Characterization of X-Linked SNP genotypic variation in globally distributed human populations

Background  

The transmission pattern of the human X chromosome reduces its population size relative to the autosomes, subjects it to disproportionate influence by female demography, and leaves X-linked mutations exposed to selection in males. As a result, the analysis of X-linked genomic variation can provide insights into the influence of demography and selection on the human genome. Here we characterize the genomic variation represented by 16,297 X-linked SNPs genotyped in the CEPH human genome diversity project samples.     

Cost-effective HRMA pre-sequence typing of clone libraries; application to phage display selection

Background  

Methodologies like phage display selection, in vitro mutagenesis and the determination of allelic expression differences include steps where large numbers of clones need to be compared and characterised. In the current study we show that high-resolution melt curve analysis (HRMA) is a simple, cost-saving tool to quickly study clonal variation without prior nucleotide sequence knowledge.     

Genome-wide DNA polymorphism analyses using VariScan

Background  

DNA sequence polymorphisms analysis can provide valuable information on the evolutionary forces shaping nucleotide variation, and provides an insight into the functional significance of genomic regions. The recent ongoing genome projects will radically improve our capabilities to detect specific genomic regions shaped by natural selection. Current available methods and software, however, are unsatisfactory for such genome-wide analysis.     

Genetic tests of ancient asexuality in Root Knot Nematodes reveal recent hybrid origins

Background  

The existence of "ancient asexuals", taxa that have persisted for long periods of evolutionary history without sexual recombination, is both controversial and important for our understanding of the evolution and maintenance of sexual reproduction. A lack of sex has consequences not only for the ecology of the asexual organism but also for its genome. Several genetic signatures are predicted from long-term asexual (apomictic) reproduction including (i) large "allelic" sequence divergence (ii) lack of phylogenetic clustering of "alleles" within morphological species and (iii) decay and loss of genes specific to meiosis and sexual reproduction. These genetic signatures can be hard to assess since it is difficult to demonstrate the allelic nature of very divergent sequences, divergence levels may be complicated by processes such as inter-specific hybridization, and genes may have secondary roles unrelated to sexual reproduction. Apomictic species of Meloidogyne root knot nematodes have been suggested previously to be ancient asexuals. Their relatives reproduce by meiotic parthenogenesis or facultative sexuality, which in combination with the abundance of nematode genomic sequence data, makes them a powerful system in which to study the consequences of reproductive mode on genomic divergence.     

Biased exonization of transposed elements in duplicated genes: A lesson from the TIF-IA gene

Background  

Gene duplication and exonization of intronic transposed elements are two mechanisms that enhance genomic diversity. We examined whether there is less selection against exonization of transposed elements in duplicated genes than in single-copy genes.     

Response to genomic selection: The Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting

Background

Over the last ten years, genomic selection has developed enormously. Simulations and results on real data suggest that breeding values can be predicted with high accuracy using genetic markers alone. However, to reach high accuracies, large reference populations are needed. In many livestock populations or even species, such populations cannot be established when traits are difficult or expensive to record, or when the population size is small. The value of genomic selection is then questionable.

Methods

In this study, we compare traditional breeding schemes based on own performance or progeny information to genomic selection schemes, for which the number of phenotypic records is limiting. Deterministic simulations were performed using selection index theory. Our focus was on the equilibrium response obtained after a few generations of selection. Therefore, we first investigated the magnitude of the Bulmer effect with genomic selection.

Results

Results showed that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP estimated breeding values, and is independent of the accuracy of selection. The reduction in response with genomic selection is greater than with selection based directly on phenotypes without the use of pedigree information, such as mass selection. To maximize the accuracy of genomic estimated breeding values when the number of phenotypic records is limiting, the same individuals should be phenotyped and genotyped, rather than genotyping parents and phenotyping their progeny. When the generation interval cannot be reduced with genomic selection, large reference populations are required to obtain a similar response to that with selection based on BLUP estimated breeding values based on own performance or progeny information. However, when a genomic selection scheme has a moderate decrease in generation interval, relatively small reference population sizes are needed to obtain a similar response to that with selection on traditional BLUP estimated breeding values.

