Despite the successful mapping of genes involved in the determinism of numerous traits, a large part of the genetic variation remains unexplained. A possible explanation is that the simple models ...used in many studies might not properly fit the actual underlying situations. Consequently, various methods have attempted to deal with the simultaneous mapping of genomic regions, assuming that these regions might interact, leading to a complex determinism for various traits. Despite some successes, no gold standard methodology has emerged. Actually, combining several interaction mapping methods might be a better strategy, leading to positive results over a larger set of situations. Our work is a step in that direction.
We first have demonstrated why aggregating results from several distinct methods might increase the statistical power while controlling the type I error. We have illustrated the approach using 6 existing methods (namely: MDR, Boost, BHIT, KNN-MDR, MegaSNPHunter and AntEpiSeeker) on simulated and real data sets. We have used a very simple aggregation strategy: a majority vote across the best loci combinations identified by the individual methods. In order to assess the performances of our aggregation approach in problems where most individual methods tend to fail, we have simulated difficult situations where no marginal effects of individual genes exist and where genetic heterogeneity is present. we have also demonstrated the use of the strategy on real data, using a WTCCC dataset on rheumatoid arthritis. Since we have been using simplistic assumptions to infer the expected power of the aggregation method, the actual power we estimated from our simulations has turned out to be a bit smaller than theoretically expected. Results nevertheless have shown that grouping the results of several methods is advantageous in terms of power, accuracy and type I error control. Furthermore, as more methods should become available in the future, using a grouping strategy will become more advantageous since adding more methods seems to improve the performances of the aggregated method.
The aggregation of methods as a tool to detect genetic interactions is a potentially useful addition to the arsenal used in complex traits analyses.
DNA methylation is a form of epigenetic regulation, having pivotal parts in controlling cellular expansion and expression levels within genes. Although blood DNA methylation has been studied in ...humans and other species, its prominence in cattle is largely unknown. This study aimed to methodically probe the genomic methylation map of Xinjiang brown (XJB) cattle suffering from bovine respiratory disease (BRD), consequently widening cattle blood methylome ranges. Genome-wide DNA methylation profiling of the XJB blood was investigated through whole-genome bisulfite sequencing (WGBS). Many differentially methylated regions (DMRs) obtained by comparing the cases and controls groups were found within the CG, CHG, and CHH (where H is A, T, or C) sequences (16,765, 7502, and 2656, respectively), encompassing 4334 differentially methylated genes (DMGs). Furthermore, GO/KEGG analyses showed that some DMGs were involved within immune response pathways. Combining WGBS-Seq data and existing RNA-Seq data, we identified 71 significantly differentially methylated (DMGs) and expressed (DEGs) genes (
< 0.05). Next, complementary analyses identified nine DMGs (
,
,
,
,
,
,
, and
) that might be involved in the immune response of XJB cattle infected with respiratory diseases. Although further investigations are needed to confirm their exact implication in the involved immune processes, these genes could potentially be used for a marker-assisted selection of animals resistant to BRD. This study also provides new knowledge regarding epigenetic control for the bovine respiratory immune process.
Although several studies have focused on the dynamics of bacterial food community, little is known about the variability of batch production and microbial changes that occur during storage. The aim ...of the study was to characterize the microbial spoilage community of minced pork meat samples, among different food production and storage, using both 16S rRNA gene sequencing and classical microbiology. Three batches of samples were obtained from four local Belgian facilities (A-D) and stored until shelf life under food wrap (FW) and modified atmosphere packaging (MAP, CO
30%/O
70%), at constant and dynamic temperature. Analysis of 288 samples were performed by 16S rRNA gene sequencing in combination with counts of psychrotrophic and lactic acid bacteria at 22°C. At the first day of storage, different psychrotrophic counts were observed between the four food companies (Kruskal-Wallist test,
-value < 0.05). Results shown that lowest microbial counts were observed at the first day for industries D and A (4.2 ± 0.4 and 5.6 ± 0.1 log CFU/g, respectively), whereas industries B and C showed the highest results (7.5 ± 0.4 and 7.2 ± 0.4 log CFU/g). At the end of the shelf life, psychrotrophic counts for all food companies was over 7.0 log CFU/g. With metagenetics, 48 OTUs were assigned. At the first day, the genus
(86.7 and 19.9% for food industries A and C, respectively) and
(38.7 and 25.7% for food companies B and D, respectively) were dominant. During the storage, a total of 12 dominant genera (>5% in relative abundance) were identified in MAP and 7 in FW.
