Title: Korean Reference Genome construction by next generation sequencer: full genome sequencing for ethnic groups is necessary.   

1. Abstract

The first full Korean individual genome sequence is made publically available. This genome will be used as the basis for building Korean Reference Genome for ethnic Korean population which represent over 100 million individuals posessing a distinct language and culture. By January 2009, 300 giga bases of a male Korean genome was sequenced by Solexa genome analyzer. It resulted in 20 fold redundancy and covers 99.9% of the NCBI reference genome. The overall accuracies in terms of XXX and YY are ZZZ and KKK. Three million new SNP candidates that were specific to Koreans were found. This high number of new SNPs justifies that each ethnic group requires its own genome sequencing project to generate its own reference genome. Although the previously reported Chinese genome is very similar to the Korean one, but there were significant differences between the two very close ethnic groups if these  two individuals are supposed to represent the population. They were different in SNP and copy number variations (CNV). The SNP chip analyses carried out alongside the genome sequencing showed that Koreans lay in between Chinese and Japanese populations. Mitochondrial sequence analyses from the Korean genome revealed that there was a significant variations showing the heterogeneous mitochodrial genome composition.The massive  data generated by next generation sequencing technology takes us one step closer to personalized genomic medicines.

 

2. Introduction

In 1977, the first full viral genome sequence  was published by Sanger et al., in MRC Centre Cambridge, UK and three years later Anderson et al., sequenced the complete human mitochondrial genome. Both the  genomes had exact number of bases, 5,386 and 16,569 respectively, and exact number of coding genes have been verified. Now, researchers know the exact functions of DNA fragments in these genomes. These key projects laid the foundation for  sequencing the full human genome which was completed in 2003.At present , there are at least four completely sequenced individual human genomes published. Among them, Craig Venter's genome has the highest quality and can be used as a reference genome for future individual genome projects. However, producing such a high quality genome is still extremely expensive.

Apart from the NCBI's reference genome that was sequenced over the last decade, it is still not clear how useful the analyses are by sequencing individual human genomes as national and global projects. NCBI's reference genome has an importance as the candidate human reference standards that can be compared to all the human genomes that will be sequenced in the future. It is an important question for government to understand how extensively the personal genome sequencing should be done. Relatively cheap SNP genotyping is already well introduced to the public.  Compared to such cheaper and simpler chip based genotyping, what merits can full genome sequencing can bring to the society? Another point to consider is that, unlike the first viral and mitochondrial genomes , understanding the exact functions of all the human genetic elements is still a difficult task even if we acquire the full human sequences. Surprisingly, researchers still cannot count the exact number of human genes after decades of sequencing let alone the exact number of proteins expressed in a cell. Yet, due to the advancement of fast next generation sequencing technologies,  more number of  full genome sequencing projects are arriving; 1000 genome project, xxxxxxx, xxxx, xxxx, and xxxx. What is the practical benefit for a society to perform such a national scale full human genome sequencing? Here, we present some justification on why distinct ethnic groups need to perform their own human genome sequencing. We found that a Korean genome has significantly different number of SNPs to the already published Chinese genome while these two ethnic groups are known to be very close. We think that the difference is large enough to produce independent Korean reference genome that can be utilized for future personal genome projects in Korea.

 

3. Results

 

 3.1 Data Summary

 

 

	GAP	Reads	Nucleotides	Genome Coverage	Depth	Genome Coverage / Total NTs
36b-PE	80 base	xxx	xxx
36b-PE	300 base	xxx	xxx
75b-PE	180 base	xxx	xxx
Total	xxx	xxx

 

 

  

3.     Nucleotide composition – GC contents (consensus)

	A	C	G	T
Reference	29.53%	20.44%	20.46%	29.57%
KSJ	28.13%	21.82%	21.79%	28.26%

-        

4.     Sequencing quality

-       High quality: 80% - African (~60%) 보다 높고, YH (81%)와 비슷한 수준

*각 nucleotide의 Q threshold를 20으로 했을때, (#NT above 20)/(#total NT) * 100 (%)

 

5.     Mapping status of each chromosome : 데이터가 더 나오면 depth가 더 깊어질 것임

         

    6. Sequencing quality distribution : high quality

         

