Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs

Öz In genomic studies, complex traits can be modelled using repeated measures, thereby gaining a better understanding of the underlying biology. An increased number of measurements per individual might reduce measurement noise, thus increasing the likelihood of detecting true genomic signals. Here we aimed to predict genomic signals over a logistic curve referring to multiple underlying genetic architectures, for both simulated and longitudinal egg weight datasets. The chicken data were obtained from 92 sires and 801 dams of an 11th generation pure line, resulting in data from 1078 hens. We analysed longitudinal measurements of egg weights with 294,705 single nucleotide polymorphisms (SNPs). We found that a single regression-based functional genome-wide association study (fGWAS) could be useful for manipulating dynamic egg weight over the entire laying period based on a moderate to major effect gene. The fGWAS SNPs associated with the egg weight were located on chromosome 1 close to the gene DLEU7, which has a role in regulating ovary weight in chickens. The SNPs were detected based on the absolute effect sizes using whole regression Bayesian models. This approach is likely to be useful for predicting polygenic risk scores and/or genomic breeding values during the genomic selection/ gene editing for longitudinal egg weight measurements.

Kaynakça

1. Das K, Li J, Wang Z, Tong C, Fu G et al. A dynamic model for genome-wide association studies. Human Genetics 2011; 129 (6): 629-639. doi: 10.1007/s00439-011-0960-6

2. Sikorska K, Lesaffre E, Groenen PJF, Rivadeneira F, Eilers PHC. Genome-wide analysis of largescale longitudinal outcomes using penalization GALLOP algorithm. Nature Scientific Reports 2018; 8: 6815. doi: 10.1038/s41598-018-24578-7

3. Wu RL, Lin M. Functional mapping how to study the genetic architecture of dynamic complex traits. Nature Reviews Genetics 2006; 7: 229-237. doi: 10.1038/nrg1804

4. Karacaören B. A Bayesian random walk approach for mapping dynamic quantitative trait. Journal of Applied Nonlinear Dynamics 2016; 5 (1): 105-115. doi: 10.5890/ JAND.2016.03.008

5. Furlotte NA, Eskin E, Eyheramendy S. Genome-wide association mapping with longitudinal data. Genet Epidemiology 2012; 36: 463-471. doi: 10.1002/gepi.21640

6. Wang Z, Xu K, Zhang X, Wu X, Wang Z. Longitudinal SNPset association analysis of quantitative phenotypes. Genetic Epidemiology 2017; 41 (1): 81-93. doi: 10.1002/gepi.22016

7. Wu Z, Hu Y, Melton PE. Longitudinal data analysis for genetic studies in the whole-genome sequencing era. Genetic Epidemiology 2014; 38 (S1): S74-S80. doi: 10.1002/gepi.21829

8. Rönnegård L, McFarlane SE, Husby A, Kawakami, Ellegren H et al. Increasing the power of genome wide association studies in natural populations using repeated measures –evaluation and implementation. Methods in Ecology Evolution 2016; 7 (7): 792-799. doi: 10.1111/2041-210X.12535

9. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al. Finding the missing heritability of complex diseases. Nature 2009; 461 (7265): 747-753. doi: 10.1038/nature08494

10. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome wide dense marker maps. Genetics 2001; 157: 1819-1829.

11. Campos G, Gianola D, Allison DB. Predicting genetic predisposition in human: the promise of whole-genome markers. Nature Reviews Genetics 2010; 11: 880-886. doi: 10.1038/nrg2898

12. Andersson L, Georges M. Domestic-animal genomics: deciphering the genetics of complex traits. Nature Reviews Genetics 2004; 5: 202-212. doi: 10.1038/nrg1294

13. Sikorska K, Montazeri NM, Uitterlinden A, Rivadeneira F, Eilers PH et al. GWAS with longitudinal phenotypes: performance of approximate procedures. European Journal of Human Genetics 2015; 23 (10): 1384. doi: 10.1038/ejhg.2015

14. Liu Z, Sun C, Yan Y, Li G, Wu G et al. Genome-wide association analysis of age-dependent egg weights in chickens. Frontiers in Genetics 2018; 9: 128. doi: 10.3389/fgene.2018.00128

15. Coster A, Bastiaansen JW, Calus MP, Maliepaard C, Bink MC. QTLMAS 2009: simulated dataset. BMC Proceedings 2010; 4 (Suppl. 1): S3. doi: 10.1186/1753-6561-4-S1-S3

16. West GB, Brown JH, Enquist BJ. A general model for ontogenetic growth. Nature 2001; 413: 628-631.

17. Ma CX, Casella G, Wu R. Functional mapping of quantitative trait loci underlying the character process: a theoretical framework. Genetics 2002; 161 (4): 1751-1762.

