bWGR: Bayesian Whole-Genome Regression.

MOTIVATION: Whole-genome regressions methods represent a key framework for genome-wide prediction, cross-validation studies, and association analysis. The bWGR offers a compendium of Bayesian methods with various priors available, allowing users to predict complex traits with different genetic architectures. RESULTS: Here we introduce bWGR, an R package that enables users to efficient fit and cross-validate Bayesian and likelihood whole-genome regression methods. It implements a series of methods referred to as the Bayesian alphabet under the traditional Gibbs sampling and optimized Expectation-Maximization. The package also enables fitting efficient multivariate models and complex hierarchical models. The package is user-friendly and computational efficient. AVAILABILITY AND IMPLEMENTATION: bWGR is an R package available in the CRAN repository. It can be installed in R by typing: install.packages("bWGR"). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High Resolution Genomic Scans Reveal Genetic Architecture Controlling Alcohol Preference in Bidirectionally Selected Rat Model.

Investigations on the influence of nature vs. nurture on Alcoholism (Alcohol Use Disorder) in human have yet to provide a clear view on potential genomic etiologies. To address this issue, we sequenced a replicated animal model system bidirectionally-selected for alcohol preference (AP). This model is uniquely suited to map genetic effects with high reproducibility, and resolution. The origin of the rat lines (an 8-way cross) resulted in small haplotype blocks (HB) with a corresponding high level of resolution. We sequenced DNAs from 40 samples (10 per line of each replicate) to determine allele frequencies and HB. We achieved ~46X coverage per line and replicate. Excessive differentiation in the genomic architecture between lines, across replicates, termed signatures of selection (SS), were classified according to gene and region. We identified SS in 930 genes associated with AP. The majority (50%) of the SS were confined to single gene regions, the greatest numbers of which were in promoters (284) and intronic regions (169) with the least in exon's (4), suggesting that differences in AP were primarily due to alterations in regulatory regions. We confirmed previously identified genes and found many new genes associated with AP. Of those newly identified genes, several demonstrated neuronal function involved in synaptic memory and reward behavior, e.g. ion channels (Kcnf1, Kcnn3, Scn5a), excitatory receptors (Grin2a, Gria3, Grip1), neurotransmitters (Pomc), and synapses (Snap29). This study not only reveals the polygenic architecture of AP, but also emphasizes the importance of regulatory elements, consistent with other complex traits.

The cellular and molecular etiology of the craniofacial defects in the avian ciliopathic mutant talpid2.

talpid(2) is an avian autosomal recessive mutant with a myriad of congenital malformations, including polydactyly and facial clefting. Although phenotypically similar to talpid(3), talpid(2) has a distinct facial phenotype and an unknown cellular, molecular and genetic basis. We set out to determine the etiology of the craniofacial phenotype of this mutant. We confirmed that primary cilia were disrupted in talpid(2) mutants. Molecularly, we found disruptions in Hedgehog signaling. Post-translational processing of GLI2 and GLI3 was aberrant in the developing facial prominences. Although both GLI2 and GLI3 processing were disrupted in talpid(2) mutants, only GLI3 activator levels were significantly altered in the nucleus. Through additional fine mapping and whole-genome sequencing, we determined that the talpid(2) phenotype was linked to a 1.4 Mb region on GGA1q that contained the gene encoding the ciliary protein C2CD3. We cloned the avian ortholog of C2CD3 and found its expression was ubiquitous, but most robust in the developing limbs and facial prominences. Furthermore, we found that C2CD3 is localized proximal to the ciliary axoneme and is important for docking the mother centriole to the ciliary vesicle and cell membrane. Finally, we identified a 19 bp deletion in talpid(2) C2CD3 that produces a premature stop codon, and thus a truncated protein, as the likely causal allele for the phenotype. Together, these data provide insight into the cellular, molecular and genetic etiology of the talpid(2) phenotype. Our data suggest that, although the talpid(2) and talpid(3) mutations affect a common ciliogenesis pathway, they are caused by mutations in different ciliary proteins that result in differences in craniofacial phenotype.

Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans.

