Recent artificial selection in U.S. Jersey cattle impacts autozygosity levels of specific genomic regions

Background: Genome signatures of artificial selection in U.S. Jersey cattle were identified by examining changes in haplotype homozygosity for a resource population of animals born between 1953 and 2007. Genetic merit of this population changed dramatically during this period for a number of traits, especially milk yield. The intense selection underlying these changes was achieved through extensive use of artificial insemination (AI), which also increased consanguinity of the population to a few superior Jersey bulls. As a result, allele frequencies are shifted for many contemporary animals, and in numerous cases to a homozygous state for specific genomic regions. The goal of this study was to identify those selection signatures that occurred after extensive use of AI since the 1960, using analyses of shared haplotype segments or Runs of Homozygosity. When combined with animal birth year information, signatures of selection associated with economically important traits were identified and compared to results from an extended haplotype homozygosity analysis. Results: Overall, our results reveal that more recent selection increased autozygosity across the entire genome, but some specific regions increased more than others. A genome-wide scan identified more than 15 regions with a substantial change in autozygosity. Haplotypes found to be associated with increased milk, fat and protein yield in U.S. Jersey cattle also consistently increased in frequency. Conclusions: The analyses used in this study was able to detect directional selection over the last few decades when individual production records for Jersey animals were available

A quantitatively-modeled homozygosity mapping algorithm, qHomozygosityMapping, utilizing whole genome single nucleotide polymorphism genotyping data

Homozygosity mapping is a powerful procedure that is capable of detecting recessive disease-causing genes in a few patients from families with a history of inbreeding. We report here a homozygosity mapping algorithm for high-density single nucleotide polymorphism arrays that is able to (i) correct genotyping errors, (ii) search for autozygous segments genome-wide through regions with runs of homozygous SNPs, (iii) check the validity of the inbreeding history, and (iv) calculate the probability of the disease-causing gene being located in the regions identified. The genotyping error correction restored an average of 94.2% of the total length of all regions with run of homozygous SNPs, and 99.9% of the total length of them that were longer than 2 cM. At the end of the analysis, we would know the probability that regions identified contain a disease-causing gene, and we would be able to determine how much effort should be devoted to scrutinizing the regions. We confirmed the power of this algorithm using 6 patients with Siiyama-type α1-antitrypsin deficiency, a rare autosomal recessive disease in Japan. Our procedure will accelerate the identification of disease-causing genes using high-density SNP array data

Homozygosity Mapping on Homozygosity Haplotype Analysis to Detect Recessive Disease-Causing Genes from a Small Number of Unrelated, Outbred Patients

Genes involved in disease that are not common are often difficult to identify; a method that pinpoints them from a small number of unrelated patients will be of great help. In order to establish such a method that detects recessive genes identical-by-descent, we modified homozygosity mapping (HM) so that it is constructed on the basis of homozygosity haplotype (HM on HH) analysis. An analysis using 6 unrelated patients with Siiyama-type α1-antitrypsin deficiency, a disease caused by a founder gene, the correct gene locus was pinpointed from data of any 2 patients (length: 1.2–21.8 centimorgans, median: 1.6 centimorgans). For a test population in which these 6 patients and 54 healthy subjects were scrambled, the approach accurately identified these 6 patients and pinpointed the locus to a 1.4-centimorgan fragment. Analyses using synthetic data revealed that the analysis works well for IBD fragment derived from a most recent common ancestor (MRCA) who existed less than 60 generations ago. The analysis is unsuitable for the genes with a frequency in general population more than 0.1. Thus, HM on HH analysis is a powerful technique, applicable to a small number of patients not known to be related, and will accelerate the identification of disease-causing genes for recessive conditions

Electron quantum metamaterials in van der Waals heterostructures

In recent decades, scientists have developed the means to engineer synthetic periodic arrays with feature sizes below the wavelength of light. When such features are appropriately structured, electromagnetic radiation can be manipulated in unusual ways, resulting in optical metamaterials whose function is directly controlled through nanoscale structure. Nature, too, has adopted such techniques -- for example in the unique coloring of butterfly wings -- to manipulate photons as they propagate through nanoscale periodic assemblies. In this Perspective, we highlight the intriguing potential of designer sub-electron wavelength (as well as wavelength-scale) structuring of electronic matter, which affords a new range of synthetic quantum metamaterials with unconventional responses. Driven by experimental developments in stacking atomically layered heterostructures -- e.g., mechanical pick-up/transfer assembly -- atomic scale registrations and structures can be readily tuned over distances smaller than characteristic electronic length-scales (such as electron wavelength, screening length, and electron mean free path). Yet electronic metamaterials promise far richer categories of behavior than those found in conventional optical metamaterial technologies. This is because unlike photons that scarcely interact with each other, electrons in subwavelength structured metamaterials are charged, and strongly interact. As a result, an enormous variety of emergent phenomena can be expected, and radically new classes of interacting quantum metamaterials designed

A Simple Method for Combining Genetic Mapping Data from Multiple Crosses and Experimental Designs

