Effective Population Size and Signatures of Selection Using Bovine 50K SNP Chips in Korean Native Cattle (Hanwoo)

Abstract

Inferring the effective population size and the pattern of selection signatures is of interest both from an evolutionary perspective and to improve models for mapping of quantitative trait genes. We used DNA samples of 61 sires and 486 progeny of the Hanwoo, genotyped by the Illumina Bovine SNP50 BeadChip, to analyze the genetic structure. Our study showed a persistent decline in effective population size throughout the period considered, but suggested a marked decline at one distinctive time point (100th generation) and two sharp decline intervals (50th-25th generation and 25th-10th generation). This pattern can be explained by Hanwoo formation and the modern breeding program. Our results revealed 95 regions exhibiting the footprint of recent positive selection at a threshold level of 0.01. We found an overlap of the 11 core regions presenting top P-values and those that had previously been identified as harboring quantitative trait loci from other breeds. The information generated from this study can be used to better understand the mechanism of selection in Hanwoo breeding, and provide important implications for the design and application of association studies in the Hanwoo population.

Keywords

cattle linkage disequilibrium effective population size selection signature

Introduction

Linkage disequilibrium (LD) has recently become the focus of intense study because so many factors affect it and are affected by it. LD describes the nonrandom association of alleles at two or more loci. Information on the structure of LD at the population level is critical for interpreting and applying results of genome-wide association studies (GWAS) and genomic selection for the improvement of economically important traits.^1,2 LD can help to unravel the genetic relationships among diverse breeds and the phylogenetic relationships between domestic animals and their wild ancestors. LD can be used to enable effective exploitation of the potentially significant historical events that occurred during domestication, breed formation, and ongoing selection (eg, bottleneck effects, genetic drift, and selective sweeps).

A wide variety of statistics have been proposed to measure the amount of LD, and these have different strengths, depending on the context. In the current literature, the two most popular measures of LD between pairs of biallelic markers are D’ and r². Typically, r² is preferred when the focus is on the predictability of one polymorphism given the other, and hence it is often used in power studies for association designs. D’, by contrast, is the measure of choice to assess recombination patterns; thus, haplotype blocks have often been defined on the basis of D’³

Effective population size (Ne) is widely regarded as one of the most important parameters in both evolutionary biology and conservation biology.⁴ In particular, the action of selection means that Ne varies across the genome, and advances in genomic techniques give new insight into how selection shapes Ne. There are three different ways to measure Ne: 1) the change in probability of identity by descent (inbreeding effective size); 2) the change in variance in allele frequency (variance effective size); and 3) the rate of loss of heterozygosity (eigenvalue effective size).⁵ In this study, we estimated Ne by the original single-sample method based on random LD that arises by chance in each generation in a finite population.

Detection of selective sweeps is useful because the effect of selection on the distribution of genetic variation can be difficult to distinguish from the pattern of genetic variation that arises after certain demographic events.^6,7 The long-range haplotype (LRH) test examines the relationship between allele frequency and the extent of LD.⁸ LRH measures the decay of identity, as a function of distance, of haplotypes that carry a specified “core” allele at one end. Alternative methods to detect selective sweeps include Tajima's D- and Fay and Wu's H-test.^9,10 Both these tests use site frequency spectrum summaries to identify selection signatures. The integrated Haplotype Score (iHS) is an extension of LRH,¹¹ and is more powerful than the Tajima's D- or Fay and Wu's H-test for selected mutations that are still segregating in the population. However, iHS is limited by low single nucleotide polymorphism (SNP) density and the inability to completely specify ancestral SNP allele states.¹²

The Korean native cattle (Hanwoo) is known for its marbled fat, tenderness, juiciness, and characteristic flavor. Hanwoo originated from a hybrid of Bos taurus × Bos zebu that migrated to the Korean peninsula in 4000 BC. Hanwoo has been intensively selected during the last few centuries, especially in the recent decades since the implementation of progeny-test-based breeding programs in the 1980s.^13–15 In this study, we use SNP data generated with the Illumina Bovine SNP50K BeadChip to explore some properties of r² and D’ as the most common measures of LD. The extent of LD is presented along with the estimation of ancestral population size for different generations and the genome-wide footprints of positive selection. This would provide a framework for evaluating the genetic structure and history of this cattle breed, and facilitate detailed studies on the identified candidate genes.

