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Background Growth traits can be used as indicators of adaptation to sub-optimal conditions. The current study aimed at identifying quantitative trait loci (QTL) that control performance under variable temperature conditions in chickens. Methods An F2 population was produced by crossing the Taiwan Country chicken L2 line (selected for body weight, comb area, and egg production) with an experimental line of Rhode Island Red layer R- (selected for low residual feed consumption). A total of 844 animals were genotyped with the 60 K Illumina single nucleotide polymorphism (SNP) chip. Whole-genome interval linkage mapping and a genome-wide association study (GWAS) were performed for body weight at 0, 4, 8, 12, and 16 weeks of age, shank length at 8 weeks of age, size of comb area at 16 weeks of age, and antibody response to sheep red blood cells at 11 weeks of age (7 and 14 days after primary immunization). Relevant genes were identified based on functional annotation of candidate genes and potentially relevant SNPs were detected by comparing whole-genome sequences of several birds between the parental lines. Results Whole-genome QTL analysis revealed 47 QTL and 714 effects associated with 178 SNPs were identified by GWAS with 5% Bonferroni genome-wide significance. Little overlap was observed between the QTL and GWAS results, with only two chromosomal regions detected by both approaches, i.e. one on GGA24 (GGA for Gallus gallu s chromosome) for BW04 and one on GGAZ for six growth-related traits. Based on whole-genome sequence, differences between the parental lines based on several birds were screened in the genome-wide QTL regions and in a region detected by both methods, resulting in the identification of 106 putative candidate genes with a total of 15,443 SNPs, of which 41 were missense and 1698 were not described in the dbSNP archive. Conclusions The QTL detected in this study for growth and morphological traits likely influence adaptation of chickens to sub-tropical climate. Using whole-genome sequence data, we identified candidate SNPs for further confirmation of QTL in the F2 design. A strong QTL effect found on GGAZ underlines the importance of sex-linked inheritance for growth traits in chickens.

AbstractBackgroundThe aim of this study was to analyse the mechanisms that underlie phenotypic quantitative trait loci (QTL) in overfed mule ducks by identifying co-localized proteomic QTL (pQTL). The QTL design consisted of three families of common ducks that were progeny-tested by using 294 male mule ducks. This population of common ducks was genotyped using a genetic map that included 334 genetic markers located across 28 APL chromosomes (APL for Anas platyrhynchos). Mule ducks were phenotyped for 49 traits related to growth, metabolism, overfeeding ability and meat and fatty liver quality, and 326 soluble fatty liver proteins were quantified.ResultsOne hundred and seventy-six pQTL and 80 phenotypic QTL were detected at the 5% chromosome-wide significance threshold. The great majority of the identified pQTL were trans-acting and localized on a chromosome other than that carrying the coding gene. The most significant pQTL (1% genome-wide significance) were found for alpha-enolase on APL18 and fatty acid synthase on APL24. Some proteins were associated with numerous pQTL (for example, 17 and 14 pQTL were detected for alpha-enolase and apolipoprotein A1, respectively) and pQTL hotspots were observed on some chromosomes (APL18, 24, 25 and 29). We detected 66 co-localized phenotypic QTL and pQTL for which the significance of the two-trait QTL (2t-QTL) analysis was higher than that of the strongest QTL using a single-trait approach. Among these, 16 2t-QTL were pleiotropic. For example, on APL15, melting rate and abundance of two alpha-enolase spots appeared to be impacted by a single locus that is involved in the glycolytic process. On APLZ, we identified a pleiotropic QTL that modified both the blood level of glucose at the beginning of the force-feeding period and the concentration of glutamate dehydrogenase, which, in humans, is involved in increased glucose absorption by the liver when the glutamate dehydrogenase 1 gene is mutated.ConclusionsWe identified pleiotropic loci that affect metabolic pathways linked to glycolysis or lipogenesis, and in the end to fatty liver quality. Further investigation, via transcriptomics and metabolomics approaches, is required to confirm the biomarkers that were found to impact the genetic variability of these phenotypic traits.