BACKGROUND: Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits. The imputation methods we evaluated were as follows: multivariate mixed model, hidden Markov model, logical algorithm, k-nearest neighbor, single value decomposition, and random forest. We used raw genotypes from the SoyNAM project and the following phenotypes: plant height, days to maturity, grain yield, and seed protein composition. RESULTS: We propose an imputation method based on multivariate mixed models using pedigree information. Our methods comparison indicate that heritability of traits can be affected by the imputation method. Genotypes with missing values imputed with methods that make use of genealogic information can favor genetic analysis of highly polygenic traits, but not genome-wide prediction accuracy. The genotypic matrix captured the highest amount of genetic variance when missing loci were imputed by the method proposed in this paper. CONCLUSIONS: We concluded that hidden Markov models and random forest imputation are more suitable to studies that aim analyses of highly heritable traits while pedigree-based methods can be used to best analyze traits with low heritability. Despite the notable contribution to heritability, advantages in genomic prediction were not observed by changing the imputation method. We identified significant differences across imputation methods in a dataset missing 20 % of the genotypic values. It means that genotypic data from genotyping technologies that provide a high proportion of missing values, such as GBS, should be handled carefully because the imputation method will impact downstream analysis.

NAM: association studies in multiple populations.

MOTIVATION: Mixed linear models provide important techniques for performing genome-wide association studies. However, current models have pitfalls associated with their strong assumptions. Here, we propose a new implementation designed to overcome some of these pitfalls using an empirical Bayes algorithm. RESULTS: Here we introduce NAM, an R package that allows user to take into account prior information regarding population stratification to relax the linkage phase assumption of current methods. It allows markers to be treated as a random effect to increase the resolution, and uses a sliding-window strategy to increase power and avoid double fitting markers into the model. AVAILABILITY AND IMPLEMENTATION: NAM is an R package available in the CRAN repository. It can be installed in R by typing install.packages ('NAM'). CONTACT: krainey@purdue.edu. SUPPLEMENTARY INFORMATION: Supplementary date are available at Bioinformatics online.

Walking through the statistical black boxes of plant breeding.

KEY MESSAGE: The main statistical procedures in plant breeding are based on Gaussian process and can be computed through mixed linear models. Intelligent decision making relies on our ability to extract useful information from data to help us achieve our goals more efficiently. Many plant breeders and geneticists perform statistical analyses without understanding the underlying assumptions of the methods or their strengths and pitfalls. In other words, they treat these statistical methods (software and programs) like black boxes. Black boxes represent complex pieces of machinery with contents that are not fully understood by the user. The user sees the inputs and outputs without knowing how the outputs are generated. By providing a general background on statistical methodologies, this review aims (1) to introduce basic concepts of machine learning and its applications to plant breeding; (2) to link classical selection theory to current statistical approaches; (3) to show how to solve mixed models and extend their application to pedigree-based and genomic-based prediction; and (4) to clarify how the algorithms of genome-wide association studies work, including their assumptions and limitations.

Genomic prediction of survival time in a population of brown laying hens showing cannibalistic behavior.

BACKGROUND: Mortality due to cannibalism causes both economic and welfare problems in laying hens. To limit mortality due to cannibalism, laying hens are often beak-trimmed, which is undesirable for animal welfare reasons. Genetic selection is an alternative strategy to increase survival and is more efficient by taking heritable variation that originates from social interactions into account, which are modelled as the so-called indirect genetic effects (IGE). Despite the considerable heritable variation in survival time due to IGE, genetic improvement of survival time in laying hens is still challenging because the detected heritable variation of the trait with IGE is still limited, ranging from 0.06 to 0.26, and individuals that are still alive at the end of the recording period are censored. Furthermore, survival time records are available late in life and only on females. To cope with these challenges, we tested the hypothesis that genomic prediction increases the accuracy of estimated breeding values (EBV) compared to parental average EBV, and increases response to selection for survival time compared to a traditional breeding scheme. We tested this hypothesis in two lines of brown layers with intact beaks, which show cannibalism, and also the hypothesis that the rate of inbreeding per year is lower for genomic selection than for the traditional breeding scheme. RESULTS AND DISCUSSION: The standard deviation of genomic prediction EBV for survival time was around 22 days for both lines, indicating good prospects for selection against mortality in laying hens with intact beaks. Genomic prediction increased the accuracy of the EBV by 35 and 32 % compared to the parent average EBV for the two lines. At the current reference population size, predicted response to selection was 91 % higher when using genomic selection than with the traditional breeding scheme, as a result of a shorter generation interval in males and greater accuracy of selection in females. The predicted rate of inbreeding per generation with truncation selection was substantially lower for genomic selection than for the traditional breeding scheme for both lines. CONCLUSIONS: Genomic selection for socially affected traits is a promising tool for the improvement of survival time in laying hens with intact beaks.

