Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study

Eirini Tarsani; Georgios Theodorou; Irida Palamidi; Antonios Kominakis

doi:doi:10.11648/j.ijgg.20200801.14

| Peer-Reviewed

Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study

Eirini Tarsani, Georgios Theodorou, Irida Palamidi, Antonios Kominakis

Published in International Journal of Genetics and Genomics (Volume 8, Issue 1)

Received: 5 December 2019 Accepted: 18 December 2019 Published: 31 January 2020

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Traditionally, genome-wide association studies (GWAS) require maximum numbers of genotyped and phenotyped animals to efficiently detect marker-trait associations. Under financial constraints, alternative solutions should be envisaged such that of performing GWAS with fractioned samples of the population. In the present study, we investigated the potential of using random and extreme phenotype samples of a population including 6,700 broilers in detecting significant markers and candidate genes for a typical complex trait (body weight at 35 days). We also explored the utility of using continuous vs. dichotomized phenotypes to detect marker-trait associations. Present results revealed that extreme phenotype samples were superior to random samples while detection efficacy was higher on the continuous over the dichotomous phenotype scale. Furthermore, the use of 50% extreme phenotype samples resulted in detection of 8 out of the 10 markers identified in whole population sampling. Putative causative variants identified in 50% extreme phenotype samples resided in genomic regions harboring 10 growth-related QTLs (e.g. breast muscle percentage, abdominal fat weight etc.) and 6 growth related genes (CACNB1, MYOM2, SLC20A1, ANXA4, FBXO32, SLAIN2). Current findings proposed the use of 50% extreme phenotype sampling as the optimal sampling strategy when performing a cost-effective GWAS.

Published in	International Journal of Genetics and Genomics (Volume 8, Issue 1)
DOI	10.11648/j.ijgg.20200801.14
Page(s)	29-40
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Body Weight, Broilers, Extreme Phenotypes