Conclusions

When the trait of interest cannot be recorded on the selection candidate, genomic selection schemes are very attractive even when the number of phenotypic records is limited, because traditional breeding requires progeny testing schemes with long generation intervals in those cases.     

Statistical Viewer: a tool to upload and integrate linkage and association data as plots displayed within the Ensembl genome browser

Background  

To facilitate efficient selection and the prioritization of candidate complex disease susceptibility genes for association analysis, increasingly comprehensive annotation tools are essential to integrate, visualize and analyze vast quantities of disparate data generated by genomic screens, public human genome sequence annotation and ancillary biological databases. We have developed a plug-in package for Ensembl called "Statistical Viewer" that facilitates the analysis of genomic features and annotation in the regions of interest defined by linkage analysis.     

Introgression of a major QTL from an inferior into a superior population using genomic selection

Background

Selection schemes aiming at introgressing genetic material from a donor into a recipient line may be performed by backcross-breeding programs combined with selection to preserve the favourable characteristics of the donor population. This stochastic simulation study investigated whether genomic selection can be effective in preserving a major quantitative trait locus (QTL) allele from a donor line during the backcrossing phase.

Methods

In a simulation study, two fish populations were generated: a recipient line selected for a production trait and a donor line characterized by an enhanced level of disease resistance. Both traits were polygenic, but one major QTL affecting disease resistance was segregating only within the donor line. Backcrossing was combined with three types of selection (for total merit index) among the crossbred individuals: classical selection, genomic selection using genome-wide dense marker maps, and gene-assisted genomic selection. It was assumed that production could be observed directly on the selection candidates, while disease resistance had to be inferred from tested sibs of the selection candidates.

Results

Classical selection was inefficient in preserving the target QTL through the backcrossing phase. In contrast, genomic selection (without specific knowledge of the target QTL) was usually effective in preserving the target QTL, and had higher genetic response to selection, especially for disease resistance. Compared with pure genomic selection, gene-assisted selection had an advantage with respect to disease resistance (28–40% increase in genetic gain) and acted as an extra precaution against loss of the target QTL. However, for total merit index the advantage of gene-assisted genomic selection over genomic selection was lower (4–5% increase in genetic gain).

Conclusion

Substantial differences between introgression programs using classical and genomic selection were observed, and the former was generally inferior with respect to both genetic gain and the ability to preserve the target QTL. Combining genomic selection with gene-assisted selection for the target QTL acted as an extra precaution against loss of the target QTL and gave additional genetic gain for disease resistance. However, the effect on total merit index was limited.     

Simultaneous quantitative and allele-specific expression analysis with real competitive PCR

Background  

For a diploid organism such as human, the two alleles of a particular gene can be expressed at different levels due to X chromosome inactivation, gene imprinting, different local promoter activity, or mRNA stability. Recently, imbalanced allelic expression was found to be common in human and can follow Mendelian inheritance. Here we present a method that employs real competitive PCR for allele-specific expression analysis.     

Analysis of a slow-growing line reveals wide genetic variability of carcass and meat quality-related traits

Background

Extensive genome-wide analyses of many human populations, using microarrays containing hundreds of thousands of single-nucleotide polymorphisms, have provided us with abundant information about global genomic diversity. However, these data can also be used to analyze local variability in individual genomic regions. In this study, we analyzed the variability in two genomic regions carrying the genes of the GSTA and GSTM subfamilies, located on different chromosomes.

Results

Analysis of the polymorphisms in GSTA and GSTM gene clusters showed similarities in their allelic and haplotype diversities. These patterns were similar in three Russian populations and the CEU population of European origin. There were statistically significant differences in all the haploblocks of both the GSTM and GSTA regions when the Russian populations were compared with populations from China and Japan. Most haploblocks also differed between the Russians and Nigerians from Yoruba, but, some of them had similar allelic frequencies. Special attention was paid to SNP rs4986947 from the intron of the GSTA4 gene, which is represented in apes by an A nucleotide. In the Asian and African samples, it was represented only by a G allele, and both allelic variants (G/A) occurred in the Russian and European populations.

