Combined Linkage Disequilibrium Research Articles

Genome-wide association mapping of quantitative trait loci for chalkiness-related traits in rice (Oryza sativa L.).

Grain chalkiness directly affects the commercial value of rice. Genes related to chalkiness reported thus far have been discovered in mutants, but it has not been identified whether these genes can be used to improve rice quality by breeding. Therefore, discovering more quantitative trait loci (QTLs) or genes related to chalkiness in the rice germplasm is necessary. This study entails a genome-wide association study on the degree of endosperm chalkiness (DEC) and percentage of grains with chalkiness (PGWC) by combining 1.2 million single-nucleotide polymorphisms (SNPs) with the phenotypic data of 173 rice accessions. Thirteen QTLs for DEC and nine for PGWC were identified, of which four were detected simultaneously for both DEC and PGWC; further, qDEC11/qPGWC11 was identified as the major QTL. By combining linkage disequilibrium analysis and SNP information, LOC_Os11g10170 was identified as the candidate gene for DEC. There were significant differences among the haplotypes of LOC_Os11g10170, and the Hap 1 of LOC_Os11g10170 was observed to reduce the DEC by 6.19%. The qRT-PCR results showed that the gene expression levels in accessions with high DEC values were significantly higher than those in accessions with low DEC values during days 21-42after flowering, with a maximum at 28days. These results provide molecular markers and germplasm resources for genetic improvement of the chalkiness-related traits in rice.

Acute pancreatitis and metabolic syndrome: genetic correlations and causal associations.

Although there is a definite correlation between the Metabolic Syndrome (MetS) and Acute Pancreatitis (AP), cause is yet unknown. The current work combined linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) approaches to fill this important information gap. In this study, we harnessed the power of publicly available gene-wide association databases (GWAS) to explore the intricate relationship between MetS and its components with AP. The cornerstone of our analysis was the Inverse-Variance Weighted (IVW) method, serving as our primary analytical tool. In addition to IVW, we complemented our investigation with several other robust MR methods, including MR-Egger, Weighted Median, Maximum Likelihood, and MR-PRESSO. By employing this diverse set of analytical approaches, we sought to ensure the comprehensiveness and robustness of our findings. LDSC regression indicated a genetic correlation between MetS and AP. Univariate MR results indicated a genetic association between MetS (OR = 1.084; 95% CI, 1.005-1.170; P = 0.037), BMI (OR = 1.459; 95% CI, 1.325-1.606; P = 1.46E-14), WHR (OR = 1.189; 95% CI, 1.068-1.323; P = 1.56 E-03), TG (OR = 1.110; 95% CI, 1.001-1.231; P = 0.047), and FI (OR = 1.798; 95% CI, 1.245-2.595; P = 1.74E-03) were able to significantly increase the risk of AP. The results of multivariate MR analysis revealed that these causality associations still existed. Our investigation has yielded compelling evidence that substantiates the presence of both a genetic correlation and a causal relationship between MetS and AP.

Genome-Wide Association Study Reveals the Genetic Basis of Total Flavonoid Content in Brown Rice.

Flavonoids have anti-inflammatory, antioxidative, and anticarcinogenic effects. Breeding rice varieties rich in flavonoids can prevent chronic diseases such as cancer and cardio-cerebrovascular diseases. However, most of the genes reported are known to regulate flavonoid content in leaves or seedlings. To further elucidate the genetic basis of flavonoid content in rice grains and identify germplasm rich in flavonoids in grains, a set of rice core collections containing 633 accessions from 32 countries was used to determine total flavonoid content (TFC) in brown rice. We identified ten excellent germplasms with TFC exceeding 300 mg/100 g. Using a compressed mixed linear model, a total of 53 quantitative trait loci (QTLs) were detected through a genome-wide association study (GWAS). By combining linkage disequilibrium (LD) analysis, location of significant single nucleotide polymorphisms (SNPs), gene expression, and haplotype analysis, eight candidate genes were identified from two important QTLs (qTFC1-6 and qTFC9-7), among which LOC_Os01g59440 and LOC_Os09g24260 are the most likely candidate genes. We also analyzed the geographic distribution and breeding utilization of favorable haplotypes of the two genes. Our findings provide insights into the genetic basis of TFC in brown rice and could facilitate the breeding of flavonoid-rich varieties, which may be a prevention and adjuvant treatment for cancer and cardio-cerebrovascular diseases.