Materials and Methods

DNA samples

SNP markers throughout the Hanwoo genome were genotyped. Samples were collected from 547 bulls born from spring through fall of 2006 in Seosan, South Korea. The bulls were 61 sires and 486 progeny. The size of the 61 sire families ranged from 2 to 18 bulls per sire, with an average of 8 bulls. Pedigree information was obtained from the Hanwoo Improvement Center of National Agricultural Cooperative Federation, Seosan. The mean kinship (coefficient of coancestry) is 0.0119, and the mean inbreeding coefficient is 0.4%, determined using pedigree information from up to four generations. For the purposes of this study, these values were assumed to be zero. Coancestry and inbreeding coefficients were computed using coancestry, inbreeding (F), and contribution (CFC) programs.¹⁶

Ethics committee approval for treatment of animals was not required, as all samples used in this study had been collected by veterinarians for routine purposes, and were reused for the research presented here.

Selection and genotyping of markers

A total of 54,001 SNPs were screened using the Illumina Bovine SNP50K BeadChip.¹⁷ SNPs were analyzed using an Illumina Bead-Station 5.2 genotyping instrument (Illumina Inc.), and genotyping was performed using BeadStudio 3.0 (Illumina Inc.) software. SNPs were removed on the basis of the following criteria: 1) Markers were filtered to exclude loci assigned to unmapped contigs, or unpositioned according to the latest reference assembly of the bovine genome Btau 4.0, and markers on the X and Y chromosomes. 2) Monomorphic SNPs and those with minor allele frequency smaller than 0.05 were filtered, because it is known that LD between SNPs with a low minor allele frequency is biased upwards, and thus high-frequency polymorphisms are preferable for accurate estimation of LD.¹⁸ 3) Animals with genotype completeness smaller than 90% were excluded. 4) Markers with significant departure from H-W equilibrium (P < 0.001) were also excluded.

Haplotype phase reconstruction

Haplotype phases were inferred from pedigree by a localized haplotype clustering model (LHCM) method,¹⁹ which is a family rule (Mendelian segregation and linkage)-based algorithm. Only maternally inherited haplotypes were used for further detailed analyses, in order to minimize the effect of over-representation of paternally inherited haplotypes within pedigrees of the bulls.

SNP annotation information

Annotation information was based on genomic positions of Bos taurus genes (Btau 4.0 build) that were obtained from Biomart.²⁰ We divided SNP pairs into the following functional categories: nonsynonymous, synonymous, within an intron or mRNA untranslated region (UTR), or within 2 kb of a gene.¹¹ SNPs in any of these functional classes were the intragenic group, and the remaining SNPs were assigned to the intergenic SNP group.

Measures of linkage disequilibrium

We used the GOLD 1.1.0 program²¹ to construct LD maps from the maternally inherited haplotypes, thus avoiding the over-representation of paternally inherited haplotypes within the primarily male pedigrees. GOLD computes Lewontin's disequilibrium coefficient D’ and r² for all pairwise SNPs.^22,23 LD was calculated for intragenic SNPs and intergenic SNPs separately to investigate whether LD within genes is higher or lower than between genes.

Past effective population size using LD

To observe the past effective population size (Ne), Ne was calculated based on the relationship between the effective population size (Ne), the recombination rate, and LD (r²). In the absence of mutation, the function of LD is

y_{i} = \frac{1}{(1 + 4 N e c)} + e_{i}

where y_i = (r² – 1/n), where n is the number of sampled haplotypes, the recombination rate c is in Morgans, Ne is the population size 1/(2c) generations ago, and e_i is the residual.²⁴ The recombination rate (c) was inferred based on the ratio between the physical size of each chromosome and length of the corresponding linkage map (NCBI Map Viewer²⁵). The past effective population size was estimated approximately in each chromosome. Under the assumption of linear population expansion, the effective population size (Ne) was fitted by nonlinear least squares regression in the program R,²⁶ using pairwise r² and SNP distance bin (0.1 Mb) within ~0.1–15 Mb to estimate Ne of the past generations. However, for the calculation of average r² for marker distance <100 kb, the SNP distance bin was 0.01 Mb.