    * G-banded Karyotype

    * Phylogeny Analysis

Linage of Y, mtDNA, and autosomes

To identify the paternal and maternal linage of a Korean genome, Y, mtDNA, and autosomes were traced separately. When compared with Y Chromosome Consortium haplogroup markers (Karafet et al, 2008), the KoRef donor Y chromosome had variations which are known as the markers of a sub-haplogroup "O2b"(Fig X). The O2b sub-haplogroup is of high frequency in the Korean and Japanese populations, and is a sub group of O haplogroup who probably originated in East Asia and later migrated into the South Pacific. The mtDNA haplotype suggested XXXXX (Fig. X). The autosomal linage was similar(or different, or similar to Y chromosome, or only similar to mtDNA) to XXX and a Korean donor was located between CHB and JPT (Fig. X).

Fig. X. Y chromosomal linage of the Korean genome

Fig. X. Autosomal linage of the Korean genome

    * Haplotype Analysis

Inferred Korean donor’s haplotypes showed more inconsistency to HapMap haplotypes than Chinese’ ones as much as XX%. Korean donor’s genome has possible haplotype breakpoints against to the haplotypes of HapMap CHB/JPT samples as much as xxx number when it was phased with mother’s genotype.

  * Donor pedigree

3.3 Alignment Results

3.4 Genetic Variations

3.4.1 SNPs

 Table X. Characterization of SNPs detected in the KSJ genome compared with dbSNP database (UCSC build 129).  The dbSNP alleles presented as validated and non-validated SNPs, and the novel SNPs meant non-present SNPs in dbSNP database

	N. of SNPs	Homo	Hetero
Total	2,474,648	700,969	1,773,679
dbSNP	1,574,921
validated	1,364,868	635,843	729,025
non-validated	210,053	48,659	161,394
5'UTR	3,513
3'UTR	15,771
CDS-Syn	10,414
CDS-Nonsyn	5,357
Novel	899,727	16,467	883,260
5'UTR	2,381
3'UTR	10,920
CDS-Syn	1,448
CDS-Nonsyn	9,472

Figure X. Characterization of SNPs detected in the KSJ genome compared with dbSNP database (UCSC build 129). 

In individual genome comparision, we compared with three different individual genome sequences such as venter, watson, and YH chaina genome In looking at the SNPs of the four individual genomes, XXX SNPs are all shared, and each also has a set of SNPs unique to their own genome: for KSJ, xxx SNPs, for YH xxx SNPs; for Venter, XXX ; and for Watson, XXX

Figure. Comparision and overlap of SNPs of KSJ genome and other genomes. Watson, Venter, and BGI YH.

Figure. Comparision and overlap of nonsyn-SNPs of KSJ genome and other genomes. Watson, Venter, and BGI YH.

We compared the non-synonymous SNPs of four individual genomes. As a result, XXX non-synonymous SNPs are all shared among the four individuals, xxx

Fig X. Comparison of Genome sequencing and Affy 6.0 genotyping alleles

3.4.2 Indels

  

 

Fig x. Distribution of indel size

 Table X. Total number of calls and fraction that match previous small Indels in dbSNP

	Total	dbSNP			dbSNP (%)
		Validated	Un- validated	Novel	Validated	Un-validated	Novel
All Indel	143235	167	54864	88204	0.12	38.30	61.58
Hetero Indel	61019	74	18977	41968	0.12	31.10	68.78
Homo Indel	82216	93	35887	46236	0.11	43.65	56.24

 

 

Fig x. Rate of indel calls mathced by dbSNP

  Figure xxx. Common InDels in GA,HY,CV Genome

Figure xxx. Occurrence of InDels in the genes and repeat elements

Figure xxx. InDel HotSpot

Talbe xxx. Mapping InDels to the Chimpanzee Genome and Identification of Specific InDel and Common InDels   

Genome	Total InDel	InDel Human Specific Fragments Mapping	GA Vs YH , CV Common InDel	GA Specific InDel	YH Specific InDel	CV Specific InDel
YH Genome	135,199	1,488	102	260	1386
Venter Genome	559,473	25,407	76	286		25331
GaChon Genome	53868	362

 

3.4.3 Structural variations

Fig X. Insert size distribution for pair-end  libraries of KSJ.

Figx. Homozygous genomic region deletion in KSJ to the reference, detected by abnormal long insert sized read pairs compared to regularly insert size read pairs.