References

[1]	Steemers FJ, Chang W, Lee G, Barker DL, Shen R, Gunderson KL. Whole-genome genotyping with the single-base extension assay. Nat Methods. Nature Publishing Group; 2006; 3: 31–33. doi: 10.1038/nmeth842.
[2]	Lebowitz RJ, Soller M, Beckmann JS. Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines. Theor Appl Genet. Springer-Verlag; 1987; 73: 556–562. doi: 10.1007/BF00289194.
[3]	Lander ES, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. Genetics Society of America; 1989; 121: 185–99. Available: http://www.ncbi. nlm.nih.gov/pubmed/2563713.
[4]	Darvasi A, Soller M. Selective genotyping for determination of linkage between a marker locus and a quantitative trait locus. Theor Appl Genet. Springer-Verlag; 1992; 85–85: 353–359. doi: 10.1007/BF00222881.
[5]	Gallais A, Moreau L, Charcosset A. Detection of marker–QTL associations by studying change in marker frequencies with selection. Theor Appl Genet. Springer-Verlag; 2007; 114: 669–681. doi: 10. 1007/s00122-006-0467-z.
[6]	Sen S, Johannes F, Broman KW. Selective genotyping and phenotyping strategies in a complex trait context. Genetics. Genetics Society of America; 2009; 181: 1613–26. doi: 10.1534/genetics.108.094607.
[7]	Barnett IJ, Lee S, Lin X. Detecting rare variant effects using extreme phenotype sampling in sequencing association studies. Genet Epidemiol. NIH Public Access; 2013; 37: 142–51. doi: 10.1002/gepi.21699.
[8]	Rabier CE. On statistical inference for selective genotyping. J Stat Plan Inference. North-Holland; 2014; 147: 24–52. doi: 10.1016/J.JSPI.2013.11.010.
[9]	Flint J, Valdar W, Shifman S, Mott R. Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet. Nature Publishing Group; 2005; 6: 271–286. doi: 10.1038/nrg1576.
[10]	Yang J, Jiang H, Yeh C-T, Yu J, Jeddeloh JA, Nettleton D, et al. Extreme-phenotype genome-wide association study (XP-GWAS): a method for identifying trait-associated variants by sequencing pools of individuals selected from a diversity panel. Plant J. John Wiley & Sons, Ltd (10. 1111); 2015; 84: 587–596. doi: 10.1111/tpj.13029.
[11]	Zhang Y, Qi G, Park J-H, Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat Genet. Nature Publishing Group; 2018; 50: 1318–1326. doi: 10.1038/s41588-018-0193-x.
[12]	Li D, Lewinger JP, Gauderman WJ, Murcray CE, Conti D. Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies. Genet Epidemiol. NIH Public Access; 2011; 35: 790–9. doi: 10.1002/gepi.20628.
[13]	Wan L, Dong L, Xiao S, Han Z, Wang X, Wang Z. Genomewide association study for economic traits in the large yellow croaker with different numbers of extreme phenotypes. J Genet. 2018; 97: 887–895. Available: http://www.ncbi.nlm.nih.gov/pubmed/30262700.
[14]	Zhou Y-J, Wang Y, Chen L-L. Detecting the Common and Individual Effects of Rare Variants on Quantitative Traits by Using Extreme Phenotype Sampling. Genes (Basel). Multidisciplinary Digital Publishing Institute (MDPI); 2016; 7. doi: 10.3390/genes7010002.
[15]	Bjørnland T, Bye A, Ryeng E, Wisløff U, Langaas M. Improving power of genetic association studies by extreme phenotype sampling: a review and some new results. 2017; Available: http://arxiv.org/abs/1701.01286.
[16]	Kranis A, Gheyas AA, Boschiero C, Turner F, Yu L, Smith S, et al. Development of a high density 600K SNP genotyping array for chicken. BMC Genomics. BioMed Central; 2013; 14: 59. doi: 10.1186/1471-2164-14-59.
[17]	Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet. Nature Publishing Group; 2012; 44: 825–830. doi: 10.1038/ng.2314.
[18]	VanRaden PM. Efficient Methods to Compute Genomic Predictions. J Dairy Sci. 2008; 91: 4414–4423. doi: 10.3168/jds.2007-0980.
[19]	Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet. NIH Public Access; 2014; 46: 100–6. doi: 10.1038/ng.2876.
[20]	Chen J, Chen Z. Extended Bayesian information criteria for model selection with large model spaces. Biometrika. Oxford University Press; 2008; 95: 759–771. doi: 10.1093/biomet/asn034.
[21]	Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing [Internet]. Journal of the Royal Statistical Society. Series B (Methodological). WileyRoyal Statistical Society; 1995. pp. 289–300. doi: 10.2307/2346101.
[22]	Yang J, Weedon MN, Purcell S, Lettre G, Estrada K, Willer CJ, et al. Genomic inflation factors under polygenic inheritance. Eur J Hum Genet. Nature Publishing Group; 2011; 19: 807–12. doi: 10.1038/ejhg.2011.39.
[23]	Falconer DS, Mackay TFC. Introduction to Quantitative Genetics. 4th ed. Longmans Green, Harlow, Essex, UK.; 1996.
[24]	Seabury C, Oldeschulte D, Saatchi M, Beever J, Decker J, Halley Y, et al. Genome-wide association study for feed efficiency and growth traits in U. S. beef cattle. BMC Genomics. BioMed Central; 2017; 18: 386–386. doi: 10.1186/S12864-017-3754-Y.
