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We used a least absolute shrinkage and selection operator (LASSO) approach to estimate marker effects for genomic selection. The least angle regression (LARS) algorithm and cross-validation were used to define the best subset of markers to include in the model. The LASSO-LARS approach was tested on two data sets : a simulated data set with 5865 individuals and 6000 Single Nucleotide Polymorphisms (SNPs); and a mouse data set with 1885 individuals genotyped for 10 656 SNPs and phenotyped for a number of quantitative traits. In the simulated data, three approaches were used to split the reference population into training and validation subsets for cross-validation: random splitting across the whole population; random sampling of validation set from the last generation only, either within or across families. The highest accuracy was obtained by random splitting across the whole population. The accuracy of genomic estimated breeding values (GEBVs) in the candidate population obtained by LASSO-LARS was 0.89 with 156 explanatory SNPs. This value was higher than those obtained by Best Linear Unbiased Prediction (BLUP) and a Bayesian method (BayesA), which were 0·75 and 0·84, respectively. In the mouse data, 1600 individuals were randomly allocated to the reference population. The GEBVs for the remaining 285 individuals estimated by LASSO-LARS were more accurate than those obtained by BLUP and BayesA for weight at six weeks and slightly lower for growth rate and body length. It was concluded that LASSO-LARS approach is a good alternative method to estimate marker effects for genomic selection, particularly when the cost of genotyping can be reduced by using a limited subset of markers. [ABSTRACT FROM AUTHOR]

Genotype-by-environment interactions for production traits in dairy cattle have often been observed, while QTL analyses have focused on detecting genes with general effects on production traits. In this study, a QTL search for genes with environmental interaction for the traits milk yield, protein yield, and fat yield were performed on Bos taurus autosome 6 (BTA6), also including information about the previously investigated candidate genes ABCG2 and OPN. The animals in the study were Norwegian Red. Eighteen grandsires and 716 sires were genotyped for 362 markers on BTA6. Every marker bracket was regarded as a putative QTL position. The effects of the candidate genes and the putative QTL were modeled as a regression on an environmental parameter (herd year), which is based on the predicted herd-year effect for the trait. Two QTL were found to have environmentally dependent effects on milk yield. These QTL were located 3.6 cM upstream and 9.1 cM downstream from ABCG2. No environmentally dependent QTL was found to significantly affect protein or fat yield. [ABSTRACT FROM AUTHOR]

A novel multipoint method, based on an approximate coalescence approach, to analyze multiple linked markers is presented. Unlike other approximate coalescence methods, it considers all markers simultaneously but only two haplotypes at a time. We demonstrate the use of this method for linkage disequilibrium (LD) mapping of QTL and estimation of effective population size. The method estimates identity-by-descent (IBD) probabilities between pairs of marker haplotypes. Both LD and combined linkage and LD mapping rely on such IBD probabilities. The method is approximate in that it considers only the information on a pair of haplotypes, whereas a full modeling of the coalescence process would simultaneously consider all haplotypes. However, full coalescence modeling is computationally feasible only for few linked markers. Using simulations of the coalescence process, the method is shown to give almost unbiased estimates of the effective population size. Compared to direct marker and haplotype association analyses, IBD-based QTL mapping showed clearly a higher power to detect a QTL and a more realistic confidence interval for its position. The modeling of LD could he extended to estimate other LD-related parameters such as recombination rates. [ABSTRACT FROM AUTHOR]

If direct tests for genetic mutations affecting economic traits in pig production where available, the tests could be used to increase the accuracy of selection, using gene-assisted selection (GAS). In this paper we evaluated the increased economic returns from using a direct genetic test in the nucleus of a commercial pig enterprise. A pig population with segregating quantitative trait loci (QTL) and markers was simulated. The QTL affected four independent traits, growth index (GI), net food intake (NFI), pigs born alive (PBA) and meat quality index (MQI). A direct genetic test was assumed to be available for the QTL which contributed the greatest proportion of genetic variance for each trait. Four strategies were investigated, GAS-GI, GAS-NFI, GAS-PBA and GAS-MQI, where in each strategy genotyping was available for an identified locus affecting only the trait in the strategy name. The effect of the QTL in each case was assumed to be known without error. For each strategy, GAS was conducted in a small nucleus population of 20 sows, and selection was on an index of the four quantitative traits. Extra dollar returns from the additional genetic gain from GAS schemes were calculated using an economic model of a pig enterprise (with a 100 sow nucleus, 1000 sow multiplier tier and 10 000 sow commercial tier). Over five generations of selection, the extra returns from GAS were calculated as the discounted difference in response from GAS and non-GAS, in dollars. The extra returns were divided by the number of progeny in the nucleus each generation, multiplied by the number of generations required to fix the QTL and a discounting factor, to give the break-even cost of the genetic test. Break-even cost of test per animal were $104 for GAS-GI, $97 for GAS-NFI, $78 for GAS-PBA and $80 for GAS-MQI. While in this study a direct genetic test was assumed to be available for the mutation causing the QTL effect, results should be similar when a haplotype of markers in linkage disequilibrium with the favourable QTL allele is used in MAS. [Copyright &y& Elsevier]