Over the past decade many linkage studies have defined chromosomal intervals containing polymorphisms that modulate a variety of traits. Many phenotypes are now associated with enough mapping data that meta-analysis could help refine locations of known QTLs and detect many novel QTLs.We describe a simple approach to combining QTL mapping results for multiple studies and demonstrate its utility using two hippocampus weight loci. Using data taken from two populations, a recombinant inbred strain set and an advanced intercross population we demonstrate considerable improvements in significance and resolution for both loci. 1-LOD support intervals were improved 51% for Hipp1a and 37% for Hipp9a. We first generate locus-wise permuted P-values for association with the phenotype from multiple maps, which can be done using a permutation method appropriate to each population. These results are then assigned to defined physical positions by interpolation between markers with known physical and genetic positions. We then use Fisher's combination test to combine position-by-position probabilities among experiments. Finally, we calculate genome-wide combined P-values by generating locus-specific P-values for each permuted map for each experiment. These permuted maps are then sampled with replacement and combined. The distribution of best locus-specific P-values for each combined map is the null distribution of genome-wide adjusted P-values.Our approach is applicable to a wide variety of segregating and non-segregating mapping populations, facilitates rapid refinement of physical QTL position, is complementary to other QTL fine mapping methods, and provides an appropriate genome-wide criterion of significance for combined mapping results

Efficient and Accurate Construction of Genetic Linkage Maps from the Minimum Spanning Tree of a Graph

Genetic linkage maps are cornerstones of a wide spectrum of biotechnology applications, including map-assisted breeding, association genetics, and map-assisted gene cloning. During the past several years, the adoption of high-throughput genotyping technologies has been paralleled by a substantial increase in the density and diversity of genetic markers. New genetic mapping algorithms are needed in order to efficiently process these large datasets and accurately construct high-density genetic maps. In this paper, we introduce a novel algorithm to order markers on a genetic linkage map. Our method is based on a simple yet fundamental mathematical property that we prove under rather general assumptions. The validity of this property allows one to determine efficiently the correct order of markers by computing the minimum spanning tree of an associated graph. Our empirical studies obtained on genotyping data for three mapping populations of barley (Hordeum vulgare), as well as extensive simulations on synthetic data, show that our algorithm consistently outperforms the best available methods in the literature, particularly when the input data are noisy or incomplete. The software implementing our algorithm is available in the public domain as a web tool under the name MSTmap

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand

Evolutionary Epidemiology of Drug-Resistance in Space

The spread of drug-resistant parasites erodes the efficacy of therapeutic treatments against many infectious diseases and is a major threat of the 21st century. The evolution of drug-resistance depends, among other things, on how the treatments are administered at the population level. “Resistance management” consists of finding optimal treatment strategies that both reduce the consequence of an infection at the individual host level, and limit the spread of drug-resistance in the pathogen population. Several studies have focused on the effect of mixing different treatments, or of alternating them in time. Here, we analyze another strategy, where the use of the drug varies spatially: there are places where no one receives any treatment. We find that such a spatial heterogeneity can totally prevent the rise of drug-resistance, provided that the size of treated patches is below a critical threshold. The range of parasite dispersal, the relative costs and benefits of being drug-resistant compared to being drug-sensitive, and the duration of an infection with drug-resistant parasites are the main factors determining the value of this threshold. Our analysis thus provides some general guidance regarding the optimal spatial use of drugs to prevent or limit the evolution of drug-resistance

Genetic linkage analysis in the age of whole-genome sequencing

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data

A second generation genetic map for rainbow trout (Oncorhynchus mykiss)

<p>Abstract</p> <p>Background</p> <p>Genetic maps characterizing the inheritance patterns of traits and markers have been developed for a wide range of species and used to study questions in biomedicine, agriculture, ecology and evolutionary biology. The status of rainbow trout genetic maps has progressed significantly over the last decade due to interest in this species in aquaculture and sport fisheries, and as a model research organism for studies related to carcinogenesis, toxicology, comparative immunology, disease ecology, physiology and nutrition. We constructed a second generation genetic map for rainbow trout using microsatellite markers to facilitate the identification of quantitative trait loci for traits affecting aquaculture production efficiency and the extraction of comparative information from the genome sequences of model fish species.</p> <p>Results</p> <p>A genetic map ordering 1124 microsatellite loci spanning a sex-averaged distance of 2927.10 cM (Kosambi) and having 2.6 cM resolution was constructed by genotyping 10 parents and 150 offspring from the National Center for Cool and Cold Water Aquaculture (NCCCWA) reference family mapping panel. Microsatellite markers, representing pairs of loci resulting from an evolutionarily recent whole genome duplication event, identified 180 duplicated regions within the rainbow trout genome. Microsatellites associated with genes through expressed sequence tags or bacterial artificial chromosomes produced comparative assignments with tetraodon, zebrafish, fugu, and medaka resulting in assignments of homology for 199 loci.</p> <p>Conclusion</p> <p>The second generation NCCCWA genetic map provides an increased microsatellite marker density and quantifies differences in recombination rate between the sexes in outbred populations. It has the potential to integrate with cytogenetic and other physical maps, identifying paralogous regions of the rainbow trout genome arising from the evolutionarily recent genome duplication event, and anchoring a comparative map with the zebrafish, medaka, tetraodon, and fugu genomes. This resource will facilitate the identification of genes affecting traits of interest through fine mapping and positional cloning of candidate genes.</p