Fine mapping of QTL and genomic prediction using allele-specific expression SNPs demonstrates that the complex trait of genetic resistance to Marek's disease is predominantly determined by transcriptional regulation.

BACKGROUND: Marek's disease (MD) is a lymphoproliferative disease of poultry induced by Marek's disease virus (MDV), a highly oncogenic alphaherpesvirus. Identifying the underlying genes conferring MD genetic resistance is desired for more efficacious control measures including genomic selection, which requires accurately identified genetic markers throughout the chicken genome. METHODS: Hypothesizing that variants located in transcriptional regulatory regions are the main mechanism underlying this complex trait, a genome-wide association study was conducted by genotyping a ~1,000 bird MD resource population derived from experimental inbred layers with SNPs containing 1,824 previously identified allele-specific expression (ASE) SNPs in response to MDV infection as well as 3,097 random SNPs equally spaced throughout the chicken genome. Based on the calculated associations, genomic predictions were determined for 200 roosters and selected sires had their progeny tested for Marek's disease incidence. RESULTS: Our analyses indicate that these ASE SNPs account for more than 83 % of the genetic variance and exhibit nearly all the highest associations. To validate these findings, 200 roosters had their genetic merit predicted from the ASE SNPs only, and the top 30 and bottom 30 ranked roosters were reciprocally mated to random hens. The resulting progeny showed that after only one generation of bidirectional selection, there was a 22 % difference in MD incidence and this approach gave a 125 % increase in accuracy compared to current pedigree-based estimates. CONCLUSIONS: We conclude that variation in transcriptional regulation is the major driving cause for genetic resistance to MD, and ASE SNPs identify the underlying genes and are sufficiently linked to the causative polymorphisms that they can be used for accurate genomic prediction as well as help define the underlying molecular basis. Furthermore, this approach should be applicable to other complex traits.

Mapping and genotypic analysis of the NK-lysin gene in chicken.

BACKGROUND: Antimicrobial peptides (AMP) are important elements of the first line of defence against pathogens in animals. NK-lysin is a cationic AMP that plays a critical role in innate immunity. The chicken NK-lysin gene has been cloned and its antimicrobial and anticancer activity has been described but its location in the chicken genome remains unknown. Here, we mapped the NK-lysin gene and examined the distribution of a functionally significant single nucleotide polymorphism (SNP) among different chicken inbred lines and heritage breeds. RESULTS: A 6000 rad radiation hybrid panel (ChickRH6) was used to map the NK-lysin gene to the distal end of chromosome 22. Two additional genes, the adipocyte enhancer-binding protein 1-like gene (AEBP1) and the DNA polymerase delta subunit 2-like (POLD2) gene, are located in the same NW_003779909 contig as NK-lysin, and were thus indirectly mapped to chromosome 22 as well. Previously, we reported a functionally significant SNP at position 271 of the NK-lysin coding sequence in two different chicken breeds. Here, we examined this SNP and found that the A allele appears to be more common than the G allele in these heritage breeds and inbred lines. CONCLUSIONS: The chicken NK-lysin gene mapped to the distal end of chromosome 22. Two additional genes, AEBP1 and POLD2, were indirectly mapped to chromosome 22 also. SNP analyses revealed that the A allele, which encodes a peptide with a higher antimicrobial activity, is more common than the G allele in our tested inbred lines and heritage breeds.

Comparison and contrast of genes and biological pathways responding to Marek's disease virus infection using allele-specific expression and differential expression in broiler and layer chickens.

