Estimating genetic diversity across the neutral genome with the use of dense marker maps

<p>Abstract</p> <p>Background</p> <p>With the advent of high throughput DNA typing, dense marker maps have become available to investigate genetic diversity on specific regions of the genome. The aim of this paper was to compare two marker based estimates of the genetic diversity in specific genomic regions lying in between markers: IBD-based genetic diversity and heterozygosity.</p> <p>Methods</p> <p>A computer simulated population was set up with individuals containing a single 1-Morgan chromosome and 1665 SNP markers and from this one, an additional population was produced with a lower marker density i.e. 166 SNP markers. For each marker interval based on adjacent markers, the genetic diversity was estimated either by IBD probabilities or heterozygosity. Estimates were compared to each other and to the true genetic diversity. The latter was calculated for a marker in the middle of each marker interval that was not used to estimate genetic diversity.</p> <p>Results</p> <p>The simulated population had an average minor allele frequency of 0.28 and an LD (r²) of 0.26, comparable to those of real livestock populations. Genetic diversities estimated by IBD probabilities and by heterozygosity were positively correlated, and correlations with the true genetic diversity were quite similar for the simulated population with a high marker density, both for specific regions (r = 0.19-0.20) and large regions (r = 0.61-0.64) over the genome. For the population with a lower marker density, the correlation with the true genetic diversity turned out to be higher for the IBD-based genetic diversity.</p> <p>Conclusions</p> <p>Genetic diversities of ungenotyped regions of the genome (i.e. between markers) estimated by IBD-based methods and heterozygosity give similar results for the simulated population with a high marker density. However, for a population with a lower marker density, the IBD-based method gives a better prediction, since variation and recombination between markers are missed with heterozygosity.</p

A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps

<p>Abstract</p> <p>Background</p> <p>In the case of an autosomal locus, four transmission events from the parents to progeny are possible, specified by the grand parental origin of the alleles inherited by this individual. Computing the probabilities of these transmission events is essential to perform QTL detection methods.</p> <p>Results</p> <p>A fast algorithm for the estimation of these probabilities conditional to parental phases has been developed. It is adapted to classical QTL detection designs applied to outbred populations, in particular to designs composed of half and/or full sib families. It assumes the absence of interference.</p> <p>Conclusion</p> <p>The theory is fully developed and an example is given.</p

A genetic algorithm based method for stringent haplotyping of family data

<p>Abstract</p> <p>Background</p> <p>The linkage phase, or haplotype, is an extra level of information that in addition to genotype and pedigree can be useful for reconstructing the inheritance pattern of the alleles in a pedigree, and computing for example Identity By Descent probabilities. If a haplotype is provided, the precision of estimated IBD probabilities increases, as long as the haplotype is estimated without errors. It is therefore important to only use haplotypes that are strongly supported by the available data for IBD estimation, to avoid introducing new errors due to erroneous linkage phases.</p> <p>Results</p> <p>We propose a genetic algorithm based method for haplotype estimation in family data that includes a stringency parameter. This allows the user to decide the error tolerance level when inferring parental origin of the alleles. This is a novel feature compared to existing methods for haplotype estimation. We show that using a high stringency produces haplotype data with few errors, whereas a low stringency provides haplotype estimates in most situations, but with an increased number of errors.</p> <p>Conclusion</p> <p>By including a stringency criterion in our haplotyping method, the user is able to maintain the error rate at a suitable level for the particular study; one can select anything from haplotyped data with very small proportion of errors and a higher proportion of non-inferred haplotypes, to data with phase estimates for every marker, when haplotype errors are tolerable. Giving this choice makes the method more flexible and useful in a wide range of applications as it is able to fulfil different requirements regarding the tolerance for haplotype errors, or uncertain marker-phases.</p

A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation

<p>Abstract</p> <p>Background</p> <p>Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation.</p> <p>Methods</p> <p>An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis.</p> <p>Results</p> <p>Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored.</p> <p>Conclusions</p> <p>The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.</p

