Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture

Background

The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped.

Methods

Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets.

Results

Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams.

Conclusions

Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.     

Myeloperoxidase reduces the opsonizing activity of immunoglobulin G and complement component C3b

The effect of myeloperoxidase, hydrogen peroxide (H2O2) and a halide (Cl) on the opsonizing molecules in immunoglobulin G (IgG) and complement factor C3b was assayed. At concentrations of the enzyme (1 microgram/ml) that can be found in the extracellular fluid during inflammation, the myeloperoxidase-H2O2-Cl system inhibited the opsonizing effect of IgG and C3b measured as phagocytic uptake and superoxide generation. The effect was related to the enzymatic peroxidative activity of the protein. The presence of albumin (10 mg/ml) reduced the effect of myeloperoxidase with 10-20%. Taurine, which in the presence of myeloperoxidase-H2O2-Cl forms hydrophilic chloramines, and D-penicillamine, which scavenges HOCl, neutralize the inhibitory effect of myeloperoxidase. This suggests that either hypochlorous acid or lipophilic chloramines may exert its effect by oxidizing free sulphydryl groups exposed on the opsonizing ligands. Since the myeloperoxidase-H2O2-halide system also affects chemotactic factors, leukotrienes, proteinases and membrane receptors, the system may in several ways affect the development of the inflammatory response.     

Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters

In the Anthropocene, watershed chemical transport is increasingly dominated by novel combinations of elements, which are hydrologically linked together as ‘chemical cocktails.’ Chemical cocktails are novel because human activities greatly enhance elemental concentrations and their probability for biogeochemical interactions and shared transport along hydrologic flowpaths. A new chemical cocktail approach advances our ability to: trace contaminant mixtures in watersheds, develop chemical proxies with high-resolution sensor data, and manage multiple water quality problems. We explore the following questions: (1) Can we classify elemental transport in watersheds as chemical cocktails using a new approach? (2) What is the role of climate and land use in enhancing the formation and transport of chemical cocktails in watersheds? To address these questions, we first analyze trends in concentrations of carbon, nutrients, metals, and salts in fresh waters over 100 years. Next, we explore how climate and land use enhance the probability of formation of chemical cocktails of carbon, nutrients, metals, and salts. Ultimately, we classify transport of chemical cocktails based on solubility, mobility, reactivity, and dominant phases: (1) sieved chemical cocktails (e.g., particulate forms of nutrients, metals and organic matter); (2) filtered chemical cocktails (e.g., dissolved organic matter and associated metal complexes); (3) chromatographic chemical cocktails (e.g., ions eluted from soil exchange sites); and (4) reactive chemical cocktails (e.g., limiting nutrients and redox sensitive elements). Typically, contaminants are regulated and managed one element at a time, even though combinations of elements interact to influence many water quality problems such as toxicity to life, eutrophication, infrastructure corrosion, and water treatment. A chemical cocktail approach significantly expands evaluations of water quality signatures and impacts beyond single elements to mixtures. High-frequency sensor data (pH, specific conductance, turbidity, etc.) can serve as proxies for chemical cocktails and improve real-time analyses of water quality violations, identify regulatory needs, and track water quality recovery following storms and extreme climate events. Ultimately, a watershed chemical cocktail approach is necessary for effectively co-managing groups of contaminants and provides a more holistic approach for studying, monitoring, and managing water quality in the Anthropocene.     

Demographic History of Indigenous Populations in Mesoamerica Based on mtDNA Sequence Data

The genetic characterization of Native American groups provides insights into their history and demographic events. We sequenced the mitochondrial D-loop region (control region) of 520 samples from eight Mexican indigenous groups. In addition to an analysis of the genetic diversity, structure and genetic relationship between 28 Native American populations, we applied Bayesian skyline methodology for a deeper insight into the history of Mesoamerica. AMOVA tests applying cultural, linguistic and geographic criteria were performed. MDS plots showed a central cluster of Oaxaca and Maya populations, whereas those from the North and West were located on the periphery. Demographic reconstruction indicates higher values of the effective number of breeding females (Nef) in Central Mesoamerica during the Preclassic period, whereas this pattern moves toward the Classic period for groups in the North and West. Conversely, Nef minimum values are distributed either in the Lithic period (i.e. founder effects) or in recent periods (i.e. population declines). The Mesomerican regions showed differences in population fluctuation as indicated by the maximum Inter-Generational Rate (IGRmax): i) Center-South from the lithic period until the Preclassic; ii) West from the beginning of the Preclassic period until early Classic; iii) North characterized by a wide range of temporal variation from the Lithic to the Preclassic. Our findings are consistent with the genetic variations observed between central, South and Southeast Mesoamerica and the North-West region that are related to differences in genetic drift, structure, and temporal survival strategies (agriculture versus hunter-gathering, respectively). Interestingly, although the European contact had a major negative demographic impact, we detect a previous decline in Mesoamerica that had begun a few hundred years before.     

Reference genes for quantitative reverse transcription-polymerase chain reaction expression studies in wild and cultivated peanut

Background

Molecular genetic studies on rare tumour entities, such as bone tumours, often require the use of decalcified, formalin-fixed, paraffin-embedded tissue (dFFPE) samples. Regardless of which decalcification procedure is used, this introduces a vast breakdown of DNA that precludes the possibility of further molecular genetic testing. We set out to establish a robust protocol that would overcome these intrinsic hurdles for bone tumour research.