In 1997, Darvasi and Soller presented empirical predictions of the confidence interval of quantitative trait loci (QTL) location for dense marker maps in experimental crosses. They showed from simulation results for backcross and F2 populations from inbred lines that the 95% confidence interval was a simple function of sample size and the effect of the QTL. In this study, we derive by theory simple equations that can be used to predict any confidence interval and show that for the 95% confidence interval, they are in good agreement with the empirical results given by Darvasi and Soller. A general form of the confidence interval is given that also applies to other population structures (e.g., collections of sib pairs). Furthermore, the expected shape of the likelihood-ratio-test around the true QTL location is derived, which is shown to be extremely leptokurtic. It is shown that this shape explains why confidence intervals from the Log of Odds (LOD) drop-off method and bootstrap results frequently differ for real data sets. [ABSTRACT FROM AUTHOR]

The profitability of using DNA markers linked to quantitative trait loci (QTL) in a pig enterprise in marker assisted selection (MAS) was evaluated, using a computer simulation of a pig population with segregating QTL and markers. The starting point for MAS was a population of pigs evolved for a large number of generations. Mutation, drift and natural selection affected both the size and frequency of segregating QTL. The QTL affected four independent traits, growth index (GI), net food intake (NFI), pigs born alive (PBA) and a meat quality index (MQI). A genome scan was conducted in the population, using a genome-wide (GW), chromosome-wide (CW) or point-wise (PW) threshold as criteria for taking QTL from the genome scan to MAS. MAS with the significant markers was conducted in a small nucleus population of 20 sows, and selection was on an index of the four quantitative traits. The extra dollar returns from the additional genetic gain from MAS schemes were calculated using an economic model of a pig enterprise (with a 100 sow nucleus, 1000 sow multiplier tier and 10 000 sow commercial tier). Returns were largest with MAS schemes using QTL detected with CW and PW thresholds. However, when the cost of genotyping per animal per marker was $4, profit (extra returns from MAS−cost of genotyping) were greatest with the GW criteria, as for this criteria the fewest markers needed to be genotyped. MAS as described in this study may be difficult to implement in commercial herds, as markers and QTL were not closely linked, and linkage phase between QTL and markers had to be established within every family. If markers could be found in linkage disequilibrium with QTL, such that marker–QTL allele associations persist across the whole population, the profitability of MAS could be increased. [Copyright &y& Elsevier]

In livestock, genotype x environment interaction (G x E) has been widely investigated, with genotype defined at the level of subspecies, breeds, individual animals within a breed (for example performance of offspring of elite sires across environments), and genotypes at single-nucleotide polymorphisms (SNPs). Environments can be described by category (e.g., tropical vs. temperate, high vs. low farm input levels, countries) and by continuous variables such as temperature. To predict breeding values of genotypes in environments described by categories, multitrait models with each category a different trait are used. The models are now being used to predict genomic estimated breeding values (GEBV) for different environments such as the value of a bull's genetics for his daughter's milk production in different countries. The multitrait genomic model has also been used to enable reference populations to be merged across environments and across countries, leading to more accurate GEBV. When the environment can be described by a continuous variable, random regression models have been used to predict response of genotypes to the environment. For example, these models have been used to determine if there are SNP genotypes associated with less sensitivity of milk production to increasing temperature. In both livestock and plant breeding, methods that use genomic information can better cope with a reduced degree of replication of individuals across environments, as it is actually the alleles that must be replicated across environments. More accurate estimates of G x E with the genomic approach may therefore be achievable than was possible in the past. [ABSTRACT FROM AUTHOR]