18. Aulchenko YS, Ripke S, Isaacs A, Van Duijn CM. GenABEL: an R library for genome wide association analysis. Bioinformatics 2006; 23 (10): 1294-1296. doi: 10.1093/bioinformatics/btm108

19. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA et al. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 2006; 38 (8): 904-909. doi: 10.1038 / ng1847

20. Legarra A, Ricardi A, Filangi O. GS3: Genomic Selection, Gibbs Sampling, Gauss Seidel (and BayesCp). Paris, France: INRA; 2011.

21. Legarra A, Robert-Granié C, Manfredi E, Elsen JM. Performance of genomic selection in mice. Genetics 2008; 180: 611-618. doi: 10.1534/genetics.108.088575

22. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria; 2018.

23. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T et al. BLUPF90 and related programs (BGF90). In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production; Montpellier, France; 2002.

24. Honkatukia M, Tuiskula-Haavisto M, de Koning DJ, Virta A, Maki-Tanila A et al. A region on chicken chromosome 2 affects both egg white thinning and egg weight. Genetics Selection Evolution 2005; 37: 563-577. doi: 10.1186/1297-9686-37-6-563

25. Tuiskula-Haavisto M, Honkatukia M, Vilkki J, de Koning DJ, Schulman NF et al. Mapping of quantitative trait loci affecting quality and production traits in egg layers. Poultry Science 2002; 81: 919-927. doi: 10.1093/ps/81.7.919

26. Wolc A, Arango J, Settar P, Fulton JE, O’Sullivan NP et al. Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens. Animal Genetics 2012; 43: 87-96. doi: 10.1111/j.1365- 2052.2012.02381.x

27. Schreiweis MA, Hester PY, Settar P, Moody DE. Identification of quantitative trait loci associated with egg quality, egg production, and body weight in an F2 resource population of chickens. Animal Genetics 2006; 37 (2): 106-112. doi: 10.1111 / j.1365-2052.2005.01394.x

28. Sasaki O, Odawara S, Takahashi H, Nirasawa K, Oyamada Y et al. Genetic mapping of quantitative trait loci affecting body weight, egg character and egg production in F2 intercross chickens. Animal Genetics 2004; 35 (3): 188-194. doi: 10.1111/j.1365-2052.2004.01133.x

29. Yi G, Liu W, Li J, Jiangxia Z, Qu L et al. Genetic analysis for dynamic changes of egg weight in 2 chicken lines. Poultry Science 2014; 93: 2963-2969. doi: 10.3382 / ps.2014-04178

30. Heuven HCM, Janss LLG. Bayesian multi-QTL mapping for growth curve parameters. BMC Proceedings 2010; 4 (Suppl. 1): S12-10. doi: 1186/1753-6561-4-s1-s12

31. Veerkamp RF, Verbyla K, Mulder HA, Calus MPL. Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips. BMC Proceedings 2010; 4 (Suppl. 1): S9-10. doi: 1186/1753-6561-4-s1-s9

32. Veerkamp RF, Mulder HA, Thompson R, Calus MPL. Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped. Journal of Dairy Science 2011; 94 (8): 4189-4197. doi: 10,3168 / jds.2011-4223

33. Sun C, Lu J, Yi G, Yuan J, Duan Z et al. Promising loci and genes for yolk and ovary weight in chickens revealed by a genome-wide association study. PLoS One 2015; 10: e0137145. doi: 10.1371/journal.pone.0137145