Conclusions

The results obtained suggest the presence of common features in the evolutionary histories of the GSTA and GSTM gene regions, and that African subpopulations were involved differently in the formation of the European and Asian human lineages.     

Caryoscope: An Open Source Java application for viewing microarray data in a genomic context

Background  

Microarray-based comparative genome hybridization experiments generate data that can be mapped onto the genome. These data are interpreted more easily when represented graphically in a genomic context.     

The effect of genomic information on optimal contribution selection in livestock breeding programs

Background

Long-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity.

Methods

The effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences.

Results

Optimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data.

Conclusions

The use of genomic estimated breeding values increased genetic gain in optimal contribution selection. Genomic estimated breeding values were more accurate and showed more within-family variation, which led to higher genetic gains for the same restriction on inbreeding. Using genomic relationships to restrict inbreeding provided no additional gain, except in the case of very large full-sib families.     

A statistical approach for array CGH data analysis

Background  

Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile.     

Correlated evolution of nucleotide substitution rates and allelic variation in Mhc-DRB lineages of primates

Background  

The major histocompatibility complex (MHC) is a key model of genetic polymorphism. Selection pressure by pathogens or other microevolutionary forces may result in a high rate of non-synonymous substitutions at the codons specifying the contact residues of the antigen binding sites (ABS), and the maintenance of extreme MHC allelic variation at the population/species level. Therefore, selection forces favouring MHC variability for any reason should cause a correlated evolution between substitution rates and allelic polymorphism. To investigate this prediction, we characterised nucleotide substitution rates and allelic polymorphism (i.e. the number of alleles detected in relation to the number of animals screened) of several Mhc class II DRB lineages in 46 primate species, and tested for a correlation between them.     

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Key message

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials.

Abstract

The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

     

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

Background  

The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.     

Allele frequency changes due to hitch-hiking in genomic selection programs

Background

Genomic selection makes it possible to reduce pedigree-based inbreeding over best linear unbiased prediction (BLUP) by increasing emphasis on own rather than family information. However, pedigree inbreeding might not accurately reflect loss of genetic variation and the true level of inbreeding due to changes in allele frequencies and hitch-hiking. This study aimed at understanding the impact of using long-term genomic selection on changes in allele frequencies, genetic variation and level of inbreeding.

Methods

Selection was performed in simulated scenarios with a population of 400 animals for 25 consecutive generations. Six genetic models were considered with different heritabilities and numbers of QTL (quantitative trait loci) affecting the trait. Four selection criteria were used, including selection on own phenotype and on estimated breeding values (EBV) derived using phenotype-BLUP, genomic BLUP and Bayesian Lasso. Changes in allele frequencies at QTL, markers and linked neutral loci were investigated for the different selection criteria and different scenarios, along with the loss of favourable alleles and the rate of inbreeding measured by pedigree and runs of homozygosity.

Results

For each selection criterion, hitch-hiking in the vicinity of the QTL appeared more extensive when accuracy of selection was higher and the number of QTL was lower. When inbreeding was measured by pedigree information, selection on genomic BLUP EBV resulted in lower levels of inbreeding than selection on phenotype BLUP EBV, but this did not always apply when inbreeding was measured by runs of homozygosity. Compared to genomic BLUP, selection on EBV from Bayesian Lasso led to less genetic drift, reduced loss of favourable alleles and more effectively controlled the rate of both pedigree and genomic inbreeding in all simulated scenarios. In addition, selection on EBV from Bayesian Lasso showed a higher selection differential for mendelian sampling terms than selection on genomic BLUP EBV.

