Ancestral alleles and population origins: inferences depend on mutation rate

Ancestral alleles defined for 70 million cattle variants using a population-based likelihood ratio test

BACKGROUND

The study of ancestral alleles provides insights into the evolutionary history, selection, and genetic structures of a population. In cattle, ancestral alleles are widely used in genetic analyses, including the detection of signatures of selection, determination of breed ancestry, and identification of admixture. Having a comprehensive list of ancestral alleles is expected to improve the accuracy of these genetic analyses. However, the list of ancestral alleles in cattle, especially at the whole genome sequence level, is far from complete. In fact, the current largest list of ancestral alleles (~ 42 million) represents less than 28% of the total number of detected variants in cattle. To address this issue and develop a genomic resource for evolutionary studies, we determined ancestral alleles in cattle by comparing prior derived whole-genome sequence variants to an out-species group using a population-based likelihood ratio test.

RESULTS

Our study determined and makes available the largest list of ancestral alleles in cattle to date (70.1 million) and includes 2.3 million on the X chromosome. There was high concordance (97.6%) of the determined ancestral alleles with those from previous studies when only high-probability ancestral alleles were considered (29.8 million positions) and another 23.5 million high-confidence ancestral alleles were novel, expanding the available reference list to improve the accuracies of genetic analyses involving ancestral alleles. The high concordance of the results with previous studies implies that our approach using genomic sequence variants and a likelihood ratio test to determine ancestral alleles is appropriate.

CONCLUSIONS

Considering the high concordance of ancestral alleles across studies, the ancestral alleles determined in this study including those not previously listed, particularly those with high-probability estimates, may be used for further genetic analyses with reasonable accuracy. Our approach that used predetermined variants in species and the likelihood ratio test to determine ancestral alleles is applicable to other species for which sequence level genotypes are available.

Optimal population size to detect quantitative trait locus in Korean native chicken: a simulation study

Objective

A genomic region associated with a particular phenotype is called quantitative trait loci (QTL). To detect the optimal F₂ population size associated with QTLs in native chicken, we performed a simulation study on F₂ population derived from crosses between two different breeds.

Methods

A total of 15 males and 150 females were randomly selected from the last generation of each F₁ population which was composed of different breed to create two different F₂ populations. The progenies produced from these selected individuals were simulated for six more generations. Their marker genotypes were simulated with a density of 50K at three different heritability levels for the traits such as 0.1, 0.3, and 0.5. Our study compared 100, 500, 1,000 reference population (RP) groups to each other with three different heritability levels. And a total of 35 QTLs were used, and their locations were randomly created.

Results

With a RP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a RP size of 500, two QTLs were detected when the heritability was 0.5. With a RP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability detected five QTLs. To sum up, RP size and heritability play a key role in detecting QTLs in a QTL study. The larger RP size and greater heritability value, the higher the probability of detection of QTLs.

Conclusion

Our study suggests that the use of a large RP and heritability can improve QTL detection in an F₂ chicken population.

Investigation of ancestral alleles in the Bovinae subfamily

BACKGROUND

In evolutionary theory, divergence and speciation can arise from long periods of reproductive isolation, genetic mutation, selection and environmental adaptation. After divergence, alleles can either persist in their initial state (ancestral allele - AA), co-exist or be replaced by a mutated state (derived alleles -DA). In this study, we aligned whole genome sequences of individuals from the Bovinae subfamily to the cattle reference genome (ARS.UCD-1.2) for defining ancestral alleles necessary for selection signatures study.

RESULTS

Accommodating independent divergent of each lineage from the initial ancestral state, AA were defined based on fixed alleles on at least two groups of yak, bison and gayal-gaur-banteng resulting in ~ 32.4 million variants. Using non-overlapping scanning windows of 10 Kb, we counted the AA observed within taurine and zebu cattle. We focused on the extreme points, regions with top 0. 1% (high count) and regions without any occurrence of AA (null count). High count regions preserved gene functions from ancestral states that are still beneficial in the current condition, while null counts regions were linked to mutated ones. For both cattle, high count regions were associated with basal lipid metabolism, essential for survival of various environmental pressures. Mutated regions were associated to productive traits in taurine, i.e. higher metabolism, cell development and behaviors and in immune response domain for zebu.