Long-range haplotype (LRH) test

Under neutral evolution, positive selection is expected to accelerate the frequency of an advantageous allele faster than recombination can break down LD at the selected haplotype.²⁷

The LRH test calculates the relative extended haplotype homozygosity (REHH), and assesses the significance of REHH by use of simulations, as described in Sabeti et al.⁸ In brief, core regions (a pair of SNPs to be in strong LD if the upper 95% confidence bound of D’ is between 0.7 and 0.98) were identified throughout the genome. Because a recombination event should have occurred in a two-SNP core region with a core haplotype composed of two derived alleles under an infinite sites model, we selected core regions with at least three SNPs.²⁸ Second, we estimated the haplotypes and haplotype frequencies in these regions. Because of the longer extent of LD in cattle compared with that in humans, 250 kb extended regions adjacent to the core regions were set. Third, extended haplotype homozygosity (EHH) and REHH were calculated in the extended regions based on the estimated haplotypes and haplotype frequencies. Finally, P-values were obtained by log-transforming the EHH and REHH in the bin (20 bins to be all equally frequent haplotypes) to achieve normality. Calculation of LRH was performed using Sweep v.1.1 program. Only maternal haplotypes were loaded.

Results

The filtering resulted in 35,968 useful SNPs, which were used for further analysis. More precisely, 3,366 SNPs (6%) did not produce any genotypes, Of the initial SNPs, 7,814 (14.5%) were monomorphic, and 5,953 (11%) were filtered out because of low minor allele frequency (MAF) (<0.05). The fraction of SNPs excluded because of partially (>25%) missing genotypes and deviation of Hardy-Weinberg equilibrium (HWE) was negligible (185 SNPs, <0.1%). This subset of markers covers 2,543.6 Mb of the genome with 70.57 ± 69.0 kb average adjacent marker spacing. The largest gap between SNPs (2081.5 kb) was located on chromosome 10. For the SNPs analyzed in this study, the average observed heterozy-gosity was estimated at 0.37 ± 0.12. The average MAF of all SNPs before quality control (QC) was 0.20, and using the filtered SNPs, the averaged MAF increased to 0.27, which was higher than the average MAF of SNPs reported on the Illumina Bovine SNP50K BeadChip for Hanwoon (0.198).²⁹ The SNPs genotyped showed an almost uniform distribution across the common frequency classes. This is probably due to the design of the SNP array, which was optimized with respect to a uniform SNP spacing and MAF distribution (Fig. 1).¹⁸

Figure 1.

Minor allele frequency of SNPs.

In total, 1,760 core regions were identified, incorporating 5,915 SNPs which corresponded to 7.72% of the combined length of all the autosomes (Table 1). The mean core region length was estimated at 111.53 ± 71.89 kb, with a maximum of 2,081.46 kb. There were 141 core regions spanning 161.02 Mb on chromosome 1, and 20 core regions covering 51.90 Mb on chromosome 29. These were the largest and smallest haplotypic structures in the genome. An additional 1,318 core region consisting of three SNPs were identified (Fig. 2). The maximum number of SNPs in a core region was 14 from chromosome 14. There were 326 core regions with four SNPs, 82 core regions with five SNPs, 18 core regions with six SNPs and 8 core regions with seven SNPs.

Figure 2.

(A) Distribution of the length of core regions. (B) The number of SNPS forming the core regions in the Hanwoo genome.

Table 1.

Genome-wide summary of marker and core region(CR) in Hanwoo cattle.