Conclusions

Neutral variation can be shaped to a great extent by the hitch-hiking effects associated with selection, rather than just by genetic drift. When implementing long-term genomic selection, strategies for genomic control of inbreeding are essential, due to a considerable hitch-hiking effect, regardless of the method that is used for prediction of EBV.     

A Hidden Markov Model to estimate population mixture and allelic copy-numbers in cancers using Affymetrix SNP arrays

Background  

Affymetrix SNP arrays can interrogate thousands of SNPs at the same time. This allows us to look at the genomic content of cancer cells and to investigate the underlying events leading to cancer. Genomic copy-numbers are today routinely derived from SNP array data, but the proposed algorithms for this task most often disregard the genotype information available from germline cells in paired germline-tumour samples. Including this information may deepen our understanding of the "true" biological situation e.g. by enabling analysis of allele specific copy-numbers. Here we rely on matched germline-tumour samples and have developed a Hidden Markov Model (HMM) to estimate allelic copy-number changes in tumour cells. Further with this approach we are able to estimate the proportion of normal cells in the tumour (mixture proportion).     

Optimising multistage dairy cattle breeding schemes including genomic selection using decorrelated or optimum selection indices

Background

The prediction of the outcomes from multistage breeding schemes is especially important for the introduction of genomic selection in dairy cattle. Decorrelated selection indices can be used for the optimisation of such breeding schemes. However, they decrease the accuracy of estimated breeding values and, therefore, the genetic gain to an unforeseeable extent and have not been applied to breeding schemes with different generation intervals and selection intensities in each selection path.

Methods

A grid search was applied in order to identify optimum breeding plans to maximise the genetic gain per year in a multistage, multipath dairy cattle breeding program. In this program, different values of the accuracy of estimated genomic breeding values and of their costs per individual were applied, whereby the total breeding costs were restricted. Both decorrelated indices and optimum selection indices were used together with fast multidimensional integration algorithms to produce results.

Results

In comparison to optimum indices, the genetic gain with decorrelated indices was up to 40% less and the proportion of individuals undergoing genomic selection was different. Additionally, the interaction between selection paths was counter-intuitive and difficult to interpret. Independent of using decorrelated or optimum selection indices, genomic selection replaced traditional progeny testing when maximising the genetic gain per year, as long as the accuracy of estimated genomic breeding values was ≥ 0.45. Overall breeding costs were mainly generated in the path "dam-sire". Selecting males was still the main source of genetic gain per year.

Conclusion

Decorrelated selection indices should not be used because of misleading results and the availability of accurate and fast algorithms for exact multidimensional integration. Genomic selection is the method of choice when maximising the genetic gain per year but genotyping females may not allow for a reduction in overall breeding costs. Furthermore, the economic justification of genotyping females remains questionable.     

Selection for complex traits leaves little or no classic signatures of selection

Background

Selection signatures aim to identify genomic regions underlying recent adaptations in populations. However, the effects of selection in the genome are difficult to distinguish from random processes, such as genetic drift. Often associations between selection signatures and selected variants for complex traits is assumed even though this is rarely (if ever) tested. In this paper, we use 8 breeds of domestic cattle under strong artificial selection to investigate if selection signatures are co-located in genomic regions which are likely to be under selection.

Results

Our approaches to identify selection signatures (haplotype heterozygosity, integrated haplotype score and F_ST) identified strong and recent selection near many loci with mutations affecting simple traits under strong selection, such as coat colour. However, there was little evidence for a genome-wide association between strong selection signatures and regions affecting complex traits under selection, such as milk yield in dairy cattle. Even identifying selection signatures near some major loci was hindered by factors including allelic heterogeneity, selection for ancestral alleles and interactions with nearby selected loci.

Conclusions

Selection signatures detect loci with large effects under strong selection. However, the methodology is often assumed to also detect loci affecting complex traits where the selection pressure at an individual locus is weak. We present empirical evidence to suggests little discernible ‘selection signature’ for complex traits in the genome of dairy cattle despite very strong and recent artificial selection.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-246) contains supplementary material, which is available to authorized users.