H, Turner D. et al. Igv.Js: an embed-dable JavaScript implementation of the integrative genomics viewer (IGV). Bioinformatics. 2023;39:btac830

PDF (1480KB)

265

Accesses

0

Citation

Detail

Sections
Recommended

/