CHR	NO. SNP	MEAN MAF	LENGTH (Mb)	LINKAGE MAP¹ (cM)	SIZE (kb)	MAX_SIZE (kb)	NO.CR (n)	CR MEAN SIZE ± SE(Kb)	% COVERAGE²	CR SNPS(n)³	MAX CR SNPS(N)
1	2376.00	0.27	161.02	142.10	67.75	1057.21	141	113.82 ± 66.66	9.97	490	10
2	1930.00	0.27	140.67	120.40	72.91	1143.31	113	116.27 ± 65.15	9.34	376	6
3	1832.00	0.27	127.85	125.20	69.40	807.55	119	107.99 ± 68.31	10.05	386	5
4	1736.00	0.28	124.13	101.50	71.37	853.22	92	106.23 ± 73.71	7.87	319	10
5	1472.00	0.27	125.80	122.10	85.52	1115.63	70	118.57 ± 74.37	6.60	245	13
6	1818.00	0.27	122.54	125.60	67.38	857.38	105	108.32 ± 67.59	9.28	357	7
7	1577.00	0.27	112.06	134.10	70.87	708.37	78	94.45 ± 60.97	6.57	255	5
8	1673.00	0.27	116.94	116.30	69.93	768.71	109	116.98 ± 212.30	11.05	382	9
9	1399.00	0.26	107.96	108.40	77.21	729.11	79	117.54 ± 64.4	8.60	268	7
10	1487.00	0.27	106.20	101.40	71.03	2081.46	72	92.83 ± 51.20	6.29	233	5
11	1567.00	0.28	110.17	123.50	70.27	890.68	75	119.02 ± 85.75	8.93	263	7
12	1145.00	0.27	85.28	105.80	74.47	948.71	55	110.82 ± 82.57	7.15	180	5
13	1228.00	0.27	84.34	87.10	68.52	574.52	56	104.32 ± 78.77	7.65	182	6
14	1219.00	0.27	81.30	85.70	66.71	608.29	44	116.07 ± 94.16	6.28	156	14
15	1160.00	0.27	84.60	93.40	72.96	761.86	49	121.21 ± 86.33	5.94	166	6
16	1101.00	0.27	77.74	96.50	70.62	1015.40	45	117.07 ± 63.90	6.78	155	7
17	1092.00	0.27	76.45	98.60	69.95	1388.99	43	110.10 ± 59.16	6.19	140	5
18	930.00	0.28	66.12	84.70	71.08	1557.49	37	116.09 ± 81.81	6.96	127	10
19	993.00	0.27	65.21	99.50	65.59	585.72	42	117.80 ± 68.26	7.59	142	6
20	1114.00	0.27	75.71	75.00	67.75	837.06	52	106.66 ± 53.92	7.33	173	5
21	997.00	0.28	69.17	87.60	69.42	849.43	45	116.32 ± 65.51	7.76	154	6
22	872.00	0.27	61.83	81.10	70.75	422.76	37	108.40 ± 63.43	6.49	120	5
23	792.00	0.28	53.29	67.10	67.37	562.47	27	127.91 ± 151.04	6.48	87	5
24	918.00	0.27	64.95	62.50	70.81	531.09	38	94.26 ± 63.65	5.52	120	4
25	699.00	0.29	43.86	64.91	61.98	351.37	28	101.73 ± 75.88	6.49	88	5
26	764.00	0.26	51.73	72.60	67.18	682.58	36	111.42 ± 57.57	7.75	117	5
27	694.00	0.28	48.73	64.10	70.28	1776.78	31	100.5 ± 56.62	6.39	100	5
28	652.00	0.27	46.02	52.40	70.62	419.44	22	103.60 ± 59.54	4.95	70	4
29	731.00	0.27	51.90	65.00	70.82	1004.44	20	112.95 ± 77.16	4.35	64	4
Total	35968.00	0.27	2543.57	2764.21	70.57	2081.46	1760	111.53 ± 71.89	7.72	5915	14

Notes:

Linkage map, NCBI Map Viewer (http://www.ncbi.nih.gov/mapview).

The proportion of total core region lengths on chromosome length.

Number of SNPs forming core regions.

Linkage disequilibrium

Decay of LD (r²) was estimated as a function of distance for each pairwise combination of SNPs on each chromosome, and a total of 24,985,377 SNP pairs were analyzed for all autosomes. To visualize the decay of LD and the proportion of pair markers, we stacked average values of r² and estimated them as a function of inter-marker distance categories (Table 2). A mean value of r² = 0.223 ± 0.274 was observed at distances shorter than 40 kb, which decreased to 0.111 ± 0.174 in the third smallest distance bin of ~60–100 kb. In the intervals of 0–40 and 40–60 kb, only 26% and 17% of markers had r² ≥ 0.3, respectively.