Quantitative trait locus for calving traits on Bos taurus autosome 18 in Holstein cattle is embedded in a complex genomic region

Although the quantitative trait locus (QTL) on chromosome 18 (BTA18) associated with paternal calving ease and stillbirth in Holstein Friesian cattle and its cross has been known for over 20 years, to our knowledge, the exact causal genetic sequence has yet escaped identification. The aim of this study was to re-examine the region of the published QTL on BTA18 and to investigate the possible reasons behind this elusiveness. For this purpose, we carried out a combined linkage disequilibrium and linkage analysis using genotyping data of 2,697 German Holstein Friesian (HF) animals and subsequent whole-genome sequencing (WGS) data analyses and genome assembly of HF samples. We confirmed the known QTL in the 95% confidence interval of 1.089 Mbp between 58.34 and 59.43 Mbp on BTA18. Additionally, these 4 SNPs in the near-perfect linkage disequilibrium with the QTL haplotype were identified: rs381577268 (on 57,816,137 bp, C/T), rs381878735 (on 59,574,329 bp, A/T), rs464221818 (on 59,329,176 bp, C/T), and rs472502785 (on 59,345,689 bp, T/C). Search for the causal mutation using short and long-read sequences, and methylation data of the BTA18 QTL region did not reveal any candidates though. The assembly showed problems in the region, as well as an abundance of segmental duplications within and around the region. Taking the QTL of BTA18 in Holstein cattle as an example, the data presented in this study comprehensively characterize the genomic features that could also be relevant for other such elusive QTL in various other cattle breeds and livestock species as well.

Genome-wide analysis of acute low salinity tolerance in the eastern oyster Crassostrea virginica and potential of genomic selection for trait improvement.

As the global demand for seafood increases, research into the genetic basis of traits that can increase aquaculture production is critical. The eastern oyster (Crassostrea virginica) is an important aquaculture species along the Atlantic and Gulf Coasts of the United States, but increases in heavy rainfall events expose oysters to acute low salinity conditions, which negatively impact production. Low salinity survival is known to be a moderately heritable trait, but the genetic architecture underlying this trait is still poorly understood. In this study, we used ddRAD sequencing to generate genome-wide single-nucleotide polymorphism (SNP) data for four F2 families to investigate the genomic regions associated with survival in extreme low salinity (<3). SNP data were also used to assess the feasibility of genomic selection (GS) for improving this trait. Quantitative trait locus (QTL) mapping and combined linkage disequilibrium analysis revealed significant QTL on eastern oyster chromosomes 1 and 7 underlying both survival and day to death in a 36-day experimental challenge. Significant QTL were located in genes related to DNA/RNA function and repair, ion binding and membrane transport, and general response to stress. GS was investigated using Bayesian linear regression models and prediction accuracies ranged from 0.48 to 0.57. Genomic prediction accuracies were largest using the BayesB prior and prediction accuracies did not substantially decrease when SNPs located within the QTL region on Chr1 were removed, suggesting that this trait is controlled by many genes of small effect. Our results suggest that GS will likely be a viable option for improvement of survival in extreme low salinity.

Are scurs in heterozygous polled (Pp) cattle a complex quantitative trait?

BackgroundBreeding genetically hornless, i.e. polled, cattle provides an animal welfare-friendly and non-invasive alternative to the dehorning of calves. However, the molecular regulation of the development of horns in cattle is still poorly understood. Studying genetic characters such as polledness and scurs, can provide valuable insights into this process. Scurs are hornlike formations that occur occasionally in a wide variety of sizes and forms as an unexpected phenotype when breeding polled cattle.MethodsWe present a unique dataset of 885 Holstein–Friesian cattle with polled parentage. The horn phenotype was carefully examined, and the phenotypic heterogeneity of the trait is described. Using a direct gene test for polledness, the polled genotype of the animals was determined. Subsequently, the existence of a putative scurs locus was investigated using high-density genotype data of a selected subset of 232 animals and two mapping approaches: mixed linear model-based association analyses and combined linkage disequilibrium and linkage analysis.ResultsThe results of an exploratory data analysis indicated that the expression of scurs depends on age at phenotyping, sex and polled genotype. Scurs were more prevalent in males than in females. Moreover, homozygous polled animals did not express any pronounced scurs and we found that the Friesian polled allele suppresses the development of scurs more efficiently than the Celtic polled allele. Combined linkage and linkage disequilibrium mapping revealed four genome-wide significant loci that affect the development of scurs, one on BTA5 and three on BTA12. Moreover, suggestive associations were detected on BTA16, 18 and 23. The mixed linear model-based association analysis supports the results of the combined linkage and linkage disequilibrium analysis. None of the mapping approaches provided convincing evidence for a monogenic inheritance of scurs.ConclusionsOur results contradict the initial and still broadly accepted model for the inheritance of horns and scurs. We hypothesise an oligogenetic model to explain the development of scurs and polledness.