[25]	Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. Nature Publishing Group; 2010; 42: 348–354. doi: 10.1038/ng. 548.
[26]	McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. BioMed Central; 2016; 17: 122. doi: 10.1186/s13059-016-0974-4.
[27]	Beavis W. QTL analyses: power, precision, and accuracy. Molecular dissection of complex traits, 1. 1998. pp. 145–162.
[28]	Zöllner S, Pritchard JK. Overcoming the Winner’s Curse: Estimating Penetrance Parameters from Case-Control Data. Am J Hum Genet. Elsevier; 2007; 80: 605. doi: 10.1086/512821.
[29]	Tak YG, Farnham PJ. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin. BioMed Central; 2015; 8: 57. doi: 10.1186/s13072-015-0050-4.
[30]	Guey LT, Kravic J, Melander O, Burtt NP, Laramie JM, Lyssenko V, et al. Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants. Genet Epidemiol. 2011; 35: n/a-n/a. doi: 10.1002/gepi.20572.
[31]	Lumley T, Dupuis J, Rice KM, Barbalic M, Bis JC, Cupples LA, et al. TWO-PHASE SUBSAMPLING DESIGNS FOR GENOMIC RESEQUENCING STUDIES [Internet]. 2012. Available: https://pdfs.semanticscholar.org/c2cb/173513a8eb8a0adfb2cd59e90248333ce4c9.pdf.
[32]	King EG, Long AD. The Beavis Effect in Next-Generation Mapping Panels in Drosophila melanogaster. G3 Genes, Genomes, Genet. G3: Genes, Genomes, Genetics; 2017; 7: 1643–1652. doi: 10.1534/G3.117.041426.
[33]	Chen F, Liu Y, Sugiura Y, Allen PD, Gregg RG, Lin W. Neuromuscular synaptic patterning requires the function of skeletal muscle dihydropyridine receptors. Nat Neurosci. Nature Publishing Group; 2011; 14: 570–577. doi: 10.1038/nn.2792.
[34]	Fukuzawa A, Lange S, Holt M, Vihola A, Carmignac V, Ferreiro A, et al. Interactions with titin and myomesin target obscurin and obscurin-like 1 to the M-band - implications for hereditary myopathies. J Cell Sci. 2008; 121: 1841–1851. doi: 10. 1242/jcs.028019.
[35]	Schoenauer R, Lange S, Hirschy A, Ehler E, Perriard J-C, Agarkova I. Myomesin 3, a Novel Structural Component of the M-band in Striated Muscle. J Mol Biol. 2008; 376: 338–351. doi: 10.1016/j.jmb.2007.11.048.
[36]	Beck L, Leroy C, Beck-Cormier S, Forand A, Salaün C, Paris N, et al. The Phosphate Transporter PiT1 (Slc20a1) Revealed As a New Essential Gene for Mouse Liver Development. Lewin A, editor. PLoS One. Public Library of Science; 2010; 5: e9148. doi: 10.1371/journal.pone.0009148.
[37]	Yao H-S, Sun C, Li X-X, Wang Y, Jin K-Z, Zhang X-P, et al. Annexin A4-nuclear factor-κB feedback circuit regulates cell malignant behavior and tumor growth in gallbladder cancer. Sci Rep. Nature Publishing Group; 2016; 6: 31056. doi: 10.1038/srep31056.
[38]	Nakashima K, Ishida A, Ijiri D, Ohtsuka A. Effect of dexamethasone on the expression of atrogin-1/MAFbx in chick skeletal muscle. Anim Sci J. John Wiley & Sons, Ltd (10. 1111); 2016; 87: 405–410. doi: 10.1111/asj.12437.
[39]	van der Vaart B, Manatschal C, Grigoriev I, Olieric V, Gouveia SM, Bjelić S, et al. SLAIN2 links microtubule plus end–tracking proteins and controls microtubule growth in interphase. J Cell Biol. 2011; 193: 1083–1099. doi: 10.1083/jcb.201012179.
[40]	Tarsani E, Kranis A, Maniatis G, Avendano S, Hager-Theodorides AL, Kominakis A. Discovery and characterization of functional modules associated with body weight in broilers. Sci Rep. Nature Publishing Group; 2019; 9: 9125. doi: 10.1038/s41598-019-45520-5.
[41]	MacLeod IM, Hayes BJ, Savin KW, Chamberlain AJ, McPartlan HC, Goddard ME. Power of a genome scan to detect and locate quantitative trait loci in cattle using dense single nucleotide polymorphisms. J Anim Breed Genet. 2010; 127: 133–142. doi: 10.1111/j.1439-0388.2009.00831.x.
[42]	Tarsani E, Kominakis A, Theodorou G, Palamidi I. Exploiting extreme phenotypes to investigate haplotype structure and detect signatures of selection for body weight in broilers. THE XV th EUROPEAN POULTRY CONFERENCE DUBROVNIK, CROATIA Conference Information and Proceedings. 2018. p. 90. Available: https://pure.au.dk/portal/files/140225746/sbornik_docladov_epc_2018.pdf.
[43]	1000 Genomes Project Consortium T 1000 GP, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1, 092 human genomes. Nature. Europe PMC Funders; 2012; 491: 56–65. doi: 10.1038/nature11632.
[44]	Claussnitzer M, Dankel SN, Kim K-H, Quon G, Meuleman W, Haugen C, et al. FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. N Engl J Med. NIH Public Access; 2015; 373: 895. doi: 10.1056/NEJMOA1502214.
[45]	Anand L. chromoMap: An R package for Interactive Visualization and Annotation of Chromosomes. bioRxiv. Cold Spring Harbor Laboratory; 2019; 605600. doi: 10.1101/605600.

