Quantitative trait loci mapping with microarray marker intensities
Pages 472 - 478
Abstract
Many methods have been developed for mapping quantitative trait loci (QTLs) using microarrays. Traditional methods for QTL mapping rely on the assumption that biallelic genotype calls represent the complete genetic variation at a marker. In reality, the process of converting microarray intensities to discrete genotype calls results in the loss of marker information on other variations involving the marker sequence, such as nearby SNPs, deletions, or copy numbers.
We have developed a novel approach to QTL mapping that directly uses microarray marker intensities. Our method scans for marker windows where the intensity distances between sample pairs are correlated with the quantitative phenotype differences. The presence of such markers indicates that samples which are genetically close together in the region also share similar phenotype values, suggesting the presence of a QTL. The significance of putative QTLs is then assessed through permutation testing. By directly incorporating genotype intensities, our method eliminates intermediate processes such as genotype calling or ancestry inference that may introduce uncertainty or data loss.
We tested our method on synthetic phenotype data of mice genotyped with the 78K-marker MegaMUGA array, and our results compared favorably to those of R/qtl, a well-establishe QTL mapping package. In addition, we used our method to map the binary albino trait in inbred and backcrossed mice to the tyrosinase (Tyr) gene on chromosome 7, and we also verified several QTLs found to affect colitis-related traits from a previous mouse study.
References
[1]
David E Barton, Byoung S Kwon, and Uta Francke. Human tyrosinase gene, mapped to chromosome 11 (q14? q21), defines second region of homology with mouse chromosome 7. Genomics, 3(1):17--24, 1988.
[2]
Karl W Broman, Hao Wu, Śaunak Sen, and Gary A Churchill. R/qtl: Qtl mapping in experimental crosses. Bioinformatics, 19(7):889--890, 2003.
[3]
Riyan Cheng, Mark Abney, Abraham A Palmer, and Andrew D Skol. Qtlrel: an r package for genome-wide association studies in which relatedness is a concern. BMC genetics, 12(1):66, 2011.
[4]
A. T. Chinwalla, L. L. Cook, K. D. Delehaunty, G. A. Fewell, L. A. Fulton, R. S. Fulton, T. A. Graves, L. D. W. Hillier, E. R. Mardis, J. D. McPherson, et al. Initial sequencing and comparative analysis of the mouse genome. Nature, 420(6915):520--562, 2002.
[5]
Deanna M Church, Leo Goodstadt, LaDeana W Hillier, Michael C Zody, Steve Goldstein, Xinwe She, Carol J Bult, Richa Agarwala, Joshua L Cherry, Michael DiCuccio, et al. Lineage-specific biology revealed by a finished genome assembly of the mouse. PLoS biology, 7(5):e1000112, 2009.
[6]
Gary A Churchill and Rebecca W Doerge. Empirical threshold values for quantitative trait mapping. Genetics, 138(3):963--971, 1994.
[7]
Collaborative Cross Consortium et al. The genome architecture of the collaborative cross mouse genetic reference population. Genetics, 190(2):389--401, 2012.
[8]
John P Didion, Hyuna Yang, Keith Sheppard, Chen-Ping Fu, Leonard McMillan, Fernando PM de Villena, and Gary A Churchill. Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias. BMC genomics, 13(1):34, 2012.
[9]
Chen-Ping Fu, Catherine E Welsh, Fernando Pardo-Manuel de Villena, and Leonard McMillan. Inferring ancestry in admixed populations using microarray probe intensities. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, pages 105--112. ACM, 2012.
[10]
Daniel M Gatti. Doqtl: Qtl mapping for diversity outbred mice. http://cgd.jax.org/apps/doqtl/DOQTL.shtml, 2014.
[11]
Christine A Hackett, Karen McLean, and Glenn J Bryan. Linkage analysis and qtl mapping using snp dosage data in a tetraploid potato mapping population. PloS one, 8(5):e63939, 2013.
[12]
Chris S Haley and Sarah A Knott. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity, 69(4):315--324, 1992.
[13]
T. M. Keane, L. Goodstadt, P. Danecek, M. A. White, K. Wong, B. Yalcin, A. Heger, A. Agam, G. Slater, M. Goodson, et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature, 477(7364):289--294, 2011.
[14]
Daisuke Komura, Fan Shen, Shumpei Ishikawa, Karen R Fitch, Wenwei Chen, Jane Zhang, Guoying Liu, Sigeo Ihara, Hiroshi Nakamura, Matthew E Hurles, et al. Genome-wide detection of human copy number variations using high-density dna oligonucleotide arrays. Genome research, 16(12):1575--1584, 2006.
[15]
Byoung S Kwon, Asifa K Haq, Seymour H Pomerantz, and Ruth Halaban. Isolation and sequence of a cdna clone for human tyrosinase that maps at the mouse c-albino locus. Proceedings of the National Academy of Sciences, 84(21):7473--7477, 1987.
[16]
Eric S Lander and David Botstein. Mapping mendelian factors underlying quantitative traits using rflp linkage maps. Genetics, 121(1):185--199, 1989.
[17]
Nathan Mantel. The detection of disease clustering and a generalized regression approach. Cancer research, 27(2 Part 1):209--220, 1967.
[18]
Richard Mott, Christopher J Talbot, Maria G Turri, Allan C Collins, and Jonathan Flint. A method for fine mapping quantitative trait loci in outbred animal stocks. Proceedings of the National Academy of Sciences, 97(23):12649--12654, 2000.
[19]
Allison R Rogala, Andrew P Morgan, Alexis M Christensen, Terry J Gooch, Timothy A Bell, Darla R Miller, Virginia L Godfrey, and Fernando Pardo-Manuel de Villena. The collaborative cross as a resource for modeling human disease: Cc011/unc, a new mouse model for spontaneous colitis. Mammalian Genome, pages 1--14, 2014.
[20]
Peter E Smouse, Jeffrey C Long, and Robert R Sokal. Multiple regression and correlation extensions of the mantel test of matrix correspondence. Systematic zoology, pages 627--632, 1986.
[21]
William Valdar, Leah C Solberg, Dominique Gauguier, Stephanie Burnett, Paul Klenerman, William O Cookson, Martin S Taylor, J Nicholas P Rawlins, Richard Mott, and Jonathan Flint. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature genetics, 38(8):879--887, 2006.
[22]
Kai Wang, Mingyao Li, Dexter Hadley, Rui Liu, Joseph Glessner, Struan FA Grant, Hakon Hakonarson, and Maja Bucan. Penncnv: an integrated hidden markov model designed for high-resolution copy number variation detection in whole-genome snp genotyping data. Genome research, 17(11):1665--1674, 2007.
[23]
Catherine E Welsh, Chen-Ping Fu, Fernando Pardo-Manuel de Villena, and Leonard McMillan. Fine-scale recombination mapping of high-throughput sequence data. In Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, page 585. ACM, 2013.
[24]
H. Yang, J. R. Wang, J. P. Didion, R. J. Buus, T. A. Bell, C. E. Welsh, F. Bonhomme, A. H. T. Yu, M. W. Nachman, J. Pialek, et al. Subspecific origin and haplotype diversity in the laboratory mouse. Nature genetics, 43(7):648--655, 2011.
Index Terms
- Quantitative trait loci mapping with microarray marker intensities
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Published: 20 September 2014
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