Mapping genomic regions affecting milk traits in Sarda sheep by using the OvineSNP50 Beadchip and principal components to perform combined linkage and linkage disequilibrium analysis

BackgroundThe detection of regions that affect quantitative traits (QTL), to implement selection assisted by molecular information, remains of particular interest in dairy sheep for which genetic gain is constrained by the high costs of large-scale phenotype and pedigree recording. QTL detection based on the combination of linkage disequilibrium and linkage analysis (LDLA) is the most suitable approach in family-structured populations. The main issue in performing LDLA mapping is the handling of the identity-by-descent (IBD) probability matrix. Here, we propose the use of principal component analysis (PCA) to perform LDLA mapping for milk traits in Sarda dairy sheep.MethodsA resource population of 3731 ewes belonging to 161 sire families and genotyped with the OvineSNP50 Beadchip was used to map genomic regions that affect five milk traits. The paternally and maternally inherited gametes of genotyped individuals were reconstructed and IBD probabilities between them were defined both at each SNP position and at the genome level. A QTL detection model fitting fixed effects of principal components that summarize IBD probabilities was tested at each SNP position. Genome-wide (GW) significance thresholds were determined by within-trait permutations.ResultsPCA resulted in substantial dimensionality reduction, in fact 137 and 32 (on average) principal components were able to capture 99% of the IBD variation at the locus and genome levels, respectively. Overall, 2563 positions exceeded the 0.05 GW significance threshold for at least one trait, which clustered into 75 QTL regions most of which affected more than one trait. The strongest signal was obtained for protein content on Ovis aries (OAR) chromosome 6 and overlapped with the region that harbours the casein gene cluster. Additional interesting positions were identified on OAR4 for fat content and on OAR11 for the three yield traits.ConclusionsPCA is a good strategy to summarize IBD probabilities. A large number of regions associated to milk traits were identified. The outputs provided by the proposed method are useful for the selection of candidate genes, which need to be further investigated to identify causative mutations or markers in strong LD with them for application in selection programs assisted by molecular information.

QTL fine mapping for intramuscular fat and fatty acid composition using high-density SNP chip array on SSC12 in Korean native pig × Yorkshire F2 population

Intramuscular fat (IMF) and fatty acid composition are characteristics that are used as important indicators of evaluating high quality pork and contribute to the economic benefits of the pig farming industry. In this study, quantitative trait loci (QTL) fine mapping of chromosome 12 was performed in a population of F2 intercross between Yorkshire (YS) and Korean native pigs (KNPs) by adopting combined linkage and linkage disequilibrium method using high-density SNP chips. QTLs for IMF (H3GA0034813 to H3GA0034965) and oleic acid (C18:1) (ASGA0054380 to ALGA0066299) were located at 120 cM (54.112–57.610 kb) and 85 cM (36.097–38.601 kb), respectively, within chromosome 12 (Sscrofa11.1 genomic reference). In addition, 31 candidate genes present within the IMF QTL region and 28 candidate genes existing within C18:1 QTL region were chosen. In order to understand the function of these candidate genes at the molecular level, these candidate genes were functionally categorized by studying gene ontology and analyzing network and pathway. Among the 59 candidate genes within the region of IMF QTL and C18:1 QTL, five (MYH1, MYH2, MYH4, ACACA, and RPS6KB1) directly interacting candidate genes were found. Furthermore, the RPS6KB1 gene was assumed to be an important candidate gene that is involved in leptin and insulin signaling pathway and participates in controlling adipogenic differentiation, fat deposition, and fatty acid composition, which is related to obesity of pigs.

