Steven Gazal et al.
nature 02 December 2015
主として朝鮮半島に生息する連中は、本当に幸運なことに、Hapmap及び1000ゲノムプロジェクトの両者で分析対象ではなかった、このことは彼ら・彼女らに巨大な幸運をもたらした。何故なら、万一、1000ゲノムプロジェクトで彼ら・彼女らが分析対象に含まれていれば、[I am Koreans]と絶対に言えなくなることは確実であるからだ。
彼ら・彼女らの異様なレベルの遺伝的特異性は、民族差別という重大な問題の故に、今後も日本及び世界の人々が知るところとはならないであろう。
しかし、2014年に事実上はアメリ食品医薬品局毒性研究センターが彼ら・彼女らの35名のDNAを分析し、異様なレベルの遺伝的特異性を完膚なきまでに明らかにしている。
①何故、このような人々が今日に至るまで、遺伝的均質性の高い民族集団として生息し存続しえたのか?
②何故、このような異様なレベルの遺伝的特異性を有する集団が、いつの時点でどのような理由で生じてしまったのか?
③何故、アメリ食品医薬品局毒性研究センターは、完全に単独で、分析したのか?
これらの疑問のうち②については、李氏朝鮮時代の奴隷制=奴婢制による意図せざる近親曽=同父異母間の兄弟姉妹間で生まれた子が他の民族集団比べて比較不能なレベルで多いが一つの答えとなるものと思われる

Abstract
The 1000 Genomes Project
2,500 sequenced individuals from 26 populations
found an unexpected high level of inbreeding in 1000 Genomes data:
nearly a quarter of the individuals were inbred and around 4% of them had inbreeding coefficients similar or greater than the ones expected for first-cousin offspring.
Inbred individuals were found in each of the 26 populations, with some populations showing proportions of inbred individuals above 50%.
Introduction
下記3つの論文が自然選択関連先行研究として挙げられている
A composite of multiple signals distinguishes causal variants in regions of positive selection.
Identifying recent adaptations in large-scale genomic data
a genome browser dedicated to signatures of natural selection in modern humans
while TGP individuals are described as unrelated and that relationships of the previous phases have been investigated by TGP consortium and others2,9, their inbreeding level is undocumented and could bias genotype and haplotype frequencies estimated on this panel.
Table 1 Inbreeding detection in TGP populations.
|
Total |
Total inbred |
|
|---|---|---|
|
African (AFR) |
660 |
90 (14%) |
|
African Caribbean in Barbados (ACB)* |
96 |
4 (4%) |
|
African Ancestry in Southwest United States (ASW)* |
60 |
1 (2%) |
|
Esan in Nigeria (ESN) |
99 |
27 (27%) |
|
Gambian in Western Division, The Gambia (GWD) |
113 |
28 (25%) |
|
Luhya in Webuye, Kenya (LWK) |
99 |
9 (9%) |
|
Mende in Sierra Leone (MSL) |
85 |
10 (12%) |
|
Yoruba in Ibadan, Nigeria (YRI) |
108 |
11 (10%) |
|
European (EUR) |
503 |
88 (17%) |
|
Utah residents with European ancestry (CEU) |
99 |
1 (1%) |
|
Finnish in Finland (FIN) |
99 |
34 (34%) |
|
British in England and Scotland (GBR) |
91 |
16 (18%) |
|
Iberian populations in Spain (IBS) |
107 |
27 (25%) |
|
Toscani in Italy (TSI) |
107 |
10 (9%) |
|
East Asian (EAS) |
504 |
54 (11%) |
|
Chinese Dai in Xishuangbanna, China (CDX) |
93 |
36 (39%) |
|
Han Chinese in Bejing, China (CHB) |
103 |
4 (4%) |
|
Southern Han Chinese, China (CHS) |
105 |
2 (2%) |
|
Japanese in Tokyo, Japan (JPT) |
104 |
4 (4%) |
|
Kinh in Ho Chi Minh City, Vietnam (KHV) |
99 |
8 (8%) |
|
South Asian (SAS) |
487 |
221 (45%) |
|
Bengali in Bangladesh (BEB) |
86 |
19 (22%) |
|
Gujarati Indian in Houston, Texas (GIH) |
103 |
41 (40%) |
|
Indian Telugu in the United Kingdom (ITU) |
100 |
44 (44%) |
|
Punjabi in Lahore, Pakistan (PJL) |
96 |
55 (57%) |
|
Sri Lankan Tamil in the United Kingdom (STU) |
102 |
62 (61%) |
|
Admixed American (AMR) |
343 |
142 (41%) |
|
Colombian in Medellin, Colombia (CLM) |
94 |
50 (53%) |
|
Mexican Ancestry in Los Angeles, California (MXL) |
64 |
11 (17%) |
|
Peruvian in Lima, Peru (PEL) |
81 |
16 (20%) |
|
Puerto Rican in Puerto Rico (PUR) |
104 |
65 (63%) |
|
TOTAL |
2497 |
595 (24%) |
We applied our FSuite pipeline11,
11は下記
FSuite: exploiting inbreeding in dense SNP chip and exome data.