Findings

The goal of our study was to establish a protocol, using a modified DNA isolation procedure and quality controls, to select decalcified samples suitable for array-CGH testing. Archival paraffin blocks were obtained from 9 different pathology departments throughout Europe, using different fixation, embedding and decalcification procedures, in order to preclude a bias for certain lab protocols. Isolated DNA samples were subjected to direct chemical labelling and enzymatic labelling systems and were hybridised on a high resolution oligonucleotide chip containing 44,000 reporter elements. Genomic alterations (gains and losses) were readily detected in most of the samples analysed. For example, both homozygous deletions of 0.6 Mb and high level of amplifications of 0.7 Mb were identified.

Conclusions

We established a robust protocol for molecular genetic testing of dFFPE derived DNA, irrespective of fixation, decalcification or sample type used. This approach may greatly facilitate further genetic testing on rare tumour entities where archival decalcified, formalin fixed samples are the only source.     

Quantification of angiogenesis in estrogen receptor-positive and negative breast carcinoma

Background

The objective of this study was to evaluate angiogenesis according to CD34 antigen expression in estrogen receptor (ER)-positive and negative breast carcinomas.

Methods

This study comprised 64 cases of infiltrating ductal carcinoma in postmenopausal women divided into two groups: Group A: ER-positive, n = 35; and Group B: ER-negative, n = 29. The anti-CD34 monoclonal antibody was used as a marker for endothelial cells. Microvessel count was carried out in 10 fields per slide using a 40× objective lens (magnification 400×). Statistical analysis of the data was performed using Student's t-test (p < 0.05).

Results

The mean number of vessels stained with the anti-CD34 antibody in the estrogen receptor-positive and negative tumors was 23.51 ± 1.15 and 40.24 ± 0.42, respectively. The number of microvessels was significantly greater in the estrogen receptor-negative tumors (p < 0.001).

Conclusion

ER-negative tumors have significantly greater CD34 antigen expression compared to ER-positive tumors.

     

A simple method to separate base population and segregation effects in genomic relationship matrices

Background

Genomic selection and estimation of genomic breeding values (GBV) are widely used in cattle and plant breeding. Several studies have attempted to detect population subdivision by investigating the structure of the genomic relationship matrix G. However, the question of how these effects influence GBV estimation using genomic best linear unbiased prediction (GBLUP) has received little attention.

Methods

We propose a simple method to decompose G into two independent covariance matrices, one describing the covariance that results from systematic differences in allele frequencies between groups at the pedigree base (G_A^*) and the other describing genomic relationships (G_S) corrected for these differences. Using this decomposition and F_st statistics, we examined whether observed genetic distances between genotyped subgroups within populations resulted from the heterogeneous genetic structure present at the base of the pedigree and/or from breed divergence. Using this decomposition, we tested three models in a forward prediction validation scenario on six traits using Brown Swiss and dual-purpose Fleckvieh cattle data. Model 0 (M0) used both components and is equivalent to the model using the standard G-matrix. Model 1 (M1) used G_S only and model 2 (M2), an extension of M1, included a fixed genetic group effect. Moreover, we analyzed the matrix of contributions of each base group (Q) and estimated the effects and prediction errors of each base group using M0 and M1.

Results

The proposed decomposition of G helped to examine the relative importance of the effects of base groups and segregation in a given population. We found significant differences between the effects of base groups for each breed. In forward prediction, differences between models in terms of validation reliability of estimated direct genomic values were small but predictive power was consistently lowest for M1. The relative advantage of M0 or M2 in prediction depended on breed, trait and genetic composition of the validation group. Our approach presents a general analogy with the use of genetic groups in conventional animal models and provides proof that standard GBLUP using G yields solutions equivalent to M0, where base groups are considered as correlated random effects within the additive genetic variance assigned to the genetic base.     

Genetic basis of resistance to Cry1Ac and Cry2Aa in Heliothis virescens (Lepidoptera: Noctuidae)

The development of pest resistance to transgenic crop plants producing insecticidal toxins from Bacillus thuringiensis Berliner (Bt) poses a major threat to their sustainable use in agriculture. Pyramiding two toxins with different modes of actions in the same plant is now being used to delay the evolution of resistance in the insects, but this strategy could fail if a single gene in a pest confers resistance to both toxins. The CP73 strain of the cotton pest Heliothis virescens (F.) is resistant to both Cry1Ac and Cry2Aa toxins from Bt. We explored the genetic basis of resistance in this strain with a backcross, split-family design. The gene with the largest effect on Cry1Ac resistance in CP73 (BtR-5) maps to linkage group 10 of H. virescens and thus differs from the previously described linkage group 9 BtR-4 resistance found in the YHD2 strain, involving mutation of the gene encoding a 12-domain cadherin-like binding target of the Cry1A toxins. Neither BtR-4 nor BtR-5 seems to confer significant resistance to Cry2Aa. A majority of the linkage groups studied in one backcross family made a small positive contribution to resistance for both toxins. Thus, the Cry2Aa resistance in CP73 is not caused by either of the two major Cry1Ac resistance-conferring genes but instead probably has a quantitative genetic basis.