Detection of quantitative trait loci and putative causal variants affecting somatic cell score in dairy sheep by using a 50K SNP chip and whole-genome sequencing

This study presents a scan of the ovine genome to identify quantitative trait loci (QTL) influencing the somatic cell score (SCS), a classical indicator of subclinical mastitis in sheep, and a subsequent high-resolution analysis of one of the identified QTL regions based on the analysis of whole-genome sequence data sets. A half-sib commercial population of Churra sheep genotyped with a 50K SNP chip was analyzed using linkage analysis (LA) and combined linkage and linkage disequilibrium analysis (LDLA). By LA, 2 5% chromosome-wide significant QTL on OAR5 and OAR25 and one 5% genome-wide significant QTL on ovine chromosome 20 (OAR20) were detected, whereas 22 significant associations were identified by LDLA. Two of the associations detected by LDLA replicated LA-detected effects (OAR20, OAR25). We compared the detected associations with previously reported QTL in sheep and cattle, and functional candidate genes were identified within the estimated confidence intervals. We then performed a high-resolution analysis of the OAR20 QTL region, the most significant QTL region identified by LA that replicated a QTL previously described in Churra sheep for SCS using microsatellite markers. For that, 2 segregating trios of 2 segregating families for the OAR20 QTL (each including the Qq sire and 2 daughters, QQ and qq) were selected for whole-genome sequencing. The bioinformatic analysis of the 6 sequenced samples performed across the genomic interval considered (14.2-41.7 Mb) identified a total of 227,030 variants commonly identified by 2 independent software packages. For the 3 different concordance tests considered, due to discrepancies regarding the QTL peak in the segregating families, the list of mutations concordant with the QTL segregating pattern was processed to identify the variants identified in immune-related genes that show a moderate/high impact on the encoded protein function. Among a list of 85 missense variants concordant with the QTL segregation pattern that were within candidate immune-related genes, 13 variants distributed across 7 genes [PKHD1, NOTCH4, AGER, ENSOARG00000009395 (HLA-C, Homo sapiens), ENSOARG00000015002 (HLA-B, H. sapiens), MOG, and ENSOARG00000018075 (BoLA, Bos taurus, orthologous to human HLA-A] were predicted to cause deleterious effects on protein function. Future studies should assess the possible associations of the candidate variants identified herein in commercial populations with indicator traits of udder inflammation (SCS, clinical mastitis).

Remapping of the belted phenotype in cattle on BTA3 identifies a multiplication event as the candidate causal mutation

BackgroundIt has been known for almost a century that the belted phenotype in cattle follows a pattern of dominant inheritance. In 2009, the approximate position of the belt locus in Brown Swiss cattle was mapped to a 922-kb interval on bovine chromosome 3 and, subsequently, assigned to a 336-kb haplotype block based on an animal set that included, Brown Swiss, Dutch Belted (Lakenvelder) and Belted Galloway individuals. A possible candidate gene in this region i.e. HES6 was investigated but the causal mutation remains unknown. Thus, to elucidate the causal mutation of this prominent coat color phenotype, we decided to remap the belted phenotype in an independent animal set of several European bovine breeds, i.e. Gurtenvieh (belted Brown Swiss), Dutch Belted and Belted Galloway and to systematically scan the candidate region. We also checked the presence of the detected causal mutation in the genome of belted individuals from a Siberian cattle breed.ResultsA combined linkage disequilibrium and linkage analysis based on 110 belted and non-belted animals identified a candidate interval of 2.5 Mb. Manual inspection of the haplotypes in this region identified four candidate haplotypes that consisted of five to eight consecutive SNPs. One of these haplotypes overlapped with the initial 922-kb interval, whereas two were positioned proximal and one was positioned distal to this region. Next-generation sequencing of one heterozygous and two homozygous belted animals identified only one private belted candidate allele, i.e. a multiplication event that is located between 118,608,000 and 118,614,000 bp. Targeted locus amplification and quantitative real-time PCR confirmed an increase in copy number of this region in the genomes of both European (Belted Galloway, Dutch Belted and Gurtenvieh) and Siberian (Yakutian cattle) breeds. Finally, using nanopore sequencing, the exact breakpoints were determined at 118,608,362 and 118,614,132 bp. The closest gene to the candidate causal mutation (16 kb distal) is TWIST2.ConclusionsBased on our findings and those of a previously published study that identified the same multiplication event, a quadruplication on bovine chromosome 3 between positions 118,608,362 and 118,614,132 bp is the most likely candidate causal mutation for the belted phenotype in cattle.

Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding

ABSTRACTWe combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ‘LALDA’) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression – BRR; Bayes A – BA; Bayes B – BB; Bayes C – BC and Bayesian LASSO – BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets.

Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides.

