Genome-Wide Association Study of Growth Performance and Immune Response to Newcastle Disease Virus of Indigenous Chicken in Rwanda

Abstract

A chicken genome has several regions with quantitative trait loci (QTLs). However, replication and confirmation of QTL effects are required particularly in African chicken populations. This study identified single nucleotide polymorphisms (SNPs) and putative genes responsible for body weight (BW) and antibody response (AbR) to Newcastle disease (ND) in Rwanda indigenous chicken (IC) using genome-wide association studies (GWAS). Multiple testing was corrected using chromosomal false detection rates of 5 and 10% for significant and suggestive thresholds, respectively. BioMart data mining and variant effect predictor tools were used to annotate SNPs and candidate genes, respectively. A total of four significant SNPs (rs74098018, rs13792572, rs314702374, and rs14123335) significantly (p ≤ 7.6E−5) associated with BW were identified on chromosomes (CHRs) 8, 11, and 19. In the vicinity of these SNPs, four genes such as pre-B-cell leukaemia homeobox 1 (PBX1), GPATCH1, MPHOSPH6, and MRM1 were identified. Four other significant SNPs (rs314787954, rs13623466, rs13910430, and rs737507850) all located on chromosome 1 were strongly (p ≤ 7.6E−5) associated with chicken antibody response to ND. The closest genes to these four SNPs were cell division cycle 16 (CDC16), zinc finger, BED-type containing 1 (ZBED1), myxovirus (influenza virus) resistance 1 (MX1), and growth factor receptor bound protein 2 (GRB2) related adaptor protein 2 (GRAP2). Besides, other SNPs and genes suggestively (p ≤ 1.5E−5) associated with BW and antibody response to ND were reported. This work offers a useful entry point for the discovery of causative genes accountable for essential QTLs regulating BW and antibody response to ND traits. Results provide auspicious genes and SNP-based markers that can be used in the improvement of growth performance and ND resistance in IC populations based on gene-based and/or marker-assisted breeding selection.

Fourth Report on Chicken Genes and Chromosomes 2022

“…Other low-cost methods such as reduced representation sequencing based on restriction enzyme cleavage have also been attempted for SNP discovery, validation, and characterization in chicken populations. Among them, restriction-site-associated DNA sequencing (RAD-seq) [Zhai et al, 2015;Yang Z et al, 2020], specific locus amplified fragment sequencing (SLAF-seq) [Jin et al, 2015;Wang W et al, 2016Wang W et al, , 2019Li F et al, 2018Li F et al, , 2021, genotyping by genome reducing and sequencing (GGRS) [Liao et al, 2015[Liao et al, , 2016Zhao QB et al, 2018], and genotyping by sequencing (GBS) [Pértille et al, 2016;Zhang M et al, 2018;Habimana et al, 2021] proved to be effective in identifying and genotyping some novel SNPs in chickens. However, these methods have not yet been widely applied.…”

Population genomics and transcriptomics identify patterns of selection in two Liangshan chicken breeds

“…The interaction between ClipR-59 and Elmo2 is mediated by the atypical PH domain of Elmo2 and the Glu-Pro-rich domain of ClipR-59, regulated by Rho-GTPase, implicating the gene ELMO2 in the regulation of muscle function [41]. MPHOSPH6 (M-phase phosphoprotein 6) has been identi ed as a novel target for 20-week body weight in chicken [42]. The IL1RAPL1 gene was strongly selected in chickens with large body weight [43].…”

Genome-wide association study reveals markers and candidate genes associated with growth in the rice flower carp, an economic fish species of integrated rice-fish culture in China

“…In reduced-representation genome sequencing (RRGS), high-density molecular markers have been obtained at the level of the whole genome (Miller et al, 2007). Genotyping-bysequencing (GBS) is a simplified genome sequencing method that is inexpensive and reliable (Davey et al, 2011;Bian and Wang, 2017), and is widely used in population genetics (Nunez et al, 2015;Drury et al, 2016) and genome-wide association studies (Wu et al, 2019;Habimana et al, 2021). With the development of GBS technology, double-digest genotyping-by-sequencing (ddGBS) can effectively reduce genome complexity and make genome-wide genetic analysis detection more convenient and reliable (Elshire et al, 2011).…”