CONCLUSIONS

Our findings suggest that retaining and losing AA in some regions are varied and made it species-specific with possibility of overlapping as it depends on the selective pressure they had to experience.

Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population

OBJECTIVE

This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h2) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population.

METHODS

A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h2 of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined.

RESULTS

Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h2 was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios.

CONCLUSION

Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

Copy Number Variation in TAS2R Bitter Taste Receptor Genes: Structure, Origin, and Population Genetics

Bitter taste receptor genes (TAS2Rs) harbor extensive diversity, which is broadly distributed across human populations and strongly associated with taste response phenotypes. The majority of TAS2R variation is composed of single-nucleotide polymorphisms. However, 2 closely positioned loci at 12p13, TAS2R43 and -45, harbor high-frequency deletion (Δ) alleles in which genomic segments are absent, resulting in copy number variation (CNV). To resolve their chromosomal structure and organization, we generated maps using long-range contig alignments and local sequencing across the TAS2R43-45 region. These revealed that the deletion alleles (43Δ and 45Δ) are 37.8 and 32.2kb in length, respectively and span the complete coding region of each gene (~1kb) along with extensive up- and downstream flanking sequence, producing separate CNVs at the 2 loci. Comparisons with a chimpanzee genome, which contained intact homologs of TAS2R43, -45, and nearby TAS2Rs, indicated that the deletions evolved recently, through unequal recombination in a cluster of closely related loci. Population genetic analyses in 946 subjects from 52 worldwide populations revealed that copy number ranged from 0 to 2 at both TAS2R43 and TAS2R45, with 43Δ and 45Δ occurring at high global frequencies (0.33 and 0.18). Estimated recombination rates between the loci were low (ρ = 2.7×10(-4); r = 6.6×10(-9)) and linkage disequilibrium was high (D' = 1.0), consistent with their adjacent genomic positioning and recent origin. Geographic variation pointed to an African origin for the deletions. However, no signatures of natural selection were found in population structure or integrated haplotype scores spanning the region, suggesting that patterns of diversity at TAS2R43 and -45 are primarily due to genetic drift.

Simulation of genomes: a review

There is an increasing role of population genetics in human genetic research linking empirical observations with hypotheses about sequence variation due to historical and evolutionary causes. In addition, the data sets are increasing in size, with genome-wide data becoming a common place in many empirical studies. As far as more information is available, it becomes clear that simplest hypotheses are not consistent with data. Simulations will provide the key tool to contrast complex hypotheses on real data by generating simulated data under the hypothetical historical and evolutionary conditions that we want to contrast. Undoubtedly, developing tools for simulating large sequences that at the same time allow simulate natural selection, recombination and complex demography patterns will be of great interest in order to better understanding the trace left on the DNA by different interacting evolutionary forces. Simulation tools will be also essential to evaluate the sampling properties of any statistics used on genome-wide association studies and to compare performance of methods applied at genome-wide scales. Several recent simulation tools have been developed. Here, we review some of the currently existing simulators which allow for efficient simulation of large sequences on complex evolutionary scenarios. In addition, we will point out future directions in this field which are already a key part of the current research in evolutionary biology and it seems that it will be a primary tool in the future research of genome and post-genomic biology.

Mobile DNA elements in primate and human evolution

Roughly 50% of the primate genome consists of mobile, repetitive DNA sequences such as Alu and LINE1 elements. The causes and evolutionary consequences of mobile element insertion, which have received considerable attention during the past decade, are reviewed in this article. Because of their unique mutational mechanisms, these elements are highly useful for answering phylogenetic questions. We demonstrate how they have been used to help resolve a number of questions in primate phylogeny, including the human-chimpanzee-gorilla trichotomy and New World primate phylogeny. Alu and LINE1 element insertion polymorphisms have also been analyzed in human populations to test hypotheses about human evolution and population affinities and to address forensic issues. Finally, these elements have had impacts on the genome itself. We review how they have influenced fundamental ongoing processes like nonhomologous recombination, genomic deletion, and X chromosome inactivation.