was more present in FW and this genus was potentially replaced by
in MAP (two-sided Welch's
-test,
-value < 0.05). Also, a high Bray-Curtis dissimilarity in genus relative abundance was observed between food companies and batches. Although the bacteria consistently dominated the microbiota in our samples are known, results indicated that bacterial diversity needs to be addressed on the level of food companies, batches variation and food storage conditions. Present data illustrate that the combined approach provides complementary results on microbial dynamics in minced pork meat samples, considering batches and packaging variations.
In the present study, juvenile striped catfish (
Pangasianodon hypophthalmus
), a freshwater fish species, have been chronically exposed to a salinity gradient from freshwater to 20 psu (practical ...salinity unit) and were sampled at the beginning (D20) and the end (D34) of exposure. The results revealed that the intestinal microbial profile of striped catfish reared in freshwater conditions were dominated by the phyla
Bacteroidetes
,
Firmicutes
,
Proteobacteria
, and
Verrucomicrobia
. Alpha diversity measures (observed OTUs (operational taxonomic units), Shannon and Faith’s PD (phylogenetic diversity)) showed a decreasing pattern as the salinities increased, except for the phylogenetic diversity at D34, which was showing an opposite trend. Furthermore, the beta diversity between groups was significantly different.
Vibrio
and
Akkermansia
genera were affected differentially with increasing salinity, the former being increased while the latter was decreased. The genus
Sulfurospirillium
was found predominantly in fish submitted to salinity treatments. Regarding the host response, the fish intestine likely contributed to osmoregulation by modifying the expression of osmoregulatory genes such as
nka1a
,
nka1b
,
slc12a1
,
slc12a2
,
cftr
, and
aqp1
, especially in fish exposed to 15 and 20 psu. The expression of heat shock proteins (hsp)
hsp60
,
hsp70
, and
hsp90
was significantly increased in fish reared in 15 and 20 psu. On the other hand, the expression of pattern recognition receptors (PRRs) were inhibited in fish exposed to 20 psu at D20. In conclusion, the fish intestinal microbiota was significantly disrupted in salinities higher than 10 psu and these effects were proportional to the exposure time. In addition, the modifications of intestinal gene expression related to ion exchange and stressful responses may help the fish to adapt hyperosmotic environment.
Key points
• It is the first study to provide detailed information on the gut microbiota of fish using the amplicon sequencing method.
• Salinity environment significantly modified the intestinal microbiota of striped catfish.
• Intestinal responses may help the fish adapt to hyperosmotic environment.
To identify novel susceptibility loci for Crohn disease (CD), we undertook a genome-wide association study with more than 300,000 SNPs characterized in 547 patients and 928 controls. We found three ...chromosome regions that provided evidence of disease association with p-values between 10(-6) and 10(-9). Two of these (IL23R on Chromosome 1 and CARD15 on Chromosome 16) correspond to genes previously reported to be associated with CD. In addition, a 250-kb region of Chromosome 5p13.1 was found to contain multiple markers with strongly suggestive evidence of disease association (including four markers with p < 10(-7)). We replicated the results for 5p13.1 by studying 1,266 additional CD patients, 559 additional controls, and 428 trios. Significant evidence of association (p < 4 x 10(-4)) was found in case/control comparisons with the replication data, while associated alleles were over-transmitted to affected offspring (p < 0.05), thus confirming that the 5p13.1 locus contributes to CD susceptibility. The CD-associated 250-kb region was saturated with 111 SNP markers. Haplotype analysis supports a complex locus architecture with multiple variants contributing to disease susceptibility. The novel 5p13.1 CD locus is contained within a 1.25-Mb gene desert. We present evidence that disease-associated alleles correlate with quantitative expression levels of the prostaglandin receptor EP4, PTGER4, the gene that resides closest to the associated region. Our results identify a major new susceptibility locus for CD, and suggest that genetic variants associated with disease risk at this locus could modulate cis-acting regulatory elements of PTGER4.
Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved ...issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field. We propose a novel approach combining K-Nearest Neighbors (KNN) and Multi Dimensional Reduction (MDR) methods for detecting gene-gene interactions as a possible alternative to existing algorithms, e especially in situations where the number of involved determinants is high. After describing the approach, a comparison of our method (KNN-MDR) to a set of the other most performing methods (i.e., MDR, BOOST, BHIT, MegaSNPHunter and AntEpiSeeker) is carried on to detect interactions using simulated data as well as real genome-wide data.
Experimental results on both simulated data and real genome-wide data show that KNN-MDR has interesting properties in terms of accuracy and power, and that, in many cases, it significantly outperforms its recent competitors.
The presented methodology (KNN-MDR) is valuable in the context of loci and interactions mapping and can be seen as an interesting addition to the arsenal used in complex traits analyses.
Identity-by-descent probabilities are important for many applications in genetics. Here we propose a method for modeling the transmission of the haplotypes from the closest genotyped relatives along ...an entire chromosome. The method relies on a hidden Markov model where hidden states correspond to the set of all possible origins of a haplotype within a given pedigree. Initial state probabilities are estimated from average genetic contribution of each origin to the modeled haplotype while transition probabilities are computed from recombination probabilities and pedigree relationships between the modeled haplotype and the various possible origins. The method was tested on three simulated scenarios based on real data sets from dairy cattle, Arabidopsis thaliana, and maize. The mean identity-by-descent probabilities estimated for the truly inherited parental chromosome ranged from 0.94 to 0.98 according to the design and the marker density. The lowest values were observed in regions close to crossing over or where the method was not able to discriminate between several origins due to their similarity. It is shown that the estimated probabilities were correctly calibrated. For marker imputation (or QTL allele prediction for fine mapping or genomic selection), the method was efficient, with 3.75% allelic imputation error rates on a dairy cattle data set with a low marker density map (1 SNP/Mb). The method should prove useful for situations we are facing now in experimental designs and in plant and animal breeding, where founders are genotyped with relatively high markers densities and last generation(s) genotyped with a lower-density panel.
Inbreeding coefficients can be estimated either from pedigree data or from genomic data, and with genomic data, they are either global or local (when the linkage map is used). Recently, we developed ...a new hidden Markov model (HMM) that estimates probabilities of homozygosity-by-descent (HBD) at each marker position and automatically partitions autozygosity in multiple age-related classes (based on the length of HBD segments). Our objectives were to: (1) characterize inbreeding with our model in an intensively selected population such as the Belgian Blue Beef (BBB) cattle breed; (2) compare the properties of the model at different marker densities; and (3) compare our model with other methods.
When using 600 K single nucleotide polymorphisms (SNPs), the inbreeding coefficient (probability of sampling an HBD locus in an individual) was on average 0.303 (ranging from 0.258 to 0.375). HBD-classes associated to historical ancestors (with small segments ≤ 200 kb) accounted for 21.6% of the genome length (71.4% of the total length of the genome in HBD segments), whereas classes associated to more recent ancestors accounted for only 22.6% of the total length of the genome in HBD segments. However, these recent classes presented more individual variation than more ancient classes. Although inbreeding coefficients obtained with low SNP densities (7 and 32 K) were much lower (0.060 and 0.093), they were highly correlated with those obtained at higher density (r = 0.934 and 0.975, respectively), indicating that they captured most of the individual variation. At higher SNP density, smaller HBD segments are identified and, thus, more past generations can be explored. We observed very high correlations between our estimates and those based on homozygosity (r = 0.95) or on runs-of-homozygosity (r = 0.95). As expected, pedigree-based estimates were mainly correlated with recent HBD-classes (r = 0.56).
Although we observed high levels of autozygosity associated with small HBD segments in BBB cattle, recent inbreeding accounted for most of the individual variation. Recent autozygosity can be captured efficiently with low-density SNP arrays and relatively simple models (e.g., two HBD classes). The HMM framework provides local HBD probabilities that are still useful at lower SNP densities.
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