Detection and replication of QTL underlying resistance to gastrointestinal nematodes in adult sheep using the ovine 50K SNP array.

BackgroundPersistence of gastrointestinal nematode (GIN) infection and the related control methods have major impacts on the sheep industry worldwide. Based on the information generated with the Illumina OvineSNP50 BeadChip (50 K chip), this study aims at confirming quantitative trait loci (QTL) that were previously identified by microsatellite-based genome scans and identifying new QTL and allelic variants that are associated with indicator traits of parasite resistance in adult sheep. We used a commercial half-sib population of 518 Spanish Churra ewes with available data for fecal egg counts (FEC) and serum levels of immunoglobulin A (IgA) to perform different genome scan QTL mapping analyses based on classical linkage analysis (LA), a combined linkage disequilibrium and linkage analysis (LDLA) and a genome-wide association study (GWAS).ResultsFor the FEC and IgA traits, we detected a total of three 5 % chromosome-wise significant QTL by LA and 63 significant regions by LDLA, of which 13 reached the 5 % genome-wise significance level. The GWAS also revealed 10 significant SNPs associated with IgAt, although no significant associations were found for LFEC. Some of the significant QTL for LFEC that were detected by LA and LDLA on OAR6 overlapped with a highly significant QTL that was previously detected in a different half-sib population of Churra sheep. In addition, several new QTL and SNP associations were identified, some of which show correspondence with effects that were reported for different populations of young sheep. Other significant associations that did not coincide with previously reported associations could be related to the specific immune response of adult animals.DiscussionOur results replicate a FEC-related QTL located on OAR6 that was previously reported in Churra sheep and provide support for future research on the identification of the allelic variant that underlies this QTL. The small proportion of genetic variance explained by the detected QTL and the large number of functional candidate genes identified here are consistent with the hypothesis that GIN resistance/susceptibility is a complex trait that is not determined by individual genes acting alone but rather by complex multi-gene interactions. Future studies that combine genomic variation analysis and functional genomic information may help elucidate the biology of GIN disease resistance in sheep.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0182-4) contains supplementary material, which is available to authorized users.

Genomic predictions based on animal models using genotype imputation on a national scale in Norwegian Red cattle.

BackgroundIn dairy cattle, current genomic predictions are largely based on sire models that analyze daughter yield deviations of bulls, which are derived from pedigree-based animal model evaluations (in a two-step approach). Extension to animal model genomic predictions (AMGP) is not straightforward, because most of the animals that are involved in the genetic evaluation are not genotyped. In single-step genomic best linear unbiased prediction (SSGBLUP), the pedigree-based relationship matrix A and the genomic relationship matrix G are combined in a matrix H, which allows for AMGP. However, as the number of genotyped animals increases, imputation of the genotypes for all animals in the pedigree may be considered. Our aim was to impute genotypes for all animals in the pedigree, construct alternative relationship matrices based on the imputation results, and evaluate the accuracy of the resulting AMGP by cross-validation in the national Norwegian Red dairy cattle population.ResultsA large-scale national dataset was effectively handled by splitting it into two sets: (1) genotyped animals and their ancestors (i.e. GA set with 20,918 animals) and (2) the descendants of the genotyped animals (i.e. D set with 4,022,179 animals). This allowed restricting genomic computations to a relatively small set of animals (GA set), whereas the majority of the animals (D set) were added to the animal model equations using Henderson’s rules, in order to make optimal use of the D set information. Genotypes were imputed by segregation analysis of a large pedigree with relatively few genotyped animals (3285 out of 20,918). Among the AMGP models, the linkage and linkage disequilibrium based G matrix (GLDLA0) yielded the highest accuracy, which on average was 0.06 higher than with SSGBLUP and 0.07 higher than with two-step sire genomic evaluations.ConclusionsAMGP methods based on genotype imputation on a national scale were developed, and the most accurate method, GLDLA0BLUP, combined linkage and linkage disequilibrium information. The advantage of AMGP over a sire model based on two-step genomic predictions is expected to increase as the number of genotyped cows increases and for species, with smaller sire families and more dam relationships.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0159-8) contains supplementary material, which is available to authorized users.

Multiple Linkage Disequilibrium Mapping Methods to Validate Additive Quantitative Trait Loci in Korean Native Cattle (Hanwoo).

