An improved hidden Markov model for the characterization of homozygous-by-descent segments in individual genomes

Inbreeding results from the mating of related individuals and has negative consequences because it brings together deleterious variants in one individual. Genomic estimates of the inbreeding coefficients are preferred to pedigree-based estimators as they measure the realized inbreeding levels and they are more robust to pedigree errors. Several methods identifying homozygous-by-descent (HBD) segments with hidden Markov models (HMM) have been recently developed and are particularly valuable when the information is degraded or heterogeneous (e.g., low-fold sequencing, low marker density, heterogeneous genotype quality or variable marker spacing). We previously developed a multiple HBD class HMM where HBD segments are classified in different groups based on their length (e.g., recent versus old HBD segments) but we recently observed that for high inbreeding levels with many HBD segments, the estimated contributions might be biased towards more recent classes (i.e., associated with large HBD segments) although the overall estimated level of inbreeding remained unbiased. We herein propose an updated multiple HBD classes model in which the HBD classification is modeled in successive nested levels. In each level, the rate specifying the expected length of HBD segments, and that is directly related to the number of generations to the ancestors, is distinct. The non-HBD classes are now modeled as a mixture of HBD segments from later generations and shorter non-HBD segments (i.e., both with higher rates). The updated model had better statistical properties and performed better on simulated data compared to our previous version. We also show that the parameters of the model are easier to interpret and that the model is more robust to the choice of the number of classes. Overall, the new model results in an improved partitioning of inbreeding in different HBD classes and should be preferred in applications relying on the length of estimated HBD segments.

Familial heterogeneity in breast cancer predisposition: a study of 22 Utah families

The problem of “missing heritability” in genome-wide analyses of complex diseases is thought to be attributable to some combination of: rare variants of moderate to large effect, common variants of very small effect, and epigenetic, epistatic, or shared environmental effects. Rare variants do not affect large numbers of people by definition, but identified genes and pathways frequently lead to important insights into pathogenesis, and become targets of chemoprevention or therapy. Family studies remain an efficient way to identify rare variants with sizable effects on disease risk. We present a genome-wide study of breast cancer in 22 large high-risk families including 154 women diagnosed with breast cancer. Appropriate marker spacing was achieved by simulation studies of founder haplotypes to reduce the chance that linkage disequilibrium produced spurious linkage peaks. For each family, we generated 100 simulations of null linkage genome-wide to estimate the probability that individual results were due to chance. We identified a total of 12 putative susceptibility regions with per-family genome-wide probability < 0.05. These regions were located on 10 chromosomes; 10 of the 22 families showed linkage at these locations; two or more families showed linkage to 6 regions on 5 chromosomes (4q, 5q, 6p, 14q, 18p, and 18q). These results indicate that there is considerable heterogeneity among families in genomic regions and thus variants predisposing to breast cancer. Moreover, they suggest that uncommon high– or medium-risk genetic variants remain to be found, and that family designs can be an efficient way to identify them.

Characterizing polymorphism and multiplex feasibility of 142 microsatellite markers from a commercial German Landrace line (short communication)

In using microsatellites to perform linkage studies or parentage testing in pigs the effectiveness of genotyping can substantially be enhanced by multiplex polymerase chain reactions (PCR), i.e. joint amplification of several microsatellites. However, and in contrast to other species, little has been published on specific multiplex sets of porcine microsatellites and their amplification conditions. As a by-product of a whole genome scan in purebred Landrace families we present information on 142 porcine microsatellites, covering all chromosomes with an average marker spacing of 15 cM. In total 96 of these markers were amplified in 45 different multiplex sets (duplex and triplex reactions) and 46 markers were amplified alone. Details of PCR conditions, observed fragment lengths in Landrace and allele frequencies are given and it is shown in detail, which microsatellites have been combined together. These informations may serve as an aid for the development of more complex multiplex sets, comprising a higher number of simultaneously amplified microsatellites.

