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Abstract Background Accuracy of genomic prediction depends on the heritability of the trait, the size of the training set, the relationship of the candidates to the training set, and the $$\text {Min}(N_{\text {QTL}},M_e)$$ Min ( N QTL , M e ) , where $$N_{\text {QTL}}$$ N QTL is the number of QTL and $$M_e$$ M e is the number of independently segregating chromosomal segments. Due to LD, the number $$Q_e$$ Q e of independently segregating QTL (effective QTL) can be lower than $$\text {Min}(N_{\text {QTL}},M_e)$$ Min ( N QTL , M e ) . In this paper, we show that $$Q_e$$ Q e is inversely associated with the trait-specific genomic relationship of a candidate to the training set. This provides an explanation for the inverse association between $$Q_e$$ Q e and the accuracy of prediction. Methods To quantify the genomic relationship of a candidate to all members of the training set, we considered the $$k^2$$ k 2 statistic that has been previously used for this purpose. It quantifies how well the marker covariate vector of a candidate can be represented as a linear combination of the rows of the marker covariate matrix of the training set. In this paper, we used Bayesian regression to make this statistic trait specific and argue that the trait-specific genomic relationship of a candidate to the training set is inversely associated with $$Q_e$$ Q e . Simulation was used to demonstrate the dependence of the trait-specific $$k^2$$ k 2 statistic on $$Q_e$$ Q e , which is related to $$N_{\text {QTL}}$$ N QTL . Conclusions The posterior distributions of the trait-specific $$k^2$$ k 2 statistic showed that the trait-specific genomic relationship between a candidate and the training set is inversely associated to $$Q_e$$ Q e and $$N_{\text {QTL}}$$ N QTL . Further, we show that trait-specific genomic relationship between a candidate and the training set is directly related to the size of the training set.

Abstract Background Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required. Results In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study. Conclusions Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection.

Abstract Background The detection of quantitative trait loci has accelerated with recent developments in genomics. The introduction of genomic selection in combination with sequencing efforts has made a large amount of genotypic data available. Functional traits such as fertility and calving traits have been included in routine genomic estimation of breeding values making large quantities of phenotypic data available for these traits. This data was used to investigate the genetics underlying fertility and calving traits and to identify potentially causative genomic regions and variants. We performed genome-wide association studies for 13 functional traits related to female fertility as well as for direct and maternal calving ease based on imputed whole-genome sequences. Deregressed breeding values from ~1000–5000 bulls per trait were used to test for associations with approximately 10 million imputed sequence SNPs. Results We identified a QTL on BTA17 associated with non-return rate at 56 days and with interval from first to last insemination. We found two significantly associated non-synonymous SNPs within this QTL region. Two more QTL for fertility traits were identified on BTA25 and 29. A single QTL was identified for maternal calving traits on BTA13 whereas three QTL on BTA19, 21 and 25 were identified for direct calving traits. The QTL on BTA19 co-localizes with the reported BH2 haplotype. The QTL on BTA25 is concordant for fertility and calving traits and co-localizes with a QTL previously reported to influence stature and related traits in Brown Swiss dairy cattle. Conclusion The detection of QTL and their causative variants remains challenging. Combining comprehensive phenotypic data with imputed whole genome sequences seems promising. We present a QTL on BTA17 for female fertility in dairy cattle with two significantly associated non-synonymous SNPs, along with five additional QTL for fertility traits and calving traits. For all of these we fine mapped the regions and suggest candidate genes and candidate variants.