 

3.4.4 PCR-amplified resutls for validated SNPs, Indel, and CNVs

3.4.5 Gene related to non-synonmous SNP's

 Non-Synonymous SNP's occuring in coding region have the higest impact on phenotype.We tested the non-synonymous SNPs for their possible effect on function and structure of proteins using Polyphen.Among the 4436 SNP's , 3477 (78%) were predicted as benign(no effect) while 602 (14%) were predicted as possibly damaging and 357(7.3 %) as probably damaging.These predictions were similar to Watson non-synonymous SNP analysis.Predictions were also made  using SIFT  (Seperating Intolerant From Tolerant) .Among 2589 SNP's sampled ,2092 (80%) were predicted to be tolerated while 477 (18%) as deleterious.         

 

Comparison of Polyphen results of Watson genome project and Korean genome project.

Genome Project	Sampling	Probably damaging	Possibly damaging	Bening (No effect)
Korean Genome	4436	357 (8 %)	602 (14 %)	3477 (78 %)
Watson Genome	3898	7.3 %	13 %	74 %

 

 

3.5 Genotypes and Phenotypes

3.6 Mitochondria analysis

3.6.1 Sequencing quality comparison based on MtDNA

The rCRS genome has  accepted as a refercence genome for mitochondria.

We compaired the rCRS genome with KoRef mtDNA.

As the Table1, the two genomes has showed very similar in sequence composition.

Sequences of genes were aligned very clear (not appear stop codons). (figure 1)

 

 

미토콘드리아 DNA 데이터의 퀄리티를 확인하기 위해서 CRS 서열과 KoRef mtDNA 간의 서열 조성을 비교해보았다.(table 1)

 

Table 1. Comparison rCRS with KoRef mtDNA in sequence composition.

genome	All	A	T	G	C	AT%	ATskew	GCskew	ATratio	GCratio
CRS	16569	5124	4094	2169	5181	56	0.112	-0.41	1.252	0.419
Korean_MT	16571	5113	4086	2180	5192	55.6	0.112	-0.405	1.251	0.42

 

보이는 바와 같이 두 서열에서의 ATGC 조성은 매우 비슷하게 나타남을 알 수 있다. 특히 solexa 실험에서 GC %가 퀄리티를 결정하는 중요한 요소로 확인하는 것으로 볼 때, 우리의 결과는 매우 신뢰성이 높음을 알 수 있다.

또한 CRS 지놈에 read들을 맵핑 시킨 결과를 바탕으로 KoRef consensus mtDNA를 만들어, 이 서열상에서의 유전자들을 prediction 했다. 이 prediction은 CRS 유전자 annotation 정보를 기반으로 비교하여 얻었다. Figure1과 table2에서 보여지는 바와 같이 미토콘드리아에서 존재하는 37개 유전자 모두가 끊김없이 clear하게 존재하는 것을 확인할 수 있었다.

 

13 mitochondrial protein coding genes, 12 rRNA genes, and  22 tRNA genes were identified by aligning with the rCRS mitochondrial genome sequences using Clustal W.

Figure 1. Annotation and visualization of KoRef mitochondria genome. Variations and genes of the genome were identified through sequence alignment against rCRS.

 

Table 2.  Genes involved in KoRef mitochondria genome.

Gene Name	start	stop	strand
HVS2	1	57
tRNA-Phe	579	649	plus
12s_rRNA	650	1603	plus
tRNA-Val	1604	1672	plus
16s_rRNA	1673	3230	plus
tRNA-Leu	3231	3305	plus
ND1	3308	4264	plus
tRNA-Ile	4264	4332	plus
tRNA-Gln	4330	4401	minus
tRNA-Met	4403	4470	plus
ND2	4471	5512	plus
tRNA-Trp	5513	5580	plus
tRNA-Ala	5588	5656	minus
tRNA-Asn	5658	5730	minus
tRNA-Cys	5762	5827	minus
tRNA-Tyr	5827	5892	minus
COX1	5905	7446	plus
tRNA-Ser	7446	7517	minus
tRNA-Asp	7519	7586	plus
COX2	7587	8270	plus
tRNA-Lys	8296	8365	plus
ATP8	8367	8573	plus
ATP6	8528	9208	plus
COX3	9208	9991	plus
tRNA-Gly	9992	10059	plus
ND3	10060	10405	plus
tRNA-Arg	10406	10470	plus
ND4L	10471	10767	plus
ND4	10761	12138	plus
tRNA-His	12139	12207	plus
tRNA-Ser2	12208	12266	plus
tRNA-Leu2	12267	12337	plus
ND5	12338	14149	plus
ND6	14150	14674	minus
tRNA-Glu	14675	14743	minus
CYTB	14748	15888	plus
tRNA-Thr	15889	15954	plus
tRNA-Pro	15956	16024	minus
HVS1	16025	16571