Table 2.

Pairwise linkage disequilibrium (r²) for syntetic SNPS at various distance pooled over all paternal autosomes on Hanwoo.

DISTANCE	N	MEDIAN	MEAN	SD	%(r² > 0.2)¹	%(r² > 0.3)²
0–40 kb	14,603	0.102	0.2226	0.2742	35	26
40–60 kb	11,148	0.064	0.1583	0.2206	25	17
60–100 kb	22,457	0.043	0.1119	0.1735	17	10
100–250 kb	81,413	0.024	0.0630	0.1120	7	4
250–500 kb	132,593	0.015	0.0355	0.0605	2	1
0.5–1 Mb	260,828	0.012	0.0269	0.0418	1	0
1–2 Mb	511,180	0.011	0.0241	0.0359	1	0
2–5 Mb	147,6343	0.01	0.0215	0.0320	0	0
5–10 Mb	2,309,702	0.009	0.0182	0.0270	0	0
10–20 Mb	4,143,933	0.007	0.0142	0.0212	0	0
20–50 Mb	9,006,713	0.004	0.0097	0.0143	0	0
50–100 Mb	6,052,736	0.003	0.0072	0.0103	0	0
>100 Mb	961,728	0.003	0.0068	0.0097	0	0

Notes:

Percentage of pairs of SNPs with r² > 0.2.

Percentage of pairs of SNPs with r² > 0.3.

Variation of LD in different distance bins of individual chromosomes combined over the genome was examined (Fig. 3). For marker pairs 0–40 kb apart, the average r² ranged from <0.18 (BTA19, BTA27, BTA28) to >0.24 (BTA6). Table 3 shows the comparison between the LD of intragenic regions and LD in intergenic regions. There was no difference in the extent of LD or the decline of LD with the distance between intragenic and intergenic regions.

Figure 3.

Chromosome-wise average r²-value and the decay of linkage disequilibrium with increasing physical distance for five smallest bins evaluated.

Table 3.

Linkage disequilibrium (r¹) of intrgenic and intragenic pairs of SNP.

DISTANCE (kb)	INTERGENIC			INTRAGENIC			P-VALUE¹
DISTANCE (kb)	N	MEAN	SD	N	MEAN	SD	P-VALUE¹
<50	11,670	0.05	0.16	4,892	0.05	0.16	>0.05
50–100	15,395	0.03	0.11	5,668	0.03	0.12	>0.05
100–150	14,420	0.02	0.08	4,887	0.02	0.09	>0.05
150–200	13,986	0.02	0.06	4,407	0.02	0.08	>0.05
200–250	13,579	0.01	0.05	3,966	0.02	0.07	>0.05

Note:

Test of equal variance.

Effective population size

Past effective population size was estimated chromosome-wise using pairwise SNP r² with inter-marker distance categories (Table 4). The ratio of genetic map distance to physical map distance was between 0.65 and 1.22 across chromosomes. Within a chromosome, the linkage map distance was assumed to be proportional to the physical genomic distance. The coefficient of variation (CV) increased with the length of the SNP interval, indicating that recent population size would be estimated less accurately than population size of many generations ago. Figure 4 shows a decreasing trend over the last 3000 generations, with an increasingly steeper slope since approximately 100 generations ago but a period of increase from 550 to 100 generations ago.

Figure 4.

Estimated effective population size over the past generations from linkage disequilibrium data. The variability at each point time reflects the variation of estimates between the 29 autosomes.

Table 4.

Effective population size(Ne) in the past Hanwoon population.