Abstract: A computer simulation model was used to optimise strategies for marker assisted selection with best linear unbiased prediction (MBLUP) for quantitative traits in a selective breeding program for the southern Australian abalone industry. The simulation model assumed five marker loci associated with genes affecting growth rate explained 50% of the genetic variance for this trait. Use of markers associated with improved disease resistance was also modelled. Parameters and simulation scheme used are described. The simulations accounted for the possibility of early induction of reproductive condition to reduce generation intervals and for selective genotyping. Affects on profitability were calculated using estimates from the industry of income and expenses for a typical farm and accounting for the costs associated with marker testing. While the extra genetic gains from MAS were in the order of 15%, if the cost of genotyping each marker per animal was $10 AUS, then MBLUP for growth was only profitable when 75 progeny per family were genotyped. Early selection (halving the generation interval) was advantageous for MBLUP and BLUP provided the number of progeny per family was greater 5, as the advantage of two generations of selection more than compensated for the decreased accuracy of selection for early growth. However accurate estimates of parameters, such as the genetic correlation between early growth rate and growth and age at first spawning, would need to be obtained before considering implementing this strategy. The advantage of MBLUP was greater for disease resistance than for growth rate. Selective genotyping, which would greatly reduced genotyping costs of implementing MBLUP to improve disease resistance, did not greatly reduce the additional response achieved with marker assisted selection. [Copyright &y& Elsevier]

Background: Topological association domains (TADs) are chromosomal domains characterised by frequent internal DNA-DNA interactions. The transcription factor CTCF binds to conserved DNA sequence patterns called CTCF binding motifs to either prohibit or facilitate chromosomal interactions. TADs and CTCF binding motifs control gene expression, but they are not yet well defined in the bovine genome. In this paper, we sought to improve the annotation of bovine TADs and CTCF binding motifs, and assess whether the new annotation can reduce the search space for cis-regulatory variants. Results: We used genomic synteny to map TADs and CTCF binding motifs from humans, mice, dogs and macaques to the bovine genome. We found that our mapped TADs exhibited the same hallmark properties of those sourced from experimental data, such as housekeeping genes, transfer RNA genes, CTCF binding motifs, short interspersed elements, H3K4me3 and H3K27ac. We showed that runs of genes with the same pattern of allele-specific expression (ASE) (either favouring paternal or maternal allele) were often located in the same TAD or between the same conserved CTCF binding motifs. Analyses of variance showed that when averaged across all bovine tissues tested, TADs explained 14% of ASE variation (standard deviation, SD: 0.056), while CTCF explained 27% (SD: 0.078). Furthermore, we showed that the quantitative trait loci (QTLs) associated with gene expression variation (eQTLs) or ASE variation (aseQTLs), which were identified from mRNA transcripts from 141 lactating cows' white blood and milk cells, were highly enriched at putative bovine CTCF binding motifs. The linearly-furthermost, and most-significant aseQTL and eQTL for each genic target were located within the same TAD as the gene more often than expected (Chi-Squared test P-value < 0.001). Conclusions: Our results suggest that genomic synteny can be used to functionally annotate conserved transcriptional components, and provides a tool to reduce the search space for causative regulatory variants in the bovine genome. [ABSTRACT FROM AUTHOR]

Related individuals share potentially long chromosome segments that trace to a common ancestor. We describe a phasing algorithm (ChromoPhase) that utilizes this characteristic of finite populations to phase large sections of a chromosome. In addition to phasing, our method imputes missing genotypes in individuals genotyped at lower marker density when more densely genotyped relatives are available. ChromoPhase uses a pedigree to collect an individual's (the proband) surrogate parents and offspring and uses genotypic similarity to identify its genomic surrogates. The algorithm then cycles through the relatives and genomic surrogates one at a time to find shared chromosome segments. Once a segment has been identified, any missing information in the proband is filled in with information from the relative. We tested ChromoPhase in a simulated population consisting of 400 individuals at a marker density of 1500/M, which is approximately equivalent to a 50K bovine single nucleotide polymorphism chip. In simulated data, 99.9% loci were correctly phased and, when imputing from 100 to 1500 markers, more than 87% of missing genotypes were correctly imputed. Performance increased when the number of generations available in the pedigree increased, but was reduced when the sparse genotype contained fewer loci. However, in simulated data, ChromoPhase correctly imputed at least 12% more genotypes than fastPHASE, depending on sparse marker density. We also tested the algorithm in a real Holstein cattle data set to impute 50K genotypes in animals with a sparse 3K genotype. In these data 92% of genotypes were correctly imputed in animals with a genotyped sire. We evaluated the accuracy of genomic predictions with the dense, sparse, and imputed simulated data sets and show that the reduction in genomic evaluation accuracy is modest even with imperfectly imputed genotype data. Our results demonstrate that imputation of missing genotypes, and potentially full genome sequence, using long-range phasing is feasible. [ABSTRACT FROM AUTHOR]