Cite This Article

Plain Text BibTeX RIS

APA Style

Eirini Tarsani, Georgios Theodorou, Irida Palamidi, Antonios Kominakis. (2020). Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study. International Journal of Genetics and Genomics, 8(1), 29-40. https://doi.org/10.11648/j.ijgg.20200801.14

Copy | Download

ACS Style

Eirini Tarsani; Georgios Theodorou; Irida Palamidi; Antonios Kominakis. Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study. Int. J. Genet. Genomics 2020, 8(1), 29-40. doi: 10.11648/j.ijgg.20200801.14

Copy | Download

AMA Style

Eirini Tarsani, Georgios Theodorou, Irida Palamidi, Antonios Kominakis. Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study. Int J Genet Genomics. 2020;8(1):29-40. doi: 10.11648/j.ijgg.20200801.14

Copy | Download

@article{10.11648/j.ijgg.20200801.14,
  author = {Eirini Tarsani and Georgios Theodorou and Irida Palamidi and Antonios Kominakis},
  title = {Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study},
  journal = {International Journal of Genetics and Genomics},
  volume = {8},
  number = {1},
  pages = {29-40},
  doi = {10.11648/j.ijgg.20200801.14},
  url = {https://doi.org/10.11648/j.ijgg.20200801.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20200801.14},
  abstract = {Traditionally, genome-wide association studies (GWAS) require maximum numbers of genotyped and phenotyped animals to efficiently detect marker-trait associations. Under financial constraints, alternative solutions should be envisaged such that of performing GWAS with fractioned samples of the population. In the present study, we investigated the potential of using random and extreme phenotype samples of a population including 6,700 broilers in detecting significant markers and candidate genes for a typical complex trait (body weight at 35 days). We also explored the utility of using continuous vs. dichotomized phenotypes to detect marker-trait associations. Present results revealed that extreme phenotype samples were superior to random samples while detection efficacy was higher on the continuous over the dichotomous phenotype scale. Furthermore, the use of 50% extreme phenotype samples resulted in detection of 8 out of the 10 markers identified in whole population sampling. Putative causative variants identified in 50% extreme phenotype samples resided in genomic regions harboring 10 growth-related QTLs (e.g. breast muscle percentage, abdominal fat weight etc.) and 6 growth related genes (CACNB1, MYOM2, SLC20A1, ANXA4, FBXO32, SLAIN2). Current findings proposed the use of 50% extreme phenotype sampling as the optimal sampling strategy when performing a cost-effective GWAS.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study
AU  - Eirini Tarsani
AU  - Georgios Theodorou
AU  - Irida Palamidi
AU  - Antonios Kominakis
Y1  - 2020/01/31
PY  - 2020
N1  - https://doi.org/10.11648/j.ijgg.20200801.14
DO  - 10.11648/j.ijgg.20200801.14
T2  - International Journal of Genetics and Genomics
JF  - International Journal of Genetics and Genomics
JO  - International Journal of Genetics and Genomics
SP  - 29
EP  - 40
PB  - Science Publishing Group
SN  - 2376-7359
UR  - https://doi.org/10.11648/j.ijgg.20200801.14
AB  - Traditionally, genome-wide association studies (GWAS) require maximum numbers of genotyped and phenotyped animals to efficiently detect marker-trait associations. Under financial constraints, alternative solutions should be envisaged such that of performing GWAS with fractioned samples of the population. In the present study, we investigated the potential of using random and extreme phenotype samples of a population including 6,700 broilers in detecting significant markers and candidate genes for a typical complex trait (body weight at 35 days). We also explored the utility of using continuous vs. dichotomized phenotypes to detect marker-trait associations. Present results revealed that extreme phenotype samples were superior to random samples while detection efficacy was higher on the continuous over the dichotomous phenotype scale. Furthermore, the use of 50% extreme phenotype samples resulted in detection of 8 out of the 10 markers identified in whole population sampling. Putative causative variants identified in 50% extreme phenotype samples resided in genomic regions harboring 10 growth-related QTLs (e.g. breast muscle percentage, abdominal fat weight etc.) and 6 growth related genes (CACNB1, MYOM2, SLC20A1, ANXA4, FBXO32, SLAIN2). Current findings proposed the use of 50% extreme phenotype sampling as the optimal sampling strategy when performing a cost-effective GWAS.
VL  - 8
IS  - 1
ER  -

Copy | Download

Author Information

Eirini Tarsani

Department of Animal Science, Agricultural University of Athens, Athens, Greece
Georgios Theodorou

Department of Animal Science, Agricultural University of Athens, Athens, Greece
Irida Palamidi

Department of Animal Science, Agricultural University of Athens, Athens, Greece
Antonios Kominakis

