Expression Levels of LCORL Are Associated with Body Size in Horses

Abstract

Body size is an important characteristic for horses of various breeds and essential for the classification of ponies concerning the limit value of 148 cm (58.27 inches) height at the withers. Genome-wide association analyses revealed the highest associated quantitative trait locus for height at the withers on horse chromosome (ECA) 3 upstream of the candidate gene LCORL. Using 214 Hanoverian horses genotyped on the Illumina equine SNP50 BeadChip and 42 different horse breeds across all size ranges, we confirmed the highly associated single nucleotide polymorphism BIEC2-808543 (−log10P = 8.3) and the adjacent gene LCORL as the most promising candidate for body size. We investigated the relative expression levels of LCORL and its two neighbouring genes NCAPG and DCAF16 using quantitative real-time PCR (RT-qPCR). We could demonstrate a significant association of the relative LCORL expression levels with the size of the horses and the BIEC2-808543 genotypes within and across horse breeds. In heterozygous C/T-horses expression levels of LCORL were significantly decreased by 40% and in homozygous C/C-horses by 56% relative to the smaller T/T-horses. Bioinformatic analyses indicated that this SNP T>C mutation is disrupting a putative binding site of the transcription factor TFIID which is important for the transcription process of genes involved in skeletal bone development. Thus, our findings suggest that expression levels of LCORL play a key role for body size within and across horse breeds and regulation of the expression of LCORL is associated with genetic variants of BIEC2-808543. This is the first functional study for a body size regulating polymorphism in horses and a further step to unravel the mechanisms for understanding the genetic regulation of body size in horses.

Skeletal variation in Tennessee Walking Horses maps to theLCORL/NCAPGgene region

“…Only loci on ECA3 surpassed our Bonferroni cut-off, including a 4 bp indel we identified within an intron near the 3=-end of LCORL. Three markers on ECA3 span a 0.38 Mb region that encompasses the previously reported LCORL/NCAPG genes (37,41,56). Two of these SNPs are located ϳ100 Kb upstream from the LCORL/ NCAPG genes (Fig.…”

“…Sequencing of the LCORL coding region is complicated by the presence of a transcribed retrogene for this locus found on unplaced contigs (ChrUn):37461285-37462892 in the EquCab 2.0 assembly (41). The location of rLCORL was determined in equine metaphase and interphase chromosomes by fluorescence in situ hybridization (FISH) using equine bacterial artificial chromosome (BAC) clones containing the retrogene.…”

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

“…Genes LCORL, VTA1, BICD2, RASA2, GNA12, NCOA1, TNS1, CEP112, DNM3 and RFWD2 were significant for women by both MGAS and MTaSPUsSet, and LCORL, RASA2 and NDUFS3 were significant for men by both tests, while LCORL and RASA2 were significant for both men and women by both tests. Gene LCORL was known to be associated with anthropometric traits, including body height in African Americans (Carty et al, 2012), birth weight and adult height (Horikoshi et al, 2013); it is also a candidate gene for body weight in sheep (Al-Mamun et al, 2015) and body size in horse (Metzger et al, 2013). Figure 4 shows the P values of the univariate test on single traitsingle SNP associations for some genes identified by MTaSPUsSet, along with the (c 1 , c 2 ) values for the most significant MTSPUsSet (c 1 ,c 2 ) test (i.e.…”

“…Among others, endeavors in the following three directions have been undertaken. First, due to polygenic effects (with small effect sizes) on complex traits, instead of the popular single SNP-single trait analysis, it may be more powerful to conduct gene-and pathway-level association tests (Gui et al, 2011;Li et al, 2011Li et al, , 2012Lin and Tang, 2011;Pan et al, 2014Pan et al, , 2015Wu et al, 2010). However, most of the existing association tests are based on the use of individual-level genotypic and phenotypic data, while quite often only summary statistics for single SNPs are available.…”

“…MGAS (Van der Sluis et al, 2015) uses an extended Simes procedure and behaves like a univariate minimum P value approach, while metaCCA (Cichonska et al, 2016) is based on canonical correlation analysis (CCA) of multiple traits and multiple SNPs, which is related to MANOVA and the GEE-score test (Kim et al, 2016;Zhang et al, 2014); the latter two may lose power in some situations with multiple but relatively sparse and weak association signals between the traits and SNPs (Pan et al, 2014;Zhang et al, 2014).…”