Kaynak Göster

Bibtex @araştırma makalesi { tbtkveterinary698472, journal = {Turkish Journal of Veterinary and Animal Sciences}, issn = {1300-0128}, eissn = {1303-6181}, address = {}, publisher = {TÜBİTAK}, year = {2020}, volume = {44}, pages = {9 - 16}, doi = {}, title = {Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs}, key = {cite}, author = {Karacaören, Burak} }
APA Karacaören, B . (2020). Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs . Turkish Journal of Veterinary and Animal Sciences , 44 (1) , 9-16 . Retrieved from https://dergipark.org.tr/tr/pub/tbtkveterinary/issue/52919/698472
MLA Karacaören, B . "Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs" . Turkish Journal of Veterinary and Animal Sciences 44 (2020 ): 9-16 <https://dergipark.org.tr/tr/pub/tbtkveterinary/issue/52919/698472>
Chicago Karacaören, B . "Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs". Turkish Journal of Veterinary and Animal Sciences 44 (2020 ): 9-16
RIS TY - JOUR T1 - Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs AU - Burak Karacaören Y1 - 2020 PY - 2020 N1 - DO - T2 - Turkish Journal of Veterinary and Animal Sciences JF - Journal JO - JOR SP - 9 EP - 16 VL - 44 IS - 1 SN - 1300-0128-1303-6181 M3 - UR - Y2 - 2019 ER -
EndNote %0 Turkish Journal of Veterinary and Animal Sciences Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs %A Burak Karacaören %T Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs %D 2020 %J Turkish Journal of Veterinary and Animal Sciences %P 1300-0128-1303-6181 %V 44 %N 1 %R %U
ISNAD Karacaören, Burak . "Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs". Turkish Journal of Veterinary and Animal Sciences 44 / 1 (Şubat 2020): 9-16 .
AMA Karacaören B . Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs. Turkish Journal of Veterinary and Animal Sciences. 2020; 44(1): 9-16.
Vancouver Karacaören B . Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs. Turkish Journal of Veterinary and Animal Sciences. 2020; 44(1): 9-16.
IEEE B. Karacaören , "Functional and whole regression-based genome-wide association analyses for weight measurements of chicken eggs", Turkish Journal of Veterinary and Animal Sciences, c. 44, sayı. 1, ss. 9-16, Şub. 2020

4404 4329

Arşiv
Sayıdaki Diğer Makaleler

Antibody detection against Akabane (AKA) and Bluetongue (BT) viruses in Algerian dromedary camels

Radhwane SAIDI, Fırat DOĞAN, Veysel Soydal ATASEVEN, Yaşar ERGÜN

The clinical outcome of three procedures for extraarticular stabilization of cranial cruciate ligament injuries in dogs

Cornel IGNA, Roxana DASCĂLU, Daniel BUMB, Bogdan SICOE, Cristian ZAHA, Larisa SCHÜSZLER

Herbal feed additives containing tannins: impact on in vitro fermentation and methane mitigation from total mixed ration

Manju WADHWA, Prabh Kaur SIDHU, Mohinder Pal Singh BAKSHI

Genotypic diversity of Salmonella ser. Gallinarum strains isolated from 2012 to 2016 in Brazil

Diogo Luiz GADOTTI, Priscila Belleza MACIEL, Raquel REBELATTO, Sabrina Castilho DUARTE, Diogenes DEZEN

The effects of thyme oil and black cumin oil in broiler feeding on growth performance, intestinal histomorphology, and cecal volatile fatty acids

Özlem Durna AYDIN, Gültekin YILDIZ

The impacts of laurel (Laurus nobilis) and basil (Ocimum basilicum) essential oils on oxidative stability and freshness of sous-vide sea bass fillets

Burcu ÖZTÜRK KERİMOĞLU, Hülya Serpil KAVUŞAN, Meltem SERDAROĞLU

The effect of milk urea level on fertility parameters in Holstein–Friesian dairy cows

Kamil SIATKA, Anna SAWA, Mariusz BOGUCKI, Sylwia KRĘŻEL-CZOPEK

Impact of the individual characteristics of French trotters on their racing performance

Wanda GÓRNIAK, Ewa WALKOWICZ, Maria SOROKO, Mariusz KORCZYŃSKI

Stress response related to ultrasonographic examination in dogs

Cenker Çağrı CINGI, Abdurrahman Fatih FİDAN, Durmuş Fatih BAŞER, Mehmet UÇAR, Zülfükar Kadir SARITAŞ

In vivo biocompatibility and fracture healing of hydroxyapatite-hexagonal boron nitridechitosan- collagen biocomposite coating in rats

Mehmet Zeki Yılmaz DEVECİ, Ramazan GÖNENCİ, İbrahim CANPOLAT, Özgür KANAT