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.

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.

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.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.

Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.

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.

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.

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.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans.

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.

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.

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.

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.

Selective sweep analysis in the genomes of the 91-R and 91-C Drosophila melanogaster strains reveals few of the 'usual suspects' in dichlorodiphenyltrichloroethane (DDT) resistance.

Adaptation of insect phenotypes for survival after exposure to xenobiotics can result from selection at multiple loci with additive genetic effects. To the authors' knowledge, no selective sweep analysis has been performed to identify such loci in highly dichlorodiphenyltrichloroethane (DDT) resistant insects. Here we compared a highly DDT resistant phenotype in the Drosophila melanogaster (Drosophila) 91-R strain to the DDT susceptible 91-C strain, both of common origin. Whole genome re-sequencing data from pools of individuals was generated separately for 91-R and 91-C, and mapped to the reference Drosophila genome assembly (v. 5.72). Thirteen major and three minor effect chromosome intervals with reduced nucleotide diversity (π) were identified only in the 91-R population. Estimates of Tajima's D (D) showed corresponding evidence of directional selection in these same genome regions of 91-R, however, no similar reductions in π or D estimates were detected in 91-C. An overabundance of non-synonymous proteins coding to synonymous changes were identified in putative open reading frames associated with 91-R. Except for NinaC and Cyp4g1, none of the identified genes were the 'usual suspects' previously observed to be associated with DDT resistance. Additionally, up-regulated ATP-binding cassette transporters have been previously associated with DDT resistance; however, here we identified a structurally altered MDR49 candidate resistance gene. The remaining fourteen genes have not previously been shown to be associated with DDT resistance. These results suggest hitherto unknown mechanisms of DDT resistance, most of which have been overlooked in previous transcriptional studies, with some genes having orthologs in mammals.

Mate competition and evolutionary outcomes in genetically modified zebrafish (Danio rerio).

Demonstrating relationships between sexual selection mechanisms and trait evolution is central to testing evolutionary theory. Using zebrafish, we found that wild-type males possessed a significant advantage in mate competition over transgenic RFP Glofish® males. In mating trials, wild-type males were aggressively superior to transgenic males in male-male chases and male-female chases; as a result, wild-type males sired 2.5× as many young as did transgenic males. In contrast, an earlier study demonstrated that female zebrafish preferred transgenic males as mates when mate competition was excluded experimentally. We tested the evolutionary consequence of this conflict between sexual selection mechanisms in a long-term study. The predicted loss of the transgenic phenotype was confirmed. More than 18,500 adults collected from 18 populations across 15 generations revealed that the frequency of the transgenic phenotype declined rapidly and was eliminated entirely in all but one population. Fitness component data for both sexes indicated that only male mating success differed between wild-type and transgenic individuals. Our predictive demographic model based on fitness components closely matched the rate of transgenic phenotype loss observed in the long-term study, thereby supporting its utility for studies assessing evolutionary outcomes of escaped or released genetically modified animals.

Methods to address poultry robustness and welfare issues through breeding and associated ethical considerations.

As consumers and society in general become more aware of ethical and moral dilemmas associated with intensive rearing systems, pressure is put on the animal and poultry industries to adopt alternative forms of housing. This presents challenges especially regarding managing competitive social interactions between animals. However, selective breeding programs are rapidly advancing, enhanced by both genomics and new quantitative genetic theory that offer potential solutions by improving adaptation of the bird to existing and proposed production environments. The outcomes of adaptation could lead to improvement of animal welfare by increasing fitness of the animal for the given environments, which might lead to increased contentment and decreased distress of birds in those systems. Genomic selection, based on dense genetic markers, will allow for more rapid improvement of traits that are expensive or difficult to measure, or have a low heritability, such as pecking, cannibalism, robustness, mortality, leg score, bone strength, disease resistance, and thus has the potential to address many poultry welfare concerns. Recently selection programs to include social effects, known as associative or indirect genetic effects (IGEs), have received much attention. Group, kin, multi-level, and multi-trait selection including IGEs have all been shown to be highly effective in reducing mortality while increasing productivity of poultry layers and reduce or eliminate the need for beak trimming. Multi-level selection was shown to increases robustness as indicated by the greater ability of birds to cope with stressors. Kin selection has been shown to be easy to implement and improve both productivity and animal well-being. Management practices and rearing conditions employed for domestic animal production will continue to change based on ethical and scientific results. However, the animal breeding tools necessary to provide an animal that is best adapted to these changing conditions are readily available and should be used, which will ultimately lead to the best possible outcomes for all impacted.