Quantitative Trait Loci by sequencing (QTL-seq) is a QTL detection method that utilizes the principles of Bulked Segregants Analysis. It detects alleles with extreme frequencies in whole-genome sequence data from two bulked populations with contrasting phenotypes. This approach is less laborious than QTL detection using linkage mapping, and the result had been shown to be comparable in the same mapping population. However, since the genomes of the two bulked populations are completely sequenced, it can facilitate further characterization of the QTL segment and the genes underlying the QTLs. In this study, QTL-seq was simulated using high-density SNP genotyping data from a recombinant inbred population consisting of 188 individuals. The genomes of both parents had been sequenced, and the SNP genotyping identified 2,207 SNP markers that were polymorphic and segregating in the population. Since the markers are dense enough and well distributed across the genome, they can be used to represent the alleles that can be obtained from whole genome resequencing of bulked individuals. The availability of genotype data for each individual in the mapping population also enabled the detection of QTL via linkage mapping. Using data generated from both approaches, various simulations were conducted to compare the results that could be obtained under ideal conditions, as well as less ideal ones such as when the QTL effects are small, the presence of skewed phenotype distribution, and a small number of bulked samples.

We describe a lower bound for the critical value of the supremum of a Chi-Square process. This bound can be approximated using an RQMC simulation. We compare numerically this bound with the upper bound given by Davies, only suitable for a regular Chi-Square process. In a second part, we focus on a non regular Chi-Square process: the Ornstein–Uhlenbeck Chi-Square process. Recently, Rabier et al. ( 2009 ) have shown that this process has an application in genetics: it is the limiting process of the likelihood ratio test process related to the test of a gene on an interval representing a chromosome. Using results from Delong (Commun Stat Theory Method A10(20):2197–2213, 1981 ), we propose a theoretical formula for the supremum of such a process and we compare it in particular with our simulated lower bound.

We consider the likelihood ratio test (LRT) process related to the test of the absence of QTL (a QTL denotes a gene with quantitative effect on a trait) on the interval [0, T ] representing a chromosome. The observation is the trait and the composition of the genome at some locations called “markers”. We focus on the interference phenomenon, i.e. a recombination event inhibits the formation of another recombination event nearby. We give the asymptotic distribution of the LRT process under the null hypothesis that there is no QTL on [0, T ] and under local alternatives with a QTL at t ⋆ on [0, T ]. We show that the LRT process is asymptotically the square of a “linear interpolated and normalized process”. We prove that under the null hypothesis, the distribution of the maximum of the LRT process is the same for a model with or without interference. However, the powers of detection are totally different between the two models.

Monoterpenes are important constituents of the aromas of food and beverages, including wine. Among monoterpenes in wines, wine lactone has the most potent odor. It was proposed to form via acid-catalyzed cyclization of (E)-8-carboxylinalool during wine maturation. It only reaches very low concentrations in wine but its extremely low odor detection threshold makes it an important aroma compound. Using LC-MS/MS, we show here that the (E)-8-carboxylinalool content in wines correlates with their wine lactone content and estimate the kinetic constant for the very slow formation of wine lactone from (E)-8-carboxylinalool. We show that (E)-8-carboxylinalool is accumulated as a glycoside in grape (Vitis vinifera) berries and that one of the cytochrome P450 enzymes most highly expressed in maturing berries, CYP76F14, efficiently oxidizes linalool to (E)-8-carboxylinalool. Our analysis of (E)-8-carboxylinalool in Riesling × Gewurztraminer grapevine progeny established that the CYP76F14 gene co-locates with a quantitative trait locus for (E)-8-carboxylinalool content in grape berries. Our data support the role of CYP76F14 as the major (E)-8-carboxylinalool synthase in grape berries and the role of (E)-8-carboxylinalool as a precursor to wine lactone in wine, providing new insights into wine and grape aroma metabolism, and new methods for food and aroma research and production.