The efficiency of genome-wide association analysis (GWAS) depends on power of detection for quantitative trait loci (QTL) and precision for QTL mapping. In this study, three different strategies for GWAS were applied to detect QTL for carcass quality traits in the Korean cattle, Hanwoo; a linkage disequilibrium single locus regression method (LDRM), a combined linkage and linkage disequilibrium analysis (LDLA) and a BayesCπ approach. The phenotypes of 486 steers were collected for weaning weight (WWT), yearling weight (YWT), carcass weight (CWT), backfat thickness (BFT), longissimus dorsi muscle area, and marbling score (Marb). Also the genotype data for the steers and their sires were scored with the Illumina bovine 50K single nucleotide polymorphism (SNP) chips. For the two former GWAS methods, threshold values were set at false discovery rate <0.01 on a chromosome-wide level, while a cut-off threshold value was set in the latter model, such that the top five windows, each of which comprised 10 adjacent SNPs, were chosen with significant variation for the phenotype. Four major additive QTL from these three methods had high concordance found in 64.1 to 64.9Mb for Bos taurus autosome (BTA) 7 for WWT, 24.3 to 25.4Mb for BTA14 for CWT, 0.5 to 1.5Mb for BTA6 for BFT and 26.3 to 33.4Mb for BTA29 for BFT. Several candidate genes (i.e. glutamate receptor, ionotropic, ampa 1 [GRIA1], family with sequence similarity 110, member B [FAM110B], and thymocyte selection-associated high mobility group box [TOX]) may be identified close to these QTL. Our result suggests that the use of different linkage disequilibrium mapping approaches can provide more reliable chromosome regions to further pinpoint DNA makers or causative genes in these regions.

Genome-wide QTL mapping of nine body composition and bone mineral density traits in pigs.

BackgroundSince the pig is one of the most important livestock animals worldwide, mapping loci that are associated with economically important traits and/or traits that influence animal welfare is extremely relevant for efficient future pig breeding. Therefore, the purpose of this study was a genome-wide mapping of quantitative trait loci (QTL) associated with nine body composition and bone mineral traits: absolute (Fat, Lean) and percentage (FatPC, LeanPC) fat and lean mass, live weight (Weight), soft tissue X-ray attenuation coefficient (R), absolute (BMC) and percentage (BMCPC) bone mineral content and bone mineral density (BMD).MethodsData on the nine traits investigated were obtained by Dual-energy X-ray absorptiometry for 551 pigs that were between 160 and 200 days old. In addition, all pigs were genotyped using Illumina’s PorcineSNP60 Genotyping BeadChip. Based on these data, a genome-wide combined linkage and linkage disequilibrium analysis was conducted. Thus, we used 44 611 sliding windows that each consisted of 20 adjacent single nucleotide polymorphisms (SNPs). For the middle of each sliding window a variance component analysis was carried out using ASReml. The underlying mixed linear model included random QTL and polygenic effects, with fixed effects of sex, housing, season and age.ResultsUsing a Bonferroni-corrected genome-wide significance threshold of P < 0.001, significant peaks were identified for all traits except BMCPC. Overall, we identified 72 QTL on 16 chromosomes, of which 24 were significantly associated with one trait only and the remaining with more than one trait. For example, a QTL on chromosome 2 included the highest peak across the genome for four traits (Fat, FatPC, LeanPC and R). The nearby gene, ZNF608, is known to be associated with body mass index in humans and involved in starvation in Drosophila, which makes it an extremely good candidate gene for this QTL.ConclusionsOur QTL mapping approach identified 72 QTL, some of which confirmed results of previous studies in pigs. However, we also detected significant associations that have not been published before and were able to identify a number of new and promising candidate genes, such as ZNF608.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0068-2) contains supplementary material, which is available to authorized users.

Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize.

Multiparental designs combined with dense genotyping of parents have been proposed as a way to increase the diversity and resolution of quantitative trait loci (QTL) mapping studies, using methods combining linkage disequilibrium information with linkage analysis (LDLA). Two new nested association mapping designs adapted to European conditions were derived from the complementary dent and flint heterotic groups of maize (Zea mays L.). Ten biparental dent families (N = 841) and 11 biparental flint families (N = 811) were genotyped with 56,110 single nucleotide polymorphism markers and evaluated as test crosses with the central line of the reciprocal design for biomass yield, plant height, and precocity. Alleles at candidate QTL were defined as (i) parental alleles, (ii) haplotypic identity by descent, and (iii) single-marker groupings. Between five and 16 QTL were detected depending on the model, trait, and genetic group considered. In the flint design, a major QTL (R(2) = 27%) with pleiotropic effects was detected on chromosome 10, whereas other QTL displayed milder effects (R(2) < 10%). On average, the LDLA models detected more QTL but generally explained lower percentages of variance, consistent with the fact that most QTL display complex allelic series. Only 15% of the QTL were common to the two designs. A joint analysis of the two designs detected between 15 and 21 QTL for the five traits. Of these, between 27 for silking date and 41% for tasseling date were significant in both groups. Favorable allelic effects detected in both groups open perspectives for improving biomass production.