The increased transcription of the Cyp6g1 gene of Drosophila melanogaster, and consequent resistance to insecticides such as DDT, is a widely cited example of adaptation mediated by cis-regulatory change. A fragment of an Accord transposable element inserted upstream of the Cyp6g1 gene is causally associated with resistance and has spread to high frequencies in populations around the world since the 1940s. Here we report the existence of a natural allelic series at this locus of D. melanogaster, involving copy number variation of Cyp6g1, and two additional transposable element insertions (a P and an HMS-Beagle). We provide evidence that this genetic variation underpins phenotypic variation, as the more derived the allele, the greater the level of DDT resistance. Tracking the spatial and temporal patterns of allele frequency changes indicates that the multiple steps of the allelic series are adaptive. Further, a DDT association study shows that the most resistant allele, Cyp6g1-[BP], is greatly enriched in the top 5% of the phenotypic distribution and accounts for ∼16% of the underlying phenotypic variation in resistance to DDT. In contrast, copy number variation for another candidate resistance gene, Cyp12d1, is not associated with resistance. Thus the Cyp6g1 locus is a major contributor to DDT resistance in field populations, and evolution at this locus features multiple adaptive steps occurring in rapid succession. [ABSTRACT FROM AUTHOR]

Continued production of food in areas predicted to be most affected by climate change, such as dairy farming regions of Australia, will be a major challenge in coming decades. Along with rising temperatures and water shortages, scarcity of inputs such as high energy feeds is predicted. With the motivation of selecting cattle adapted to these changing environments, we conducted a genome wide association study to detect DNA markers (single nucleotide polymorphisms) associated with the sensitivity of milk production to environmental conditions. To do this we combined historical milk production and weather records with dense marker genotypes on dairy sires with many daughters milking across a wide range of production environments in Australia. Markers associated with sensitivity of milk production to feeding level and sensitivity of milk production to temperature humidity index on chromosome nine and twenty nine respectively were validated in two independent populations, one a different breed of cattle. As the extent of linkage disequilibrium across cattle breeds is limited, the underlying causative mutations have been mapped to a small genomic interval containing two promising candidate genes. The validated marker panels we have reported here will aid selection for high milk production under anticipated climate change scenarios, for example selection of sires whose daughters will be most productive at low levels of feeding. [ABSTRACT FROM AUTHOR]

Proposes a novel multilocus measure of linkage disequilibrium called chromosome segment homozygosity (CSH). Variability of CSH decreased as the number of markers in the chromosome segment was increased; Assertion that CSH is defined for a specific chromosome segment; Indication that CSH had a lower coefficient of variation than r⊃2.

Investigates the method for fine-scale mapping of genes. Correlation between twinning rate and ovulation; Factors affecting monozygotic twinning; Detection of marker haplotype in the chromosome.

Effective population size (Ne) determines the amount of genetic variation, genetic drift, and linkage disequilibrium (LD) in populations. Here, we present the first genome-wide estimates of human effective population size from LD data. Chromosome-specific effective population size was estimated for all autosomes and the X chromosome from estimated LD between SNP pairs <100 kb apart. We account for variation in recombination rate by using coalescent-based estimates of fine-scale recombination rate from one sample and correlating these with LD in an independent sample. Phase I of the HapMap project produced between 18 and 22 million SNP pairs in samples from four populations: Yoruba from Ibadan (YRI), Nigeria; Japanese from Tokyo (JPT); Han Chinese from Beijing (HCB); and residents from Utah with ancestry from northern and western Europe (CEU). For CEU, JPT, and HCB, the estimate of effective population size, adjusted for SNP ascertainment bias, was ∼3100, whereas the estimate for the YRI was ∼7500, consistent with the out-of-Africa theory of ancestral human population expansion and concurrent bottlenecks. We show that the decay in LD over distance between SNPs is consistent with recent population growth. The estimates of Ne are lower than previously published estimates based on heterozygosity, possibly because they represent one or more bottlenecks in human population size that occurred ∼10,000 to 200,000 years ago. [ABSTRACT FROM AUTHOR]