FRAGMENT SIZE (kb)	15	25	50	100	200	500	1,000	2,000	5,000	10,000	15,000
Effective population size¹
Mean	3,802	3,210	2,886	2,771	3,008	3,130	2,206	1,284	630	402	327
Median	700	435	476	363	452	451	391	196	116	59	46
SD	3,913	3,125	2,827	2,659	3,017	3,038	2,087	1,229	604	382	322
Min	2,442	2,351	2,168	2,152	2,239	2,139	1,580	936	432	287	250
Max	4,939	4,051	3,914	3636	3,972	3,949	3,165	1,745	889	524	442
CV(%)	5	3	4	2	3	8	11	13	16	22	27
Linkage distance (cM)²	0.016	0.024	0.047	0.094	0.188	0.470	0.940	1.882	4.706	9.456	14.209
Generation³	3,179	2,115	1,061	531	266	106	53	27	11	4	4

Notes:

Mean, median, standard deviation and 95% CI of past effective population size were calculated from Ne in 29 bovine autosomal chromosomes.

Linkage map distance (c) was inferred approximately by comparison between linkage map and physical genomic information.

The generation in the past population was calculated as 1/2c.

Whole-genome screening for selection signatures

For all 1,760 core regions, a total of 12,186 LRH tests with an average of 6.92 tests per core region were made. Taking this into consideration, we skipped core haplotypes with frequency <25% and plotted the -log₁₀ of the P-values associated with REHH against the chromosomal position to visualize the chromosomal distribution of outlying core haplotypes (Fig. 5). Table 5 presents the genome-wide statistics of the selection signature test, including the number of tests and outlying core haplotypes for each chromosome. Of the 9,028 tests on core haplotypes with frequency >0.1, 95 tests displayed outlying peaks on a threshold level of 0.01.

Figure 5.

Genome-wide map of –log₁₀(REHH P-values) for core haplotypes with frequency >0.1. Dashed lines display the threshold level of 0.01.

Table 5.

Summary statistics of whole genome extent haplotype homozygosity test.

Chr.	TEST ON CH (n)¹	P-VALUE <0.05	P-VALUE <0.01
1	704	36	8
2	549	27	5
3	616	32	9
4	479	22	5
5	370	19	4
6	509	28	4
7	381	20	3
8	585	33	10
9	347	20	5
10	403	16	6
11	389	17	4
12	279	12	1
13	299	14	7
14	246	9	2
15	244	12	2
16	250	11	2
17	219	6	1
18	199	10	1
19	224	14	1
20	263	10	3
21	201	8	1
22	201	8	2
23	156	7	2
24	215	10	3
25	166	8	3
26	176	5	1
27	143	6	0
28	104	5	0
29	111	5	0
Total	9028	430	95

Note:

The number of tests on core haplotypes (both sides) with frequency ≥0.1.

We also explored a quantitative trait loci (QTL) database available online (http://www.animalgenome.org/QTLdb/cattle.html)³⁰ to identify any overlapping of the outlying core regions with published QTL in dairy and beef cattle. Additional File 1 lists the traits, approximate position, and reported population of the overlapping QTL for each core region.

Discussion

The pattern of LD in domestic animals means that a marker may be in LD with a QTL some distance away and show an association with the trait affected by the QTL; it also tells us how natural selection, genetic drift, recombination, and mutation all affect the levels of LD.^2,31 In this study, we present an analysis of LD of 35,968 SNP genotypes in Hanwoo, in which only maternally inherited haplotypes were used. We elected to use maternal haplotypes in consideration of the complexity of the pedigrees, with dams contributing information to multiple families. Bohmanova et al reported that the use of maternal haplotypes is recommended for analyses of LD in populations consisting of large paternal half-sib families.³²

Comparable estimates of extensive LD have been reported in cattle.^18,32,33 Consistent with previous analysis in cattle, the decline of LD as a function of distance was rapid, with average r² declining from 0.1119 to 0.0355 when moving from 60 to 500 kb (Table 2). Recent studies that used SNPs for evaluating the extent of LD revealed that strong LD extends for shorter distances than previously reported. Marques et al used 505 SNPs on BTA14 in Holsteins and found moderate levels of LD (r² ≥ 0.2) extending up to 100 kb.³⁴ Khatkar et al demonstrated that r² ≥ 0.2 in Australian Holsteins between SNPs less than 60 kb apart.³⁵ Sargolzaei et al reported useful LD (r² ≥ 0.2) between markers with intermarker distance ≤ 100 kb in North American Holsteins.³⁶ In other cattle breeds (Japanese Black, Angus, Brahman, and Holstein), the average LD declined to 0.2 within 100 kb genomic regions.³⁷ Our study shows that moderate levels of r² (between 0.2 and 0.24) were observed at distances shorter than 40 kb. It is important to note that the level of LD between adjacent markers in Hanwoo was weaker than in other cattle breeds. This indicates that Hanwoo may have a higher actual Ne, probably because of a relatively low selection intensity compared to other breeds.

