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Fatty acid bing protein 4 (FABP4) plays a key role in fat regulation in mammals and is a strong positional candidate gene for the FAT1 quantitative trait locus located on porcine chromosome 4. DNA resequencing of file FABP4 gene region in 23 pigs from 10 breeds and wild boar revealed 134 variants in 6.4 kb, representing a silent nucleotide diversity of [[pi].sub.s] = 0.01, much higher than reported so far in animal domestic species. Moreover, this diversity was highly structured. Also strikingly, the FABP4 phylogenetic tree did not show any geographical or breed origin clustering, with distant breeds sharing similar haplotypes and some of the most heterozygous samples pertaining to highly inbred animals like Iberian Guadyerbas (inbreeding coefficient ~0.3) or British Tamworth. In contrast, the cytochrome b (mtDNA) phylogenetic tree was coherent with geographical origin. The estimated age of the most recent common ancestor for the most divergent Iberian or Tamworth haplotypes was much older than domestication. An additional panel of 100 pigs from 8 different breeds and wild boar from Spain, Tunisia, Sardinia, and Japan was genotyped for seven selected single nucleotide polymorphisms and shows that high variability at the porcine FABP4 is the rule rather than the exception. Pig populations, even highly inbred, can maintain high levels of variability for surprisingly long periods of time.

Dissecting the genetic architecture of regulatory elements on a genome-wide basis is now technically feasible. The potential medical and genetical implications of this kind of experiment being very large, it is paramount to assess the reliability and repeatability of the results. This is especially relevant in outbred populations, such as humans, where the genetic architecture is necessarily more complex than in crosses between inbred lines. Here we simulated a chromosome-wide SNP association study using real human microarray data. Our model predicted, as observed, a highly significant clustering of quantitative trait loci (QTL) for gene expression. Importantly, the estimates of QTL positions were often unstable, and a decrease in the number of individuals of 16% resulted in a loss of power of ~30% and a large shift in the position estimate in ~30-40% of the remaining significant QTL. We also found that the analysis of two repeated measures of the same mRNA can also result in two QTL that are located far apart. The intrinsic difficulties of analyzing outbred populations should not be underestimated. We anticipate that (many) conflicting results may be collected in the future if whole-genome association studies for mRNA levels are carried out in outbred populations.

A method for mapping complex trait genes using cDNA microarray and molecular marker data jointly is presented and illustrated via simulation. We introduce a novel approach for simulating phenotypes and genotypes conditionally on real, publicly available, microarray data. The model assumes an underlying continuous latent variable (liability) related to some measured cDNA expression levels. Partial least-squares logistic regression is used to estimate the liability under several scenarios where the level of gene interaction, the gene effect, and the number of cDNA levels affecting liability are varied. The results suggest that: (1) the usefulness of microarray data for gene mapping increases when both the number of cDNA levels in the underlying liability and the QTL effect decrease and when genes are coexpressed; (2) the correlation between estimated and true liability is large, at least under our simulation settings; (S) it is unlikely that cDNA clones identified as significant with partial least squares (or with some other technique) are the true responsible cDNAs, especially as the number of clones in the liability increases; (4) the number of putatively significant cDNA levels increases critically if cDNAs are coexpressed in a cluster (however, the proportion of true causal cDNAs within the significant ones is similar to that in a no-coexpression scenario); and (5) data reduction is needed to smooth out the variability encountered in expression levels when these are analyzed individually.

We present a Bayesian method that combines linkage and linkage disequilibrium (LDL) information for quantitative trait locus (QTL) mapping. This method uses jointly all marker information (haplotypes) and all available pedigree information; i.e., it is not restricted to any specific experimental design and it is not required that phases are known. Infinitesimal genetic effects or environmental noise ('fixed') effects can equally be fitted. A diallelic QTL is assumed and both additive and dominant effects can be estimated. We have implemented a combined Gibbs/Metropolis-Hastings sampling to obtain the marginal posterior distributions of the parameters of interest. We have also implemented a Bayesian variant of usual disequilibrium measures like D' and [r.sup.2] between QTL and markers. We illustrate the method with simulated data in 'simple' (two-generation full-sib families) and 'complex' (four-generation) pedigrees. We compared the estimates with and without using linkage disequilibrium information. In general, using LDL resulted in estimates of QTL position that were much better than linkage-only estimates when there was complete disequilibrium between the mutant QTL allele and the marker. This advantage, however, decreased when the association was only partial. In all cases, additive and dominant effects were estimated accurately either with or without disequilibrium information.

Marked-assisted genetic improvement of agricultural species exploits statistical dependencies in the joint distribution of marker genotypes and quantitative traits. An issue is how molecular (e.g., dense marker maps) and phenotypic information (e.g., some measure of yield in plants) is to be used for predicting the genetic value of candidates for selection. Multiple regression, selection index techniques, best linear unbiased prediction, and ridge regression of phenotypes on marker genotypes have been suggested, as well as more elaborate methods. Here, phenotype-marker associations are modeled hierarchically via multilevel models including chromosomal effects, a spatial covariance of marked effects within chromosomes, background genetic variability, and family heterogeneity. Lorenz curves and Gini coefficients are suggested for assessing the inequality of the contribution of different marked effects to genetic variability. Classical and Bayesian methods are presented. The Bayesian approach includes a Markov chain Monte Carlo implementation. The generality and flexibility of the Bayesian method is illustrated when a Lorenz curve is to be inferred.