 

3.6.2 Variation aspect of mitochondrial genome

 

read들을 CRS에 맵핑 시킨 결과를 바탕으로 mtDNA에서 나타내어지는 SNP를 동정하였다. 모두 44건의 SNP를 찾을 수 있었으며, 그 중 insertion 3건과 deletion 1건을 찾을 수 있었다. 찾아진 SNP들은 앞에서 밝힌 유전자 정보를 바탕으로 annotation 하였다. 또한 단백질 코딩 영역에서 발견되어진 SNP에 대해서는 아미노산 변화 여부를 확인하였다. 그 결과로 6개의 non-synonymous가 발견되었다. (table3)

 

Table 3. Nucleotide substitutions in KoRef mtDNA against rCRS genome. 

#	Position	rCRS	KSJ	Class	Gene	Type	rCRS_aa	KSJ_aa	rCRS_nt	KSJ_nt
1	73	A	G	single	-	control_region
2	150	C	T	single	-	control_region
3	195	T	C	single	-	control_region
4	263	A	G	single	-	control_region
5	310	-	T	insertion	-	control_region
6	310	T	C	single	-	control_region
7	311	-	C	insertion	-	control_region
8	408	T	A	single	-	control_region
9	750	A	G	single	12s_rRNA	rRNA
10	1438	A	G	single	12s_rRNA	rRNA
11	2352	T	C	single	16s_rRNA	rRNA
12	2483	T	C	single	16s_rRNA	rRNA
13	2706	A	G	single	16s_rRNA	rRNA
14	3107	X	T	single	16s_rRNA	rRNA
15	3109	T	-	deletion	16s_rRNA	rRNA
16	4769	A	G	single	ND2	synonymous	Met	Met	ATA	ATG
17	5580	T	C	single	tRNA-Trp	tRNA
18	7028	C	T	single	COX1	synonymous	Ala	Ala	GCC	GCT
19	8701	A	G	single	ATP6	non-synonymous	Thr	Ala	ACC	GCC
20	8860	A	G	single	ATP6	non-synonymous	Thr	Ala	ACA	GCA
21	9377	A	G	single	COX3	synonymous	Trp	Trp	TGA	TGG
22	9540	T	C	single	COX3	synonymous	Leu	Leu	TTA	CTA
23	10398	A	G	single	ND3	non-synonymous	Thr	Ala	ACC	GCC
24	10819	A	G	single	ND4	synonymous	Lys	Lys	AAA	AAG
25	10873	T	C	single	ND4	synonymous	Pro	Pro	CCT	CCC
26	11017	T	C	single	ND4	synonymous	Ser	Ser	AGT	AGC
27	11719	G	A	single	ND4	synonymous	Gly	Gly	GGG	GGA
28	11722	T	C	single	ND4	synonymous	Leu	Leu	CTT	CTC
29	12705	C	T	single	ND5	synonymous	Ile	Ile	ATC	ATT
30	12850	A	G	single	ND5	non-synonymous	Ile	Val	ATC	GTC
31	14212	T	C	single	ND6	synonymous	Val	Val	GTA	GTG
32	14580	A	G	single	ND6	synonymous	Leu	Leu	TTG	CTG
33	14766	C	T	single	CYTB	non-synonymous	Thr	Ile	ACT	ATT
34	14905	G	A	single	CYTB	synonymous	Met	Met	ATG	ATA
35	15301	G	A	single	CYTB	synonymous	Leu	Leu	TTG	TTA
36	15326	A	G	single	CYTB	non-synonymous	Thr	Ala	ACA	GCA
37	15932	T	C	single	tRNA-Thr	tRNA
38	16172	T	C	single	HVS1	control_region
39	16183	A	C	single	HVS1	control_region
40	16189	T	C	single	HVS1	control_region
41	16193	-	C	insertion	HVS1	control_region
42	16223	C	T	single	HVS1	control_region
43	16320	C	T	single	HVS1	control_region
44	16519	T	C	single	HVS1	control_region

 

3.6.3 MtDNA ethno-genographic lineage

 

Fig XX. mtDNA ethno-genogeographic lineage

Korean mitochondrial DNA had on XX SNPs. According to 22 haplogroups defined by sequence differences from the rCRS, haplogroup D4, prevalent in Asia, mapping into our mtDNA.