BACKGROUND: Marek's disease (MD) is a commercially important neoplastic disease of chickens caused by the Marek's disease virus (MDV), a naturally occurring oncogenic alphaherpesvirus. Enhancing MD genetic resistance is desirable to augment current vaccines and other MD control measures. High throughput sequencing was used to profile splenic transcriptomes from individual F1 progeny infected with MDV at 4 days of age from both outbred broilers (meat-type) and inbred layer (egg-type) chicken lines that differed in MD genetic resistance. The resulting information was used to identify SNPs, genes, and biological pathways exhibiting allele-specific expression (ASE) in response to MDV infection in each type of chicken. In addition, we compared and contrasted the results of pathway analyses (ASE and differential expression (DE)) between chicken types to help inform on the biological response to MDV infection. RESULTS: With 7 individuals per line and treatment group providing high power, we identified 6,132 single nucleotide polymorphisms (SNPs) in 4,768 genes and 4,528 SNPs in 3,718 genes in broilers and layers, respectively, that exhibited ASE in response to MDV infection. Furthermore, 548 and 434 genes in broilers and layers, respectively, were found to show DE following MDV infection. Comparing the datasets, only 72 SNPs and 850 genes for ASE and 20 genes for DE were common between the two bird types. Although the chicken types used in this study were genetically different, at the pathway level, both TLR receptor and JAK/STAT signaling pathways were enriched as well as exhibiting a high proportion of ASE genes, especially at the beginning of both above mentioned regulatory pathways. CONCLUSIONS: RNA sequencing with adequate biological replicates is a powerful approach to identify high confidence SNPs, genes, and pathways that are associated with transcriptional response to MDV infection. In addition, the SNPs exhibiting ASE in response to MDV infection provide a strong foundation for determining the extent to which variation in expression influences MD incidence plus yield genetic markers for genomic selection. However, given the paucity of overlap among ASE SNP sets (broilers vs. layers), it is likely that separate screens need to be incorporated for each population. Finally, comparison of gene lists obtained between these two diverse chicken types indicate the TLR and JAK/STAT signaling are conserved when responding to MDV infection and may be altered by selection of genes exhibiting ASE found at the start of each pathway.

The CHD3 remodeler PICKLE associates with genes enriched for trimethylation of histone H3 lysine 27.

In Arabidopsis (Arabidopsis thaliana), the ATP-dependent chromatin remodeler PICKLE (PKL) determines expression of genes associated with developmental identity. PKL promotes the epigenetic mark trimethylation of histone H3 lysine 27 (H3K27me3) that facilitates repression of tissue-specific genes in plants. It has previously been proposed that PKL acts indirectly to promote H3K27me3 by promoting expression of the POLYCOMB REPRESSIVE COMPLEX2 complex that generates H3K27me3. We undertook expression and chromatin immunoprecipitation analyses to further characterize the contribution of PKL to gene expression and developmental identity. Our expression data support a critical and specific role for PKL in expression of H3K27me3-enriched loci but do not support a role for PKL in expression of POLYCOMB REPRESSIVE COMPLEX2. Moreover, our chromatin immunoprecipitation data reveal that PKL protein is present at the promoter region of multiple H3K27me3-enriched loci, indicating that PKL directly acts on these loci. In particular, we find that PKL is present at LEAFY COTYLEDON1 and LEAFY COTYLEDON2 during germination, which is when PKL acts to repress these master regulators of embryonic identity. Surprisingly, we also find that PKL is present at the promoters of actively transcribed genes that are ubiquitously expressed such as ACTIN7 and POLYUBIQUITIN10 that do not exhibit PKL-dependent expression. Taken together, our data contravene the previous model of PKL action and instead support a direct role for PKL in determining levels of H3K27me3 at repressed loci. Our data also raise the possibility that PKL facilitates a common chromatin remodeling process that is not restricted to H3K27me3-enriched regions.

Experiences with a single-step genome evaluation.

Genomic selection can be implemented based on the genomic relationship matrix (GBLUP) and can be combined with phenotypes from nongenotyped animals through the use of best linear unbiased prediction (BLUP). A common method to combine both sources of information involves multiple steps, but is difficult to use with complicated models and is nonoptimal. A simpler method, termed single-step GBLUP, or ssGBLUP, integrates the genomically derived relationships (G) with population-based pedigree relationships (A) into a combined relationship matrix (H) and allows for genomic selection in a single step. The ssGBLUP method is easy to implement and uses standard BLUP-based programs. Experiences with field data in chickens, pigs, and dairy indicate that ssGBLUP is more accurate yet much simpler than multi-step methods. The current limits of ssGBLUP are approximately 100,000 genotypes and 18 traits. Models involving 10 million animals have been run successfully. The inverse of H can also be used in existing programs for parameter estimationm, but a properly scaled G is needed for unbiased estimation. Also, as genomic predictions can be converted to SNP effects, ssGBLUP is useful for genomic-wide association studies. The single-step method for genomic selection translates the use of genomic information into standard BLUP, and variance-component estimation programs become a routine.