Polymorphisms of the prion protein gene and their effects on litter size and risk evaluation for scrapie in Chinese Hu sheep

It is well known that scrapie is a fatal, neurodegenerative disease in sheep and goat, which belongs to the group of transmissible spongiform encephalopathies (TSEs) or prion diseases. It has been confirmed that the polymorphisms of prion protein gene (PRNP) at codons 136, 154, and 171 have strong relationship with scrapie in sheep. In the present study, nine polymorphisms of PRNP at codons 136, 154, and 171 and other six loci (at codons 101, 112, 127, 137, 138, and 152) were detected in 180 Chinese Hu sheep. All the alleles at codons 136, 154, and 171 have been identified and resulted in three new genotypes. The frequencies of predominant alleles were 85% (A136), 99.40% (R154), and 37.78% (Q171), respectively. The predominant haplotype ARQ has a relatively high frequency of 57.77%. The frequencies of dominant genotypes of ARR/ARQ and ARQ/ARQ were 30 and 26.67%, respectively. Three new found genotypes named ARQ/TRK, ARQ/TRR, and TRR/TRQ had the same lower frequencies (0.56%). The relationship of PRNP genotype with scrapie risk and litter size showed that the predominant genotypes are corresponded to the risk score of R1 (1.67%), R2 (32.22%), and R3 (42.22%). Just at the first parity, the individuals with ARH/ARH genotype had significantly larger litter size than the mean value and those with ARQ/ARQ and ARR/ARQ genotypes. In short, this study provided preliminary information about alleles and genotypes of PRNP in Chinese Hu sheep. It could be concluded that Hu sheep has a low susceptibility to natural scrapie, and the predominant PRNP genotype at least has no significant effect on litter size

Stressful conditions reveal decrease in size, modification of shape but relatively stable asymmetry in bumblebee wings

Human activities can generate a wide variety of direct and indirect effects on animals, which can manifest as environmental and genetic stressors. Several phenotypic markers have been proposed as indicators of these stressful conditions but have displayed contrasting results, depending, among others, on the phenotypic trait measured. Knowing the worldwide decline of multiple bumblebee species, it is important to understand these stressors and link them with the drivers of decline. We assessed the impact of several stressors (i.e. natural toxin-, parasite-, thermic- and inbreeding-stress) on both wing shape and size and their variability as well as their directional and fluctuating asymmetries. The total data set includes 650 individuals of Bombus terrestris (Hymenoptera: Apidae). Overall wing size and shape were affected by all the tested stressors. Except for the sinigrin (e.g. glucosinolate) stress, each stress implies a decrease of wing size. Size variance was affected by several stressors, contrary to shape variance that was affected by none of them. Although wing size directional and fluctuating asymmetries were significantly affected by sinigrin, parasites and high temperatures, neither directional nor fluctuating shape asymmetry was significantly affected by any tested stressor. Parasites and high temperatures led to the strongest phenotype modifications. Overall size and shape were the most sensitive morphological traits, which contrasts with the common view that fluctuating asymmetry is the major phenotypic marker of stress

Estimating Heritabilities and Genetic Correlations: Comparing the ‘Animal Model’ with Parent-Offspring Regression Using Data from a Natural Population