On the prediction of simultaneous inbreeding coefficients at multiple loci

A new deterministic method for predicting simultaneous inbreeding coefficients at three and four loci is presented. The method involves calculating the conditional probability of IBD (identical by descent) at one locus given IBD at other loci, and multiplying this probability by the prior probability of the latter loci being simultaneously IBD. The conditional probability is obtained applying a novel regression model, and the prior probability from the theory of digenic measures of Weir and Cockerham. The model was validated for a finite monoecious population mating at random, with a constant effective population size, and with or without selfing, and also for an infinite population with a constant intermediate proportion of selfing. We assumed discrete generations. Deterministic predictions were very accurate when compared with simulation results, and robust to alternative forms of implementation. These simultaneous inbreeding coefficients were more sensitive to changes in effective population size than in marker spacing. Extensions to predict simultaneous inbreeding coefficients at more than four loci are now possible.

Merging Carrot Linkage Groups based on Conserved Dominant AFLP Markers in F2 Populations

Markers were placed on linkage groups, ordered, and merged for two unrelated F2 populations of carrot (Daucus carota L.). Included were 277 and 242 dominant Amplified fragment-length polymorphism (AFLP) markers and 10 and eight codominant markers assigned to the nine linkage groups of Brasilia × HCM and B493 × QAL F2 populations, respectively. The merged linkage groups were based on two codominant markers and 28 conserved dominant AFLP markers (based upon sequence and size) shared by both populations. The average marker spacing was 4.8 to 5.5 cM in the four parental coupling phase maps. The average marker spacing in the six merged linkage groups was 3.75 cM with maximum gaps among linkage groups ranging from 8.0 to 19.8 cM. Gaps of a similar size were observed with the linkage coupling phase maps of the parents, indicating that linkage group integration did not double the bias which comes with repulsion phase mapping. Three out of nine linkage groups of carrot were not merged due to the absence of common markers. The six merged linkage groups incorporated similar numbers of AFLP fragments from the four parents, further indicating no significant increase in bias expected with repulsion phase linkage. While other studies have merged linkage maps with shared AFLPs of similar size, this is the first report to use shared AFLPs with highly conserved sequence to merge linkage maps in carrot. The genome coverage in this study is suitable to apply quantitative trait locus analysis and to construct a cross-validated consensus map of carrot, which is an important step toward an integrated map of carrot.

High-Resolution Mapping of Quantitative Trait Loci by Selective Recombinant Genotyping

Selective recombinant genotyping (SRG) is a three-stage procedure for high-resolution mapping of a QTL that has previously been mapped to a known confidence interval (target C.I.). In stage 1, a large mapping population is accessed and phenotyped, and a proportion, P, of the high and low tails is selected. In stage 2, the selected individuals are genotyped for a pair of markers flanking the target C.I., and a group of R individuals carrying recombinant chromosomes in the target interval are identified. In stage 3, the recombinant individuals are genotyped for a set of M markers spanning the target C.I. Extensive simulations showed that: (1) Standard error of QTL location (SEQTL) decreased when QTL effect (d) or population size (N) increased, but was constant for given “power factor” (PF = d2N); (2) increasing the proportion selected in the tails beyond 0.25 had only a negligible effect on SEQTL; and (3) marker spacing in the target interval had a remarkably powerful effect on SEQTL, yielding a reduction of up to 10-fold in going from highest (24 cM) to lowest (0.29 cM) spacing at given population size and QTL effect. At the densest marker spacing, SEQTL of 1.0-0.06 cM were obtained at PF = 500-16,000. Two new genotyping procedures, the half-section algorithm and the golden section/half-section algorithm, allow the equivalent of complete haplotyping of the target C.I. in the recombinant individuals to be achieved with many fewer data points than would be required by complete individual genotyping.

Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

Speed congenics: accelerated genome recovery using genetic markers

Genetic markers throughout the genome can be used to speed up ‘recovery’ of the recipient genome in the backcrossing phase of the construction of a congenic strain. The prediction of the genomic proportion during backcrossing depends on the assumptions regarding the distribution of chromosome segments, the population structure, the marker spacing and the selection strategy. In this study simulation was used to investigate the rate of recovery of the recipient genome for a mouse, Drosophila and Arabidopsis genome. It was shown that an incorrect assumption of a binomial distribution of chromosome segments, and failing to take account of a reduction in variance in genomic proportion due to selection, can lead to a downward bias of up to two generations in the estimation of the number of generations required for the formation of a congenic strain.