A snapshot of the hepatic transcriptome: ad libitum alcohol intake suppresses expression of cholesterol synthesis genes in alcohol-preferring (P) rats.

Research is uncovering the genetic and biochemical effects of consuming large quantities of alcohol. One prime example is the J- or U-shaped relationship between the levels of alcohol consumption and the risk of atherosclerotic cardiovascular disease. Moderate alcohol consumption in humans (about 30 g ethanol/d) is associated with reduced risk of coronary heart disease, while abstinence and heavier alcohol intake is linked to increased risk. However, the hepatic consequences of moderate alcohol drinking are largely unknown. Previous data from alcohol-preferring (P) rats showed that chronic consumption does not produce significant hepatic steatosis in this well-established model. Therefore, free-choice alcohol drinking in P rats may mimic low risk or nonhazardous drinking in humans, and chronic exposure in P animals can illuminate the molecular underpinnings of free-choice drinking in the liver. To address this gap, we captured the global, steady-state liver transcriptome following a 23 week free-choice, moderate alcohol consumption regimen (∼ 7.43 g ethanol/kg/day) in inbred alcohol-preferring (iP10a) rats. Chronic consumption led to down-regulation of nine genes in the cholesterol biosynthesis pathway, including HMG-CoA reductase, the rate-limiting step for cholesterol synthesis. These findings corroborate our phenotypic analyses, which indicate that this paradigm produced animals whose hepatic triglyceride levels, cholesterol levels and liver histology were indistinguishable from controls. These findings explain, at least in part, the J- or U-shaped relationship between cardiovascular risk and alcohol intake, and provide outstanding candidates for future studies aimed at understanding the mechanisms that underlie the salutary cardiovascular benefits of chronic low risk and nonhazardous alcohol intake.

The prospects of selection for social genetic effects to improve welfare and productivity in livestock.

Social interactions between individuals living in a group can have both positive and negative effects on welfare, productivity, and health of these individuals. Negative effects of social interactions in livestock are easier to observe than positive effects. For example, laying hens may develop feather pecking, which can cause mortality due to cannibalism, and pigs may develop tail biting or excessive aggression. Several studies have shown that social interactions affect the genetic variation in a trait. Genetic improvement of socially-affected traits, however, has proven to be difficult until relatively recently. The use of classical selection methods, like individual selection, may result in selection responses opposite to expected, because these methods neglect the effect of an individual on its group mates (social genetic effects). It has become clear that improvement of socially-affected traits requires selection methods that take into account not only the direct effect of an individual on its own phenotype but also the social genetic effects, also known as indirect genetic effects, of an individual on the phenotypes of its group mates. Here, we review the theoretical and empirical work on social genetic effects, with a focus on livestock. First, we present the theory of social genetic effects. Subsequently, we evaluate the evidence for social genetic effects in livestock and other species, by reviewing estimates of genetic parameters for direct and social genetic effects. Then we describe the results of different selection experiments. Finally, we discuss issues concerning the implementation of social genetic effects in livestock breeding programs. This review demonstrates that selection for socially-affected traits, using methods that target both the direct and social genetic effects, is a promising, but sometimes difficult to use in practice, tool to simultaneously improve production and welfare in livestock.

Changes in variance explained by top SNP windows over generations for three traits in broiler chicken.

The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1-3, 2-4, 3-5, and 1-5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated.

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.

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.

Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens.

The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.