Department of Animal Science, Agricultural University of Athens, Athens, Greece

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Eirini Tarsani, Georgios Theodorou, Irida Palamidi, Antonios Kominakis. (2020). Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study. International Journal of Genetics and Genomics, 8(1), 29-40. https://doi.org/10.11648/j.ijgg.20200801.14

Copy | Download

ACS Style

Eirini Tarsani; Georgios Theodorou; Irida Palamidi; Antonios Kominakis. Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study. Int. J. Genet. Genomics 2020, 8(1), 29-40. doi: 10.11648/j.ijgg.20200801.14

Copy | Download

AMA Style

Eirini Tarsani, Georgios Theodorou, Irida Palamidi, Antonios Kominakis. Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study. Int J Genet Genomics. 2020;8(1):29-40. doi: 10.11648/j.ijgg.20200801.14

Copy | Download

@article{10.11648/j.ijgg.20200801.14,
  author = {Eirini Tarsani and Georgios Theodorou and Irida Palamidi and Antonios Kominakis},
  title = {Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study},
  journal = {International Journal of Genetics and Genomics},
  volume = {8},
  number = {1},
  pages = {29-40},
  doi = {10.11648/j.ijgg.20200801.14},
  url = {https://doi.org/10.11648/j.ijgg.20200801.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20200801.14},
  abstract = {Traditionally, genome-wide association studies (GWAS) require maximum numbers of genotyped and phenotyped animals to efficiently detect marker-trait associations. Under financial constraints, alternative solutions should be envisaged such that of performing GWAS with fractioned samples of the population. In the present study, we investigated the potential of using random and extreme phenotype samples of a population including 6,700 broilers in detecting significant markers and candidate genes for a typical complex trait (body weight at 35 days). We also explored the utility of using continuous vs. dichotomized phenotypes to detect marker-trait associations. Present results revealed that extreme phenotype samples were superior to random samples while detection efficacy was higher on the continuous over the dichotomous phenotype scale. Furthermore, the use of 50% extreme phenotype samples resulted in detection of 8 out of the 10 markers identified in whole population sampling. Putative causative variants identified in 50% extreme phenotype samples resided in genomic regions harboring 10 growth-related QTLs (e.g. breast muscle percentage, abdominal fat weight etc.) and 6 growth related genes (CACNB1, MYOM2, SLC20A1, ANXA4, FBXO32, SLAIN2). Current findings proposed the use of 50% extreme phenotype sampling as the optimal sampling strategy when performing a cost-effective GWAS.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Identification of Candidate Genes for Body Weight in Broilers Using Extreme-Phenotype Genome-Wide Association Study
AU  - Eirini Tarsani
AU  - Georgios Theodorou
AU  - Irida Palamidi
AU  - Antonios Kominakis
Y1  - 2020/01/31
PY  - 2020
N1  - https://doi.org/10.11648/j.ijgg.20200801.14
DO  - 10.11648/j.ijgg.20200801.14
T2  - International Journal of Genetics and Genomics
JF  - International Journal of Genetics and Genomics
JO  - International Journal of Genetics and Genomics
SP  - 29
EP  - 40
PB  - Science Publishing Group
SN  - 2376-7359
UR  - https://doi.org/10.11648/j.ijgg.20200801.14
AB  - Traditionally, genome-wide association studies (GWAS) require maximum numbers of genotyped and phenotyped animals to efficiently detect marker-trait associations. Under financial constraints, alternative solutions should be envisaged such that of performing GWAS with fractioned samples of the population. In the present study, we investigated the potential of using random and extreme phenotype samples of a population including 6,700 broilers in detecting significant markers and candidate genes for a typical complex trait (body weight at 35 days). We also explored the utility of using continuous vs. dichotomized phenotypes to detect marker-trait associations. Present results revealed that extreme phenotype samples were superior to random samples while detection efficacy was higher on the continuous over the dichotomous phenotype scale. Furthermore, the use of 50% extreme phenotype samples resulted in detection of 8 out of the 10 markers identified in whole population sampling. Putative causative variants identified in 50% extreme phenotype samples resided in genomic regions harboring 10 growth-related QTLs (e.g. breast muscle percentage, abdominal fat weight etc.) and 6 growth related genes (CACNB1, MYOM2, SLC20A1, ANXA4, FBXO32, SLAIN2). Current findings proposed the use of 50% extreme phenotype sampling as the optimal sampling strategy when performing a cost-effective GWAS.
VL  - 8
IS  - 1
ER  -

Copy | Download