On the need for a control line in selection experiments: A likelihood analysis

The question of whether selection experiments ought to include a control line, as opposed to investing all facilities in a single selected line, is addressed using a likelihood perspective. The consequences of using a control line are evaluated under two scenarios. In the first one, environmental trend is modeled and inferred from the data. In this case, a control line is shown to be highly beneficial in terms of the efficiency of inferences about eheritability and response to selection. In the second scenario, environmental trend is not modeled. One can imagine that a previous analysis of the experimental data had lent support to this decision. It is shown that in this situation where a control line may seem superfluous, inclusion of a control line can result in minor gains in efficiency if a high selection intensity is practiced in the selected line. Further, if there is a loss, it is moderately small. The results are verified to hold under more complicated data structures via Monte Carlo simulation. For completeness, divergent selection designs are also reviewed, and inferences based on a conditional and full likelihood approach are contrasted

Using singleton densities to detect recent selection in Bos taurus

Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the “Singleton Density Score” (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural‐cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans—milk protein content, milk fat content, and stature—have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.

BACKGROUND: Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. RESULTS: We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance. CONCLUSION: We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users

The efficiency of mapping of quantitative trait loci using cofactor analysis in half-sib design

This simulation study was designed to study the power and type I error rate in QTL mapping using cofactor analysis in half-sib designs. A number of scenarios were simulated with different power to identify QTL by varying family size, heritability, QTL effect and map density, and three threshold levels for cofactor were considered. Generally cofactor analysis did not increase the power of QTL mapping in a half-sib design, but increased the type I error rate. The exception was with small family size where the number of correctly identified QTL increased by 13% when heritability was high and 21% when heritability was low. However, in the same scenarios the number of false positives increased by 49% and 45% respectively. With a liberal threshold level of 10% for cofactor combined with a low heritability, the number of correctly identified QTL increased by 14% but there was a 41% increase in the number of false positives. Also, the power of QTL mapping did not increase with cofactor analysis in scenarios with unequal QTL effect, sparse marker density and large QTL effect (25% of the genetic variance), but the type I error rate tended to increase. A priori, cofactor analysis was expected to have higher power than individual chromosome analysis especially in experiments with lower power to detect QTL. Our study shows that cofactor analysis increased the number of false positives in all scenarios with low heritability and the increase was up to 50% in low power experiments and with lower thresholds for cofactors

Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers

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Sampling genotypes in large pedigrees with loops

Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Scalar-Gibbs is easy to implement, and it is widely used in genetics. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. These problems do not arise if the genotypes are sampled jointly from the entire pedigree. This paper proposes a method to jointly sample genotypes. The method combines the Elston-Stewart algorithm and iterative peeling, and is called the ESIP sampler. For a hypothetical pedigree, genotype probabilities are estimated from samples obtained using ESIP and also scalar-Gibbs. Approximate probabilities were also obtained by iterative peeling. Comparisons of these with exact genotypic probabilities obtained by the Elston-Stewart algorithm showed that ESIP and iterative peeling yielded genotypic probabilities that were very close to the exact values. Nevertheless, estimated probabilities from scalar-Gibbs with a chain of length 235 000, including a burn-in of 200 000 steps, were less accurate than probabilities estimated using ESIP with a chain of length 10 000, with a burn-in of 5 000 steps. The effective chain size (ECS) was estimated from the last 25 000 elements of the chain of length 125 000. For one of the ESIP samplers, the ECS ranged from 21 579 to 22 741, while for the scalar-Gibbs sampler, the ECS ranged from 64 to 671. Genotype probabilities were also estimated for a large real pedigree consisting of 3 223 individuals. For this pedigree, it is not feasible to obtain exact genotype probabilities by the Elston-Stewart algorithm. ESIP and iterative peeling yielded very similar results. However, results from scalar-Gibbs were less accurate

Assessing the genetic background and genomic relatedness of red cattle populations originating from Northern Europe

Background Local cattle breeds need special attention, as they are valuable reservoirs of genetic diversity. Appropriate breeding decisions and adequate genomic management of numerically smaller populations are required for their conservation. At this point, the analysis of dense genome-wide marker arrays provides encompassing insights into the genomic constitution of livestock populations. We have analyzed the genetic characterization of ten cattle breeds originating from Germany, The Netherlands and Denmark belonging to the group of red dairy breeds in Northern Europe. The results are intended to provide initial evidence on whether joint genomic breeding strategies of these populations will be successful. Results Traditional Danish Red and Groningen White-Headed were the most genetically differentiated breeds and their populations showed the highest levels of inbreeding. In contrast, close genetic relationships and shared ancestry were observed for the populations of German Red and White Dual-Purpose, Dutch Meuse-Rhine-Yssel, and Dutch Deep Red breeds, reflecting their common histories. A considerable amount of gene flow from Red Holstein to German Angler and to German Red and White Dual-Purpose was revealed, which is consistent with frequent crossbreeding to improve productivity of these local breeds. In Red Holstein, marked genomic signatures of selection were reported on chromosome 18, suggesting directed selection for important breeding goal traits. Furthermore, tests for signatures of selection between Red Holstein, Red and White Dual-Purpose, and Meuse-Rhine-Yssel uncovered signals for all investigated pairs of populations. The corresponding genomic regions, which were putatively under different selection pressures, harboured various genes which are associated with traits such as milk and beef production, mastitis and female fertility. This study provides comprehensive knowledge on the genetic constitution and genomic connectedness of divergent red cattle populations in Northern Europe. The results will help to design and optimize breeding strategies. A joint genomic evaluation including some of the breeds studied here seems feasible

Irreducibility and efficiency of ESIP to sample marker genotypes in large pedigrees with loops

Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Among MCMC methods, scalar-Gibbs is the easiest to implement, and it is used in genetics. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. Joint sampling of genotypes has been proposed as a strategy to overcome these problems. An algorithm that combines the Elston-Stewart algorithm and iterative peeling (ESIP sampler) to sample genotypes jointly from the entire pedigree is used in this study. Here, it is shown that the ESIP sampler yields an irreducible Markov chain, regardless of the number of alleles at a locus. Further, results obtained by ESIP sampler are compared with other methods in the literature. Of the methods that are guaranteed to be irreducible, ESIP was the most efficient