Multi-animal-model study reveals mutations in neural plasticity and nociception genes linked to excessive alcohol drinking.

BACKGROUND: The basis for familial alcohol use disorder (AUD) remains an enigma due to various biological and societal confounds. The present study used three of the most adopted and documented rat models, combining the alcohol-preferring/non-alcohol-preferring (P/NP) lines and high alcohol-drinking/low alcohol-drinking (HAD/LAD) replicated lines, of AUD as examined through the lens of whole genomic analyses. METHODS: We used complete genome sequencing of the P/NP lines and previously published sequences of the HAD/LAD replicates to enhance the discovery of variants associated with AUD and to remove confounding with genetic background and random genetic drift. Specifically, we used high-order statistical methods to search for genetic variants whose frequency changes in whole sets of gene ontologies corresponded with phenotypic changes in the direction of selection, that is, ethanol-drinking preference. RESULTS: Our first finding was that in addition to variants causing translational changes, the principal genetic changes associated with drinking predisposition were silent mutations and mutations in the 3' untranslated regions (3'UTR) of genes. Neither of these types of mutations alters the amino acid sequence of the translated protein but they influence both the rate and conformation of gene transcription, including its stability and posttranslational events that alter gene efficacy. This finding argues for refocusing human genomic studies on changes in gene efficacy. Among the key ontologies identified were the central genes associated with the Na+ voltage-gated channels of neurons and glia (including the Scn1a, Scn2a, Scn2b, Scn3a, Scn7a, and Scn9a subtypes) and excitatory glutamatergic secretion (including Grm2 and Myo6), both of which are essential in neuroplasticity. In addition, we identified "Nociception or Sensory Perception of Pain," which contained variants in nociception (Arrb1, Ccl3, Ephb1) and enlist sodium (Scn1a, Scn2a, Scn2b, Scn3a, Scn7a), pain activation (Scn9a), and potassium channel (Kcna1) genes. CONCLUSION: The multi-model analyses used herein reduced the confounding effects of random drift and the "founders" genetic background. The most differentiated bidirectionally selected genes across all three animal models were Scn9a, Scn1a, and Kcna, all of which are annotated in the nociception ontology. The complexity of neuroplasticity and nociception adds strength to the hypothesis that neuroplasticity and pain (physical or psychological) are prominent phenotypes genetically linked to the development of AUD.

Transcriptomic analysis of peritoneal cells in a mouse model of sepsis: confirmatory and novel results in early and late sepsis.

BACKGROUND: The events leading to sepsis start with an invasive infection of a primary organ of the body followed by an overwhelming systemic response. Intra-abdominal infections are the second most common cause of sepsis. Peritoneal fluid is the primary site of infection in these cases. A microarray-based approach was used to study the temporal changes in cells from the peritoneal cavity of septic mice and to identify potential biomarkers and therapeutic targets for this subset of sepsis patients. RESULTS: We conducted microarray analysis of the peritoneal cells of mice infected with a non-pathogenic strain of Escherichia coli. Differentially expressed genes were identified at two early (1 h, 2 h) and one late time point (18 h). A multiplexed bead array analysis was used to confirm protein expression for several cytokines which showed differential expression at different time points based on the microarray data. Gene Ontology based hypothesis testing identified a positive bias of differentially expressed genes associated with cellular development and cell death at 2 h and 18 h respectively. Most differentially expressed genes common to all 3 time points had an immune response related function, consistent with the observation that a few bacteria are still present at 18 h. CONCLUSIONS: Transcriptional regulators like PLAGL2, EBF1, TCF7, KLF10 and SBNO2, previously not described in sepsis, are differentially expressed at early and late time points. Expression pattern for key biomarkers in this study is similar to that reported in human sepsis, indicating the suitability of this model for future studies of sepsis, and the observed differences in gene expression suggest species differences or differences in the response of blood leukocytes and peritoneal leukocytes.

Identification and Validation of Ikaros (IKZF1) as a Cancer Driver Gene for Marek's Disease Virus-Induced Lymphomas.