 

 

3.6.4 Mapping disease information

We have been able to detect the expected phenotype based on the literature given the according to published literature (ref 1)

 

44개의 SNP들을 이용하여 질병 정보를 annotation 하였다. 그 결과 mtDNA의 1438번째 위치에 존재하는 SNP (A/G) 가 12S rRNA 유전자에서 diabetes Mellitus 에 맵핑되는 것을 확인 하였다.

 

Position: A1438G

Disease:    Diabetes Mellitus

Gene:    12S rRNA

Reference: Tawata, M., Ohtaka, M., Iwase, E., Ikegishi, Y., Aida, K. and Onaya, T. (1998). "New mitochondrial DNA homoplasmic mutations associated with Japanese patients with type 2 diabetes." Diabetes 47(2):276-277.

Pubmed ID: 9519725

 

3.6.5 Heteroplasmy

possibility

• paternal inheritance (ref 2)
• by aging, MtDNA variation in one indivisual is increasing.

Observed heteroplasmy

 10 =< MHP < 50% : 1

5 <= MHP < 10 : 8

1 <= MHP < 5 : 567 

MHP: Minor-heteroplasmy

Table 4. Heteroplasmic mtDNA changes in KoRef mtDNA.

 

Position	Nucleotide substitutions	Gene location: amino acid change	%heteroplasmy

 

3.6.6 References

1. Tawata, M., Ohtaka, M., Iwase, E., Ikegishi, Y., Aida, K. and Onaya, T. (1998). "New mitochondrial DNA homoplasmic mutations associated with Japanese patients with type 2 diabetes." Diabetes 47(2):276-277.    9519725

2. Gustafson A. W., Heckerling P. S., Vissing J., Schwartz M. (2002). Paternal Inheritance of Mitochondrial DNA. N Engl J Med 347: 2081-2082

 

 

 

4. Material & Methods

4.1 Short read alignment

 We aligned a fast short-read alginment program called MAQ. To sped up the alignment, MAQ only considers positions that have 2 or fewer mismatches in the first 28bp.

Sequences that fail to reach a mismatch score threshold but whose read pair in mapped are searched with a gapped alignment algorithm in the regions defined by the read pair.

MAQ always reports a single alignment, and if a read can be alinged equally well to multiple positions, MAQ will randmly pick one positions and give it a mapping quality zero.

MAQ usefully utilzes the read-pair information of paired reads.It is able to use this information to correct wrong alignments, to add confidence to correct alignments, and to accurately map a read to repetitive sequences if its mate is confidently aligned. With paired-end reads, MAQ also finds small indels from the gapped alignment described above. And, read pairs were aligned as abnormal long-inserted size if they had high-confidence alignments of each individual read.

 

4.2 Calling SNPs

MAQ produces a consensus genotype sequence from the alignment. The consensus sequence is inferred from a Bayesian statical model and each consensus genotype is associated with a Phred quality which measures the probability that the consensus genotype is incorrect. Potential SNPs are detected by comparing the consensus sequence to the NCBI reference and are further filtered by a set of predefined rules. These rules are:

1. Minimum 3 read depth (-d 3)

2. Filtering overestimate depth, randomly placed repetitive hits, had to be more than 100 (-D 100)

3. Consensus including SNP lower than Q20

4. Adjacent sequence lower than Q20

5.  More 2 reads for supporting an allele of heterozygous SNPs

6. Remove SNPs within the 3bp flanking region around a potential indel

4.3 Detection of small Indels

We called small indels (from 16 bp deletion to 7 bp insertion) by using paired-end indel detection mehtods of MAQ, requied at least three reads to support the indel, and add information confirmed by reads from both strands.

In any 20 bp window, if there are 2 or more Indels, discard them all. Finally, we classified homozygous indels by supporting less 10% about other allele.

5. Discussion

 

 

 

6. Supplementary information  

 

7. References

 

Other information