Results
Overview of methods
FSuite performance in admixed samples
We investigated the accuracy of FSuite f estimates by simulating 100 replicates of a sample of 300 admixed individuals with different levels of inbreeding and different levels of European and African ancestry.
Figure 1 shows the difference between FSuite estimates and true f value (Δf) against the true genomic proportion of European ancestry (ADMCEU) of the individual,
Inbreeding estimation and detection on the last phase of 1000 Genomes project
Before applying FSuite on the TGP data, we ran the multi-point method RELPAIR17,18 on individual pairs from each population in order to detect unknown first or second degree relationships.
We detected 15 unreported relationships closer than first-cousins: 8 parent/offspring relationships (including one trio), 3 full-sibs, 1 half-sib, 3 avuncular relationships (Table S3). We thus excluded 14 individuals to estimate population allele frequencies.
94 individuals can be considered as descending from recent inbreeding, i.e. being offspring of first-cousin or closest relationships.
2497がこの分析で対象とした数である。いとこ婚よりも近い関係にある者の比率は、94/2500=3.76%である
Finally, note that GIH and PUR populations, that had a high proportion of inbred individuals (40% and 64%, respectively), had only one individual who exhibited recent inbreeding.
Discussion
In conclusion, we have shown that multi-point approaches provide reliable estimates of the genomic inbreeding coefficient f even when there are some admixed individuals in the studied population.
On the final phase (Phase III) of the 1000 Genome Project, we found that nearly a quarter of the individuals in this panel were inbred and that around 4% of them had inbreeding coefficients similar or greater than the ones expected for first-cousin offspring.
This level of inbreeding was unexpectedly high and is much higher than the 4% of inbred individuals that we detected on HapMap III10.
下がTableS3の集団名と関係だけを取り出したもの。略語は下記の通りで間違いないと思われる
CO=cousinいとこ婚、FS=full siblings兄弟姉妹間、PO=parent offspring親子間、祖父孫間
AV=伯父姪婚、HS=half siblings 異父又は異母兄弟姉妹間
目立つつのは
①CDX=中国の少数民族であるタイ族(=南方系アジア人の源流とされている)のいとこ婚の多さである。しかし、少数民族では文化人類学者の分析では、交叉いとこ婚が非常に多いことが知られており、人口の少なさからタイ族では交叉いとこ婚が多いと推定される
②日本人=JPTはこの表ではゼロであるが、しかし、数値的にはtable1では4%となっており、日本人の遺伝的な均質性=近縁性を反映していると考えられる。言い換えれば、江戸時代の田舎における近親婚の蔓延を反映していると推測される
計228事例
*同父同母兄弟間→3例
*異母同父又は同母異父兄弟間→1例
*伯父姪間→3例
*親子間(全てアフリカ又は南アジア)→8例
| SUPER POP | POP | INFERED RELATIONSHIP |
| AFR | ACB | CO |
| AFR | ACB | CO |
| AFR | ACB | FS |
| AFR | ACB | CO |
| AFR | ASW | AV |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | PO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | CO |
| AFR | ASW | PO |
| AFR | ASW | PO |
| AFR | ASW | PO |
| AFR | ASW | CO |
| AFR | ASW | PO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | ESN | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | GWD | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | AV |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | FS |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | LWK | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | MSL | CO |
| AFR | YRI | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | CLM | CO |
| AMR | MXL | CO |
| AMR | MXL | CO |
| AMR | MXL | CO |
| AMR | MXL | CO |
| AMR | MXL | CO |
| AMR | PEL | CO |
| AMR | PUR | CO |
| AMR | PUR | CO |
| AMR | PUR | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CDX | CO |
| EAS | CHS | CO |
| EAS | CHS | AV |
| EAS | CHS | CO |
| EAS | CHS | CO |
| EAS | CHS | CO |
| EAS | CHS | CO |
| EAS | CHS | CO |
| EAS | KHV | CO |
| EUR | CEU | CO |
| EUR | CEU | CO |
| EUR | CEU | CO |
| EUR | CEU | CO |
| EUR | CEU | CO |
| EUR | CEU | CO |
| EUR | GBR | CO |
| EUR | GBR | CO |
| EUR | GBR | CO |
| EUR | GBR | CO |
| EUR | GBR | CO |
| EUR | GBR | CO |
| EUR | TSI | CO |
| EUR | TSI | CO |
| EUR | TSI | CO |
| SAS | BEB | CO |
| SAS | GIH | CO |
| SAS | GIH | CO |
| SAS | GIH | CO |
| SAS | GIH | PO |
| SAS | GIH | PO |
| SAS | GIH | CO |
| SAS | GIH | CO |
| SAS | GIH | HS |
| SAS | ITU | CO |
| SAS | ITU | CO |
| SAS | ITU | CO |
| SAS | ITU | CO |
| SAS | ITU | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | PJL | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | FS |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | PO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
| SAS | STU | CO |
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