Marek's disease virus (MDV) is the causative agent for Marek's disease (MD), which is characterized by T-cell lymphomas in chickens. While the viral Meq oncogene is necessary for transformation, it is insufficient, as not every bird infected with virulent MDV goes on to develop a gross tumor. Thus, we postulated that the chicken genome contains cancer driver genes; i.e., ones with somatic mutations that promote tumors, as is the case for most human cancers. To test this hypothesis, MD tumors and matching control tissues were sequenced. Using a custom bioinformatics pipeline, 9 of the 22 tumors analyzed contained one or more somatic mutation in Ikaros (IKFZ1), a transcription factor that acts as the master regulator of lymphocyte development. The mutations found were in key Zn-finger DNA-binding domains that also commonly occur in human cancers such as B-cell acute lymphoblastic leukemia (B-ALL). To validate that IKFZ1 was a cancer driver gene, recombinant MDVs that expressed either wild-type or a mutated Ikaros allele were used to infect chickens. As predicted, birds infected with MDV expressing the mutant Ikaros allele had high tumor incidences (~90%), while there were only a few minute tumors (~12%) produced in birds infected with the virus expressing wild-type Ikaros. Thus, in addition to Meq, key somatic mutations in Ikaros or other potential cancer driver genes in the chicken genome are necessary for MDV to induce lymphomas.

The development and characterization of a 60K SNP chip for chicken.

BACKGROUND: In livestock species like the chicken, high throughput single nucleotide polymorphism (SNP) genotyping assays are increasingly being used for whole genome association studies and as a tool in breeding (referred to as genomic selection). To be of value in a wide variety of breeds and populations, the success rate of the SNP genotyping assay, the distribution of the SNP across the genome and the minor allele frequencies (MAF) of the SNPs used are extremely important. RESULTS: We describe the design of a moderate density (60k) Illumina SNP BeadChip in chicken consisting of SNPs known to be segregating at high to medium minor allele frequencies (MAF) in the two major types of commercial chicken (broilers and layers). This was achieved by the identification of 352,303 SNPs with moderate to high MAF in 2 broilers and 2 layer lines using Illumina sequencing on reduced representation libraries. To further increase the utility of the chip, we also identified SNPs on sequences currently not covered by the chicken genome assembly (Gallus_gallus-2.1). This was achieved by 454 sequencing of the chicken genome at a depth of 12x and the identification of SNPs on 454-derived contigs not covered by the current chicken genome assembly. In total we added 790 SNPs that mapped to 454-derived contigs as well as 421 SNPs with a position on Chr_random of the current assembly. The SNP chip contains 57,636 SNPs of which 54,293 could be genotyped and were shown to be segregating in chicken populations. Our SNP identification procedure appeared to be highly reliable and the overall validation rate of the SNPs on the chip was 94%. We were able to map 328 SNPs derived from the 454 sequence contigs on the chicken genome. The majority of these SNPs map to chromosomes that are already represented in genome build Gallus_gallus-2.1.0. Twenty-eight SNPs were used to construct two new linkage groups most likely representing two micro-chromosomes not covered by the current genome assembly. CONCLUSIONS: The high success rate of the SNPs on the Illumina chicken 60K Beadchip emphasizes the power of Next generation sequence (NGS) technology for the SNP identification and selection step. The identification of SNPs from sequence contigs derived from NGS sequencing resulted in improved coverage of the chicken genome and the construction of two new linkage groups most likely representing two chicken micro-chromosomes.

Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds.

Breed utilization, genetic improvement, and industry consolidation are predicted to have major impacts on the genetic composition of commercial chickens. Consequently, the question arises as to whether sufficient genetic diversity remains within industry stocks to address future needs. With the chicken genome sequence and more than 2.8 million single-nucleotide polymorphisms (SNPs), it is now possible to address biodiversity using a previously unattainable metric: missing alleles. To achieve this assessment, 2551 informative SNPs were genotyped on 2580 individuals, including 1440 commercial birds. The proportion of alleles lacking in commercial populations was assessed by (1) estimating the global SNP allele frequency distribution from a hypothetical ancestral population as a reference, then determining the portion of the distribution lost, and then (2) determining the relationship between allele loss and the inbreeding coefficient. The results indicate that 50% or more of the genetic diversity in ancestral breeds is absent in commercial pure lines. The missing genetic diversity resulted from the limited number of incorporated breeds. As such, hypothetically combining stocks within a company could recover only preexisting within-breed variability, but not more rare ancestral alleles. We establish that SNP weights act as sentinels of biodiversity and provide an objective assessment of the strains that are most valuable for preserving genetic diversity. This is the first experimental analysis investigating the extant genetic diversity of virtually an entire agricultural commodity. The methods presented are the first to characterize biodiversity in terms of allelic diversity and to objectively link rate of allele loss with the inbreeding coefficient.