There were no differences in the extent of LD and the decline of LD with the distance between intragenic and intergenic regions (Table 3). Indeed, knowledge of LD with genes is important. However, noncoding elements such as miRNAs might also play a role in many inherited traits. Therefore, these results suggest that noncoding regions have been an important substrate for adaptive evolution in Hanwoo.

The number of SNPs needed depends on the distance over which LD operates. If SNPs are too far apart, a QTL may not be in sufficient LD with any of the markers, and so will be undetected.² Meuwissen et al reported the required level of LD (r²) for genomic selection to achieve an accuracy of 0.85 for genomic breeding values to be 0.2 using simulation.³⁸ To achieve this level, our results indicate that the SNP spacing should be ~40 kb in the future population, which corresponds to more than 75,000 evenly distributed SNPs across the genome. LD showed extensive variability between genomic regions and chromosomes. This variation was probably attributed to recombination rates varying between and within chromosomes, heterozygosity, genetic drift, and effects of selection. When the decay of r² with a distance was plotted separately for each chromosome, the average r² values are lower than 0.2 on BTA10, 15, 17, 19, 23, 25, 27, 28, and 29 in the 0–40 kb interval when compared to all other autosomes (Fig. 3). This difference could be due to a selection process acting on these chromosomal regions. This suggests that the loci on BTA 10, 15, 17, 19, 23, 25, 27, 28, and 29 need more SNPs to achieve sufficient power.

The pattern of LD observed in a population depends on the history of the population, especially the history of its effective population size.² This study shows a persistent decline in the effective population size. In particular, there are sharp declines at two time intervals and one distinctive time point (Fig. 4; Table 4). The distinctive point at 100 generations ago shows a sharp decline in population size, which is consistent with previous studies.^39,40 This pattern can be explained by breed formation and modern breeding programs.¹⁵ The first sharp decline of the effective population size was observed in our study ~25–50 generations ago, and is due to enhancement of selection. A second and more recent sharp decline seems to have occurred ~10–25 generations ago and might thus correspond to several events related to the intensive use of artificial insemination (AI). Overall, the Korean national economy has grown very rapidly since 1970, which has brought about a change in the Korean lifestyle, including significant consumption of meat from cattle. In this regard, improvement of selection methods, together with the adaptation to different agro-ecological constraints, has been necessary and might have had a direct effect on the population structure of cattle.

The estimated effective population size for the most recent time is around 300 individuals. Compared to other reports,^{18,33,39–41} recent Ne for Hanwoo were much higher than in other livestock populations. This suggests a small genetic drift associated with a small decrease in the population size over generations. Hanwoo values are greater than those recommended by Food and Agriculture Organization (FAO).⁴² Frankham et al suggest a minimum Ne of 50–100 for sustaining reproductive fitness in the short term (~100 years). However, this is below the value recommended to maintain evolutionary potential for long-term species management (Ne > 500–5000),⁴³ as additive genetic variation may eventually be lost by drift. In addition, a very large Ne indicated genomic selection, which would be difficult to apply to Hanwoo. To overcome these problems, it may be necessary to reduce Ne in a breeding program, for instance, by using only the best families or existing varieties to breed the new strain, where estimation of SNP effects is based on the genotyping of DNA pools and SNP density is reduced by estimating SNP effects within one or a few families.^44,45

Positive selection is expected to accelerate the frequency of an advantageous allele faster than recombination can break down LD at the selected haplotype.²⁷ In this report, we employed the LRH test by selecting a “core” haplotype with elevated EHH relative to other core haplotypes, at the locus conditional on haplotype frequency. This was used to identify selection signatures within a single population. Our result revealed 95 regions exhibiting the footprint of recent positive selection at a threshold level of 0.01.