Abstract Background Domestication, breed formation and intensive selection have resulted in divergent cattle breeds that likely exhibit their own genomic signatures. In this study, we used genotypes from 27,612 autosomal single nucleotide polymorphisms to characterize population structure based on 9214 sires representing nine Swiss dairy cattle populations: Brown Swiss (BS), Braunvieh (BV), Original Braunvieh (OB), Holstein (HO), Red Holstein (RH), Swiss Fleckvieh (SF), Simmental (SI), Eringer (ER) and Evolèner (EV). Genomic inbreeding (F ROH) and signatures of selection were determined by calculating runs of homozygosity (ROH). The results build the basis for a better understanding of the genetic development of Swiss dairy cattle populations and highlight differences between the original populations (i.e. OB, SI, ER and EV) and those that have become more popular in Switzerland as currently reflected by their larger populations (i.e. BS, BV, HO, RH and SF). Results The levels of genetic diversity were highest and lowest in the SF and BS breeds, respectively. Based on F ST values, we conclude that, among all pairwise comparisons, BS and HO (0.156) differ more than the other pairs of populations. The original Swiss cattle populations OB, SI, ER, and EV are clearly genetically separated from the Swiss cattle populations that are now more common and represented by larger numbers of cows. Mean levels of F ROH ranged from 0.027 (ER) to 0.091 (BS). Three of the original Swiss cattle populations, ER (F ROH: 0.027), OB (F ROH: 0.029), and SI (F ROH: 0.039), showed low levels of genomic inbreeding, whereas it was much higher in EV (F ROH: 0.074). Private signatures of selection for the original Swiss cattle populations are reported for BTA4, 5, 11 and 26. Conclusions The low levels of genomic inbreeding observed in the original Swiss cattle populations ER, OB and SI compared to the other breeds are explained by a lesser use of artificial insemination and greater use of natural service. Natural service results in more sires having progeny at each generation and thus this breeding practice is likely the major reason for the remarkable levels of genetic diversity retained within these populations. The fact that the EV population is regionally restricted and its small census size of herd-book cows explain its high level of genomic inbreeding.

Somatosensory cortex is innervated by afferents originating from the locus coeruleus which typically release noradrenaline. We tested if activation of presynaptic α1-adrenoceptors (AR) coupled to a Gq-mediated signaling cascade resulted in calcium (Ca2+) release from stores and thereby increased spontaneous transmitter release in rat barrel cortex. Adding 1–100 μM noradrenaline (NA) or 5 μM cirazoline (CO), a α1-AR specific agonist, to the standard artificial cerebrospinal fluid increased the frequency of miniature excitatory postsynaptic currents (mEPSC) by 64 ± 7% in 51% of pyramidal cells in layer II (responders) with no effect on the amplitude. In 42 responders, the mEPSC frequency during control was significantly smaller (39 ± 2 vs. 53 ± 4 Hz) and upon NA exposure, the input resistance (Rin) decreased (9 ± 7%) compared to non-responders. Experiments using CO and the antagonist prazosin revealed that NA acted via binding to α1-ARs, which was further corroborated by simultaneously blocking β- and α2-ARs with propranolol and yohimbine, which did not prevent the increase in mEPSC frequency. To verify elements in the signaling cascade, both the phospholipase C inhibitor edelfosine and the membrane permeable IP3 receptor blocker 2-APB averted the increase in mEPSC frequency. Likewise, emptying Ca2+ stores with cyclopiazonic acid or the chelation of intracellular Ca2+ with BAPTA-AM prevented the frequency increase, suggesting that the frequency increase was caused by presynaptic store release. When group I metabotropic glutamate receptors were activated with DHPG, co-application of NA occluded a further frequency increase suggesting that the two receptor activations may not signal independently of each other. The increased mEPSC frequency in a subset of pyramidal cells results in enhanced synaptic noise, which, together with the reduction in Rin, will affect computation in the network.

Recent progress in neuroscience to image and investigate brain function has been made possible by impressive developments in optogenetic and opto-molecular tools. Such research requires advances in optical techniques for the delivery of light through brain tissue with high spatial resolution. The tissue causes distortions to the wavefront of the incoming light which broadens the focus and consequently reduces the intensity and degrades the resolution. Such effects are detrimental in techniques requiring focal stimulation. Adaptive wavefront correction has been demonstrated to compensate for these distortions. However, iterative derivation of the corrective wavefront introduces time constraints that limit its applicability to probe living cells. Here, we demonstrate that we can pre-determine and generalize a small set of Zernike modes to correct for aberrations of the light propagating through specific brain regions. A priori identification of a corrective wavefront is a direct and fast technique that improves the quality of the focus without the need for iterative adaptive wavefront correction. We verify our technique by measuring the efficiency of two-photon photolysis of caged neurotransmitters along the dendrites of a whole-cell patched neuron. Our results show that encoding the selected Zernike modes on the excitation light can improve light propagation through brain slices of rats as observed by the neuron's evoked excitatory post-synaptic potential in response to localized focal uncaging at the spines of the neuron's dendrites.

Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP-based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.