Genome-wide scan reveals LEMD3 and WIF1 on SSC5 as the candidates for porcine ear size.

The quantitative trait loci (QTL) for porcine ear size was previously reported to mainly focus on SSC5 and SSC7. Recently, a missense mutation, G32E, in PPARD in the QTL interval on SSC7 was identified as the causative mutation for ear size. However, on account of the large interval of QTL, the responsible gene on SSC5 has not been identified. In this study, an intercross population was constructed from the large-eared Minzhu, an indigenous Chinese pig breed, and the Western commercial Large White pig to examine the genetic basis of ear size diversity. A GWAS was performed to detect SNPs significantly associated with ear size. Thirty-five significant SNPs defined a 10.78-Mb (30.14–40.92 Mb) region on SSC5. Further, combining linkage disequilibrium and haplotype sharing analysis, a reduced region of 3.07-Mb was obtained. Finally, by using a selective sweep analysis, a critical region of about 450-kb interval containing two annotated genes LEMD3 and WIF1 was refined in this work. Functional analysis indicated that both represent biological candidates for porcine ear size, with potential application in breeding programs. The two genes could also be used as novel references for further study of the mechanism underlying human microtia.

Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses.

Obesity is a complex condition with world-wide exponentially rising prevalence rates, linked with severe diseases like Type 2 Diabetes. Economic and welfare consequences have led to a raised interest in a better understanding of the biological and genetic background. To date, whole genome investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation of haplotype blocks. We built Weighted Interaction SNP Hub (WISH) and differentially wired (DW) networks using genotypic correlations amongst obesity-associated SNPs resulting from GWA analysis. GWA results and SNP modules detected by WISH and DW analyses were further investigated by functional enrichment analyses. The functional annotation of SNPs revealed several genes associated with obesity, e.g., NPC2 and OR4D10. Moreover, gene enrichment analyses identified several significantly associated pathways, over and above the GWA study results, that may influence obesity and obesity related diseases, e.g., metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index and employ systems genetics in a porcine model to provide important insights into the complex genetic architecture associated with obesity and many biological pathways that underlie it.

Allelic variation in a cellulose synthase gene (PtoCesA4) associated with growth and wood properties in Populus tomentosa.

Lignocellulosic biomass from trees provides a renewable feedstock for biofuels, lumber, pulp, paper, and other uses. Dissecting the mechanism underlying natural variation of the complex traits controlling growth and lignocellulose biosynthesis in trees can enable marker-assisted breeding to improve wood quality and yield. Here, we combined linkage disequilibrium (LD)-based association analysis with traditional linkage analysis to detect the genetic effect of a Populus tomentosa cellulose synthase gene, PtoCesA4. PtoCesA4 is strongly expressed in developing xylem and leaves. Nucleotide diversity and LD in PtoCesA4, sampled from the P. tomentosa natural distribution, revealed that PtoCesA4 harbors high single nucleotide polymorphism (SNP) diversity (πT = 0.0080 and θw = 0.0098) and low LD (r2 ≥ 0.1, within 1400 bp), demonstrating that the potential of a candidate-gene-based LD approach in understanding the molecular basis underlying quantitative variation in this species. By combining single SNP, multi-SNP, and haplotype-based associations in an association population of 460 individuals with single SNP linkage analysis in a family-based linkage populations (1200 individuals), we identified three strong associations (false discovery rate Q < 0.05) in both populations. These include two nonsynonymous markers (SNP49 associated with α-cellulose content and SNP59 associated with fiber width) and a noncoding marker (SNP18 associated with α-cellulose content). Variation in RNA transcript abundance among genotypic classes of SNP49 was confirmed in these two populations. Therefore, combining different methods allowed us to examine functional PtoCesA4 allelic variation underlying natural variation in complex quantitative traits related to growth and lignocellulosic biosynthesis.