Estimation of Recombination Rate and Maternal Linkage Disequilibrium in Half-Sibs

Hampel, Alexander; Teuscher, F.; Gomez‐Raya, Luis; Doschoris, Michael; Wittenburg, Dörte

doi:10.3389/fgene.2018.00186

Cited by 10 publications

(13 citation statements)

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“…Let denote the number of markers on a chromosome. The recombination rate between each pair of markers and , ( ) was estimated using an expectation–maximization approach which relies on likelihood theory [ 21 – 23 ]. This approach uses sire haplotypes that have been reconstructed within each half-sib family by hsphase and progeny genotypes.…”

Section: Methodsmentioning

confidence: 99%

“…Estimating recombination frequency across all intra-chromosomal marker pairs allowed the identification of markers that are misplaced in the current genome assembly. For this purpose, SNPs with a markedly high recombination rate with the neighboring markers were identified following Hampel et al [ 23 ]. Briefly, the mean recombination rate of was calculated for all SNPs , and the mean of was taken for .…”

Section: Methodsmentioning

confidence: 99%

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Male recombination map of the autosomal genome in German Holstein

Qanbari

Wittenburg

2020

Genet Sel Evol

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Background Recombination is a process by which chromosomes are broken and recombine to generate new combinations of alleles, therefore playing a major role in shaping genome variation. Recombination frequencies ($$\theta$$ θ ) between markers are used to construct genetic maps, which have important implications in genomic studies. Here, we report a recombination map for 44,696 autosomal single nucleotide polymorphisms (SNPs) according to the coordinates of the most recent bovine reference assembly. The recombination frequencies were estimated across 876 half-sib families with a minimum number of 39 and maximum number of 4236 progeny, comprising over 367 K genotyped German Holstein animals. Results Genome-wide, over 8.9 million paternal recombination events were identified by investigating adjacent markers. The recombination map spans 24.43 Morgan (M) for a chromosomal length of 2486 Mbp and an average of ~ 0.98 cM/Mbp, which concords with the available pedigree-based linkage maps. Furthermore, we identified 971 putative recombination hotspot intervals (defined as $$\theta$$ θ > 2.5 standard deviations greater than the mean). The hotspot regions were non-uniformly distributed as sharp and narrow peaks, corresponding to ~ 5.8% of the recombination that has taken place in only ~ 2.4% of the genome. We verified genetic map length by applying a likelihood-based approach for the estimation of recombination rate between all intra-chromosomal marker pairs. This resulted in a longer autosomal genetic length for male cattle (25.35 cM) and in the localization of 51 putatively misplaced SNPs in the genome assembly. Conclusions Given the fact that this map is built on the coordinates of the ARS-UCD1.2 assembly, our results provide the most updated genetic map yet available for the cattle genome.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Male recombination map of the autosomal genome in German Holstein

Qanbari

Wittenburg

2020

Genet Sel Evol

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“…For instance, a window of about 1 Mbp length was detected on BTA6. A statistical reason for this complication lies in the high multicollinearity among predictor variables due to linkage and linkage disequilibrium (LD) between SNPs (e.g., [3]). Region-based aggregation tests in biologically relevant regions (e.g., genes; [4]) or fine-mapping approaches in independent partitions of the genome [5] have been suggested as powerful options.…”

Section: Introductionmentioning

confidence: 99%

Design of experiments for fine-mapping quantitative trait loci in livestock populations

et al. 2020

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Background: Single nucleotide polymorphisms (SNPs) which capture a significant impact on a trait can be identified with genome-wide association studies. High linkage disequilibrium (LD) among SNPs makes it difficult to identify causative variants correctly. Thus, often target regions instead of single SNPs are reported. Sample size has not only a crucial impact on the precision of parameter estimates, it also ensures that a desired level of statistical power can be reached. We study the design of experiments for fine-mapping of signals of a quantitative trait locus in such a target region. Methods: A multi-locus model allows to identify causative variants simultaneously, to state their positions more precisely and to account for existing dependencies. Based on the commonly applied SNP-BLUP approach, we determine the z-score statistic for locally testing non-zero SNP effects and investigate its distribution under the alternative hypothesis. This quantity employs the theoretical instead of observed dependence between SNPs; it can be set up as a function of paternal and maternal LD for any given population structure. Results: We simulated multiple paternal half-sib families and considered a target region of 1 Mbp. A bimodal distribution of estimated sample size was observed, particularly if more than two causative variants were assumed. The median of estimates constituted the final proposal of optimal sample size; it was consistently less than sample size estimated from single-SNP investigation which was used as a baseline approach. The second mode pointed to inflated sample sizes and could be explained by blocks of varying linkage phases leading to negative correlations between SNPs. Optimal sample size increased almost linearly with number of signals to be identified but depended much stronger on the assumption on heritability. For instance, three times as many samples were required if heritability was 0.1 compared to 0.3. An R package is provided that comprises all required tools. Conclusions: Our approach incorporates information about the population structure into the design of experiments. Compared to a conventional method, this leads to a reduced estimate of sample size enabling the resource-saving design of future experiments for fine-mapping of candidate variants.

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“…For instance, a window of about 1Mbp length was detected on BTA6. A statistical reason for this complication lies in the high multicollinearity among predictor variables due to linkage and linkage disequilibrium (LD) between SNPs (e.g., Hampel et al ., 2018). Region-based aggregation tests in biologically relevant regions (e.g., genes; Lee et al ., 2014) or fine-mapping approaches in independent partitions of the genome (Schaid et al ., 2018) have been suggested as powerful options.…”

Section: Introductionmentioning

confidence: 99%

Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations

Wittenburg

Bonk

Doschoris

et al. 2019

Preprint

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22Single nucleotide polymorphisms (SNPs) which capture a significant impact on 23 a trait can be identified with genome-wide association studies. High linkage disequi-24 librium (LD) among SNPs makes it difficult to identify causative variants correctly. 25Thus, often target regions instead of single SNPs are reported. Sample size has not 26 only a crucial impact on the precision of parameter estimates, it also ensures that a 27 desired level of statistical power can be reached. We study the design of experiments 28 for fine-mapping of signals of a quantitative trait locus in such a target region. 29A multi-locus model allows to identify causative variants simultaneously, to state 30 their positions more precisely and to account for existing dependencies. Based on 31 the commonly applied SNP-BLUP approach, we determine the z-score statistic for 32 locally testing non-zero SNP effects and investigate its distribution under the al-33 ternative hypothesis. This quantity employs the theoretical instead of observed 34 dependence between SNPs; it can be set up as a function of paternal and maternal 35 LD for any given population structure. 36 We simulated multiple paternal half-sib families and considered a target region of 37 1 Mbp. A bimodal distribution of estimated sample size was observed, particu-38 larly if more than two causative variants were assumed. The median of estimates 39 constituted the final proposal of optimal sample size; it was consistently less than 40 sample size estimated from single-SNP investigations which was used as a baseline 41 approach. The second mode pointed to inflated sample sizes and could be explained 42 by blocks of varying linkage phases leading to negative correlations between SNPs. 43Optimal sample size increased almost linearly with number of signals to be identified 44 but depended much stronger on the assumption on heritability. For instance, three 45 times as many samples were required if heritability was 0.1 compared to 0.3. These 46 results enable the resource-saving design of future experiments for fine-mapping of 47

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Estimation of Recombination Rate and Maternal Linkage Disequilibrium in Half-Sibs

Cited by 10 publications

References 44 publications

Male recombination map of the autosomal genome in German Holstein

Male recombination map of the autosomal genome in German Holstein

Design of experiments for fine-mapping quantitative trait loci in livestock populations

Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations

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