Quantitative genetic parameters are nowadays more frequently estimated with restricted maximum likelihood using the ‘animal model’ than with traditional methods such as parent-offspring regressions. These methods have however rarely been evaluated using equivalent data sets. We compare heritabilities and genetic correlations from animal model and parent-offspring analyses, respectively, using data on eight morphological traits in the great reed warbler (Acrocephalus arundinaceus). Animal models were run using either mean trait values or individual repeated measurements to be able to separate between effects of including more extended pedigree information and effects of replicated sampling from the same individuals. We show that the inclusion of more pedigree information by the use of mean traits animal models had limited effect on the standard error and magnitude of heritabilities. In contrast, the use of repeated measures animal model generally had a positive effect on the sampling accuracy and resulted in lower heritabilities; the latter due to lower additive variance and higher phenotypic variance. For most trait combinations, both animal model methods gave genetic correlations that were lower than the parent-offspring estimates, whereas the standard errors were lower only for the mean traits animal model. We conclude that differences in heritabilities between the animal model and parent-offspring regressions were mostly due to the inclusion of individual replicates to the animal model rather than the inclusion of more extended pedigree information. Genetic correlations were, on the other hand, primarily affected by the inclusion of more pedigree information. This study is to our knowledge the most comprehensive empirical evaluation of the performance of the animal model in relation to parent-offspring regressions in a wild population. Our conclusions should be valuable for reconciliation of data obtained in earlier studies as well as for future meta-analyses utilizing estimates from both traditional methods and the animal model

Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

BACKGROUND: Structure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas. RESULTS: An algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries. CONCLUSION: The seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website

A Single Basis for Developmental Buffering of Drosophila Wing Shape

The nature of developmental buffering processes has been debated extensively, based on both theoretical reasoning and empirical studies. In particular, controversy has focused on the question of whether distinct processes are responsible for canalization, the buffering against environmental or genetic variation, and for developmental stability, the buffering against random variation intrinsic in developmental processes. Here, we address this question for the size and shape of Drosophila melanogaster wings in an experimental design with extensively replicated and fully controlled genotypes. The amounts of variation among individuals and of fluctuating asymmetry differ markedly among genotypes, demonstrating a clear genetic basis for size and shape variability. For wing shape, there is a high correlation between the amounts of variation among individuals and fluctuating asymmetry, which indicates a correspondence between the two types of buffering. Likewise, the multivariate patterns of shape variation among individuals and of fluctuating asymmetry show a close association. For wing size, however, the amounts of individual variation and fluctuating asymmetry are not correlated. There was a significant link between the amounts of variation between wing size and shape, more so for fluctuating asymmetry than for variation among individuals. Overall, these experiments indicate a considerable degree of shared control of individual variation and fluctuating asymmetry, although it appears to differ between traits

Polymorphisms in the selectin gene cluster are associated with fertility and survival time in a population of Holstein Friesian cows

Selectins are adhesion molecules, which mediate attachment between leucocytes and endothelium. They aid extravasation of leucocytes from blood into inflamed tissue during the mammary gland’s response to infection. Selectins are also involved in attachment of the conceptus to the endometrium and subsequent placental development. Poor fertility and udder health are major causes for culling dairy cows. The three identified bovine selectin genes SELP, SELL and SELE are located in a gene cluster. SELP is the most polymorphic of these genes. Several SNP in SELP and SELE are associated with human vascular disease, while SELP SNP rs6127 has been associated with recurrent pregnancy loss in women. This study describes the results of a gene association study for SNP in SELP (n = 5), SELL (n = 2) and SELE (n = 1) with fertility, milk production and longevity traits in a population of 337 Holstein Friesian dairy cows. Blood samples for PCR-RFLP were collected at 6 months of age and animals were monitored until either culling or 2,340 days from birth. Three SNP in SELPEx4-6 formed a haplotype block containing a Glu/Ala substitution at rs42312260. This region was associated with poor fertility and reduced survival times. SELPEx8 (rs378218397) coded for a Val475Met variant locus in the linking region between consensus repeats 4 and 5, which may influence glycosylation. The synonymous SNP rs110045112 in SELEEx14 deviated from Hardy Weinberg equilibrium. For both this SNP and rs378218397 there were too few AA homozygotes present in the population and AG heterozygotes had significantly worse fertility than GG homozygotes. Small changes in milk production associated with some SNP could not account for the reduced fertility and only SELPEx6 showed any association with somatic cell count. These results suggest that polymorphisms in SELP and SELE are associated with the likelihood of successful pregnancy, potentially through compromised implantation and placental development