Comparison of linkage disequilibrium and haplotype diversity on macro- and microchromosomes in chicken.

BACKGROUND: The chicken (Gallus gallus), like most avian species, has a very distinct karyotype consisting of many micro- and a few macrochromosomes. While it is known that recombination frequencies are much higher for micro- as compared to macrochromosomes, there is limited information on differences in linkage disequilibrium (LD) and haplotype diversity between these two classes of chromosomes. In this study, LD and haplotype diversity were systematically characterized in 371 birds from eight chicken populations (commercial lines, fancy breeds, and red jungle fowl) across macro- and microchromosomes. To this end we sampled four regions of approximately 1 cM each on macrochromosomes (GGA1 and GGA2), and four 1.5 -2 cM regions on microchromosomes (GGA26 and GGA27) at a high density of 1 SNP every 2 kb (total of 889 SNPs). RESULTS: At a similar physical distance, LD, haplotype homozygosity, haploblock structure, and haplotype sharing were all lower for the micro- as compared to the macrochromosomes. These differences were consistent across populations. Heterozygosity, genetic differentiation, and derived allele frequencies were also higher for the microchromosomes. Differences in LD, haplotype variation, and haplotype sharing between populations were largely in line with known demographic history of the commercial chicken. Despite very low levels of LD, as measured by r2 for most populations, some haploblock structure was observed, particularly in the macrochromosomes, but the haploblock sizes were typically less than 10 kb. CONCLUSION: Differences in LD between micro- and macrochromosomes were almost completely explained by differences in recombination rate. Differences in haplotype diversity and haplotype sharing between micro- and macrochromosomes were explained by differences in recombination rate and genotype variation. Haploblock structure was consistent with demography of the chicken populations, and differences in recombination rates between micro- and macrochromosomes. The limited haploblock structure and LD suggests that future whole-genome marker assays will need 100+K SNPs to exploit haplotype information. Interpretation and transferability of genetic parameters will need to take into account the size of chromosomes in chicken, and, since most birds have microchromosomes, in other avian species as well.

Genetic improvement of traits affected by interactions among individuals: Sib selection schemes.

Livestock populations are usually kept in groups. As a consequence, social interactions among individuals affect productivity, health, and welfare. Current selection methods (individual selection), however, ignore those interactions and yield suboptimal or in some cases even negative responses. In principle, selection between groups instead of individuals offers a solution, but has rarely been adopted in practice for two reasons. First, the relationship between group selection theory and common animal breeding concepts, such as the accuracy of selection, is unclear. Second, application of group selection requires keeping selection candidates in groups, which is often undesirable in practice. This work has two objectives. First, we derive expressions for the accuracy of individual and group selection, which provides a measurement of quality for those methods. Second, we investigate the opportunity to improve traits affected by interactions by using information on relatives kept in family groups, while keeping selection candidates individually. The accuracy of selection based on relatives is shown to be an analogy of the classical expression for traits not affected by interactions. Our results show that selection based on relatives offers good opportunities for effective genetic improvement of traits affected by interactions.

Multilevel selection 1: Quantitative genetics of inheritance and response to selection.

Interaction among individuals is universal, both in animals and in plants, and substantially affects evolution of natural populations and responses to artificial selection in agriculture. Although quantitative genetics has successfully been applied to many traits, it does not provide a general theory accounting for interaction among individuals and selection acting on multiple levels. Consequently, current quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups. Understanding the full impacts of heritable interactions on the outcomes of selection requires a quantitative genetic framework including all levels of selection and relatedness. Here we present such a framework and provide expressions for the response to selection. Results show that interaction among individuals may create substantial heritable variation, which is hidden to classical analyses. Selection acting on higher levels of organization captures this hidden variation and therefore always yields positive response, whereas individual selection may yield response in the opposite direction. Our work provides testable predictions of response to multilevel selection and reduces to classical theory in the absence of interaction. Statistical methodology provided elsewhere enables empirical application of our work to both natural and domestic populations.