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum Schwabe is an important disease of wheat that causes severe yield losses along with serious quality concerns. Incorporating the host resistance from either wild relatives, landraces, or exotic materials remains challenging and has shown limited success. Therefore, a better understanding of the genetic basis of native FHB resistance in hard winter wheat (HWW) and combining it with major quantitative trait loci (QTLs) can facilitate the development of FHB-resistant cultivars. In this study, we evaluated a set of 257 breeding lines from the South Dakota State University (SDSU) breeding program to uncover the genetic basis of native FHB resistance in the US hard winter wheat. We conducted a multi-locus genome-wide association study (ML-GWAS) with 9,321 high-quality single-nucleotide polymorphisms (SNPs). A total of six distinct marker-trait associations (MTAs) were identified for the FHB disease index (DIS) on five different chromosomes including 2A, 2B, 3B, 4B, and 7A. Further, eight MTAs were identified for Fusarium-damaged kernels (FDK) on six chromosomes including 3B, 5A, 6B, 6D, 7A, and 7B. Out of the 14 significant MTAs, 10 were found in the proximity of previously reported regions for FHB resistance in different wheat classes and were validated in HWW, while four MTAs represent likely novel loci for FHB resistance. Accumulation of favorable alleles of reported MTAs resulted in significantly lower mean DIS and FDK score, demonstrating the additive effect of FHB resistance alleles. Candidate gene analysis for two important MTAs identified several genes with putative proteins of interest; however, further investigation of these regions is needed to identify genes conferring FHB resistance. The current study sheds light on the genetic basis of native FHB resistance in the US HWW germplasm and the resistant lines and MTAs identified in this study will be useful resources for FHB resistance breeding via marker-assisted selection.

We detected a new target quantitative trait locus (QTL) for lodging resistance in rice by analyzing lodging resistance to typhoons (Maysak and Haishen) using a scale from 0 (no prostrating) to 1 (little prostrating or prostrating) to record the resistance score in a Cheongcheong/Nagdong double haploid rice population. Five quantitative trait loci for lodging resistance to typhoons were detected. Among them, qTyM6 and qTyH6 exhibited crucial effects of locus RM3343–RM20318 on chromosome 6, which overlaps with our previous rice lodging studies for the loci qPSLSA6-2, qPSLSB6-5, and qLTI6-2. Within the target locus RM3343–RM20318, 12 related genes belonging to the cytochrome P450 protein family were screened through annotation. Os06g0599200 (OsTyM/Hq6) was selected for further analysis. We observed that the culm and panicle lengths were positively correlated with lodging resistance to typhoons. However, the yield was negatively correlated with lodging resistance to typhoons. The findings of this study improve an understanding of rice breeding, particularly the culm length, early maturing, and heavy panicle varieties, and the mechanisms by which the plant’s architecture can resist natural disasters such as typhoons to ensure food safety. These results also provide the insight that lodging resistance in rice may be associated with major traits such as panicle length, culm length, tiller number, and heading date, and thereby improvements in these traits can increase lodging resistance to typhoons. Moreover, rice breeding should focus on maintaining suitable varieties that can withstand the adverse effects of climate change in the future and provide better food security.

The English grain aphid (EGA) (Sitobion avenae F.) is one of the most destructive species of aphids in wheat- (Triticum aestivum L.) planting areas worldwide. Large quantities of insecticides are usually used to control aphid damage. The identification of new EGA-resistant genes is necessary for sustainable wheat production. The objective of this study was to identify candidate genes for EGA resistance from stable quantitative trait loci (QTLs). We previously constructed a genetic map of unigenes (UG-Map) with 31,445 polymorphic sub-unigenes via the RNA sequencing of ‘TN18 × LM6’ recombinant inbred lines (TL-RILs). The relative aphid index (RAI) for the TL-RILs was investigated for two growing seasons, with three measured times (MTs) in each season. Using the UG-Map, 43 candidate genes were identified from 22 stable QTLs, with an average of 1.95 candidate genes per QTL. Among the 34 candidate genes annotated in the reference genome Chinese Spring (CS) RefSeq v1.1, the homologous genes of seven candidate genes, TraesCS1A02G-319900, TraesCS1B02G397300, TraesCS2D02G460800, TraesCS4A02G015600LC, TraesCS5B02G329200, TraesCS-6A02G000600 and TraesCS6A02G418600LC have been previously reported to play roles in aphid resistance. This suggests that these genes are strongly associated with EGA resistance in wheat. The candidate genes in this study should facilitate the cloning of EGA-resistant genes and genetic improvement in wheat breeding programs.