We present a very flexible method that allows us to analyze X-linked quantitative trait loci (QTL) in crosses between outbred lines. The dosage compensation phenomenon is modeled explicitly in an identity-by-descent approach. A variety of models can be fitted, ranging from considering alternative fixed alleles within the founder breeds to a model where the only genetic variation is within breeds, as well as mixed models. Different genetic variances within each founder breed can be estimated. We illustrate the method with data from an [F.sub.2] cross between Iberian x Landrace pigs for intramuscular fat content and meat color component a*. The Iberian allele exhibited a strong overdominant effect for intramuscular fat in females. There was also limited evidence of one or more regions affecting color component a*. The analysis suggested that the QTL alleles were fixed in the Iberian founders, whereas there was some evidence of segregation in Landrace for the QTL affecting a* color component.

Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH4) and dry matter intake (DMI) were individually measured over 4–6 weeks to calculate the CH4 yield (CH4y = CH4/DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH4y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl‐coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least‐squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH4y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH4 emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH4y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane‐reduction selection programmes in the dairy cattle industry provided they are heritable. [ABSTRACT FROM AUTHOR]

We provide a theoretical framework for quantitative trait locus (QTL) analysis of a crossed population where parental lines may be outbred and dominance as well as inbreeding are allowed for. It can be applied to any pedigree. A biallelic QTL is assumed, and the QTL allele frequencies can be different in each breed. The genetic covariance between any two individuals is expressed as a nonlinear function of the probability of up to 15 possible identity modes and of the additive and dominance effects, together with the allelic frequencies in each of the two parental breeds. The probabilities of each identity mode are obtained at the desired genome positions using a Monte Carlo Markov chain method. Unbiased estimates of the actual genetic parameters are recovered in a simulated [F.sub.2] cross and in a six-generation complex pedigree under a variety of genetic models (allele fixed or segregating in the parental populations and additive or dominance action). Results from analyzing an [F.sub.2] cross between Meishan and Large White pigs are also presented.

We develop a mixed-model approach for QTL analysis in crosses between outbred lines that allows for QTL segregation within lines as well as for differences in mean QTL effects between lines. We also propose a method called 'segment mapping' that is based in partitioning the genome in a series of segments. The expected change in mean according to percentage of breed origin, together with the genetic variance associated with each segment, is estimated using maximum likelihood. The method also allows the estimation of differences in additive variances between the parental lines. Completely fixed random and mixed models together with segment mapping are compared via simulation. The segment mapping and mixed-model behaviors are similar to those of classical methods, either the fixed or random models, under simple genetic models (a single QTL with alternative alleles fixed in each line), whereas they provide less biased estimates and have higher power than fixed or random models in more complex situations, i.e., when the QTL are segregating within the parental lines. The segment mapping approach is particularly useful to determining which chromosome regions are likely to contain QTL when these are linked.

Background: Pigs were domesticated independently in Eastern and Western Eurasia early during the agricultural revolution, and have since been transported and traded across the globe. Here, we present a worldwide survey on 60K genome-wide single nucleotide polymorphism (SNP) data for 2093 pigs, including 1839 domestic pigs representing 122 local and commercial breeds, 215 wild boars, and 39 out-group suids, from Asia, Europe, America, Oceania and Africa. The aim of this study was to infer global patterns in pig domestication and diversity related to demography, migration, and selection. Results: A deep phylogeographic division reflects the dichotomy between early domestication centers. In the core Eastern and Western domestication regions, Chinese pigs show differentiation between breeds due to geographic isolation, whereas this is less pronounced in European pigs. The inferred European origin of pigs in the Americas, Africa, and Australia reflects European expansion during the sixteenth to nineteenth centuries. Human-mediated introgression, which is due, in particular, to importing Chinese pigs into the UK during the eighteenth and nineteenth centuries, played an important role in the formation of modern pig breeds. Inbreeding levels vary markedly between populations, from almost no runs of homozygosity (ROH) in a number of Asian wild boar populations, to up to 20% of the genome covered by ROH in a number of Southern European breeds. Commercial populations show moderate ROH statistics. For domesticated pigs and wild boars in Asia and Europe, we identified highly differentiated loci that include candidate genes related to muscle and body development, central nervous system, reproduction, and energy balance, which are putatively under artificial selection. Conclusions: Key events related to domestication, dispersal, and mixing of pigs from different regions are reflected in the 60K SNP data, including the globalization that has recently become full circle since Chinese pig breeders in the past decades started selecting Western breeds to improve local Chinese pigs. Furthermore, signatures of ongoing and past selection, acting at different times and on different genetic backgrounds, enhance our insight in the mechanism of domestication and selection. The global diversity statistics presented here highlight concerns for maintaining agrodiversity, but also provide a necessary framework for directing genetic conservation. [ABSTRACT FROM AUTHOR]