We found an overlap of the 11 core regions presenting top P-values and those that had previously been identified as harboring other breeds’ QTL (Supplementary Table 1). In such a case, a possible higher initial frequency of beneficial alleles might be imported in a breed through crosses with other breeds. Selection may have started from a moderate initial frequency, and beneficial alleles may be included in diverse haplotypes. It is important to note the distinct demographic history of Hanwoo: the Ministry of Agriculture, Forestry and Fishery (MAFF) controls the breeding system for improving the productivity of Korean native cattle. From 1955 to 1965, Korean cattle were crossed with beef breeds such as Angus, Hereford, and Brown Swiss to improve the body weight. Since the early 1960s, there has been a program for improvement through pure breeding and crossbreeding. From 1971 to 1975, crosses of Korean cattle and Angus or Charolais were crossed with Holstein, which resulted in an improvement of both body weight and dressing percentage. Since 1978, Korean cattle have been crossed with Charolais bulls to make a new composite breed that is five-eighth Charolais and three-eighth Korean cattle.

Eleven QTL regions, associated not only with production traits but also with meat and carcass traits, have been identified. Within the last century, Hanwoo have been intensively selected for both the quality (marbling, tenderness, and flavor) and the quantity (carcass weight) of meat to meet the growing demand for beef in Korea. Cattle farming methods have changed dramatically, so it is not surprising that QTLs identified are related to meat traits, showing the signatures of recent selection. Especially for BTA14, which harbors known genes and QTL for several economically important traits, we found agreement between the regions (24.3–25.4 Mb) and those that had previously been identified to harbor carcass weight QTL peak on Hanwoo.⁴⁶ We extended core regions in both directions up to 1 Mb as the length of the core domains. We identified PLAG1, CHCHD7, SDR16C5, SDR16C6, PENK, FAM110B, CYP7A1, SDCBP, and TOX as the positional and functional candidate genes for the carcass weight QTL in cattle. The associated region on bovine chromosome 14 is conserved in human chromosome 8q21, which has been reported to be associated with adult height.⁴⁷ The positive correlations between REHH values and effects of the SNPs on meat traits suggest co-location of some selected regions of the chromosome and QTL affecting production traits. It is also important to note that the distinct demographic history of Hanwoo, including severe bottlenecks, inbreeding, and nonrandom mating, can produce considerable heterogeneity in genome-wide patterns, confounding inferences of selection. However, the message conveyed by this study has important implications for the design and application of association studies in Hanwoo populations. These results give us the confidence to conduct further in-depth studies.

Conclusion

In this work, the Illumina Bovine SNP50K chips were used for SNP genotyping in an elite Hanwoo population comprising 61 sires and their 486 steers. Haplotype phases were inferred by an LHCM method, and maternal haplotypes were chosen across all autosomal chromosomes. Ne was estimated from the LDs between syntenic SNPs, and the results showed that Ne values started from 4,000 to ~3,200 generations ago, decreasing gradually. However, there were two sharp Ne decline periods, between 25–50 and 10–25 generations ago. In the latter, the Hanwoo breeding program was implemented with the technique of artificial insemination. The Ne of the current Hanwoo population was estimated to be around 300.

To identify selection signatures on any genomic position, an LRH test was applied. Our results revealed 95 regions exhibiting footprints of recent positive selection (P < 0.01). We found an overlap of the 11 core regions presenting top P-values and those that had previously been identified as harboring other breeds’ QTL. Additional studies, especially comparisons of different populations, are needed to confirm and refine our results.

Author Contributions

Conceived and designed the experiments: YL, JJK. Analyzed the data: YL, JJK. Wrote the first draft of the manuscript: YL. Contributed to the writing of the manuscript: YL, JJK. Agree with manuscript results and conclusions: YL, JJK. Jointly developed the structure and arguments for the paper: YL, JJK. Made critical revisions and approved final version: YL, JJK. Both authors reviewed and approved of the final manuscript

Supplementary Material

Supplementary Table 1.

Summary statistics for 11 core haplotypes showing the lowest P-values after the relative extended haplotype homozygosity (REHH) test.

Footnotes

Acknowledgments

Thanks for the academic editor and reviewers for their comments, which were very helpful in improving the manuscript.

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