Help Me Find My Relatives!

I would like to meet my biological family. Thank you for any help. ❤︎ If you have any information or expertise that can help me to find my biological family, please do not hesitate to contact me.

Below you can find some information about me. Please note that some of this information given to me might be inaccurate or wrong. So please do not focus on the details, rather: do not hesitate to contact me.

Pictures of me

Aged 0—1

Aged 17—18

  • Born in the first half of 2002

    Given the name 常含晨 (Chang Han Chen) by a Social Welfare Institute in China

A Social Welfare Institute in China estimated that I was born around May 2002 (this is a guess only, this date could be very inaccurate).

It was claimed by the Social Welfare Institute that I was found by a finder at the riverbank approaching the Social Welfare Institute on June 1, 2002.

The Social Welfare Institute gave me the name 常含晨 (Chang Han Chen).

  • Adopted from China in March 2003

    Given the name Elise Hoste by my adoptive parents

I was adopted from a Social Welfare Institute in March 2003 into a Belgian family. My adoptive parents gave me the name Elise Hoste.

I am now 165 centimeters tall.

  • DNA analysis (WeGene)

I uploaded my DNA from 23andMe into WeGene in March 2021.

Below are the preliminary results.

DNA Analysis of March 26th, 2021

The maternal haplogroup of my DNA is B5a1.

The Neanderthal ratio of my DNA is 2.336% (which is more than 11.83% of WeGene users).

145,948 people participated in the 姓氏祖源 analysis.

Ancestral components

WeGene gave me the following DNA analysis.

Chinese nation (中华民族) ancestral components

Total (89.22%)

Southern Han (南方汉族)65.61%
Miao-Yao language group (苗瑶语族群)8.37%

Dai (傣族)

5.88%

Mountain group (高山族群)

4.25%

Lahu (拉祜族)

2.75%

She Nationality (畲族)

1.17%

Tibetan (藏族)

1.16%

Southeast Asia (东南亚) ancestral components

Total (8.71%)

Viet Nam (越南京族)

2.95%

Thai (泰国人)

2.92%

Cambodian (柬埔寨人)

2.83%

South Asia (南亚) ancestral components

Total (2.03%)

Indian (印度人)

2.03%

‘Mountain group’ had a note saying “includes Ami and Atayal” but I am not sure whether or not this is related to my own DNA.

Ancestral similarity map

The darker the color, the more similar the average ancestral source of the users in this place is to my ancestral source, and the gray means that there are not enough users in the area to calculate the similarity. As the number of participating users in various regions increases, your results may be updated.

Further more, based on my DNA, WeGene gave me additional maps for the 3 provinces with whom my ancestry was estimated to be most similar:

  • Guizhou Province (9.68% similarity)
  • Chongqing Province (9.63% similarity)
  • Sichuan Province (9.63% similarity)

Further more, WeGene estimated that my ancestry was most similar with the people from the following 3 cities:

  • Qiandongnan City (17.98% similarity), in Guizhou Province
  • Anshun City (9.71% similarity), in Guizhou Province
  • Wenshan City (9.69% similarity), in Yunnan Province
  • Xiushan Tujia and Miao Autonomous counties and cities (9.65% similarity), in Chongqing Province
Guizhou Province

The ancestral similarity index between me and the people in Guizhou Province reached 9.68%

The map below shows Guizhou Province. The darker the color, the more similar to my ancestors.

Chinese nation (中华民族) ancestral components

Me (89.22%)

Local average (94.95%)

Southern Han (南方汉族)65.61%44.28%
Miao-Yao language group (苗瑶语族群)8.37%2.39%

Dai (傣族)

5.88%

2.16%

Mountain group (高山族群)

4.25%

1.84%

Lahu (拉祜族)

2.75%

1.03%

Other (其他)

2.36%

2.04%

Northern Han (北方汉族)0.00%32.22%
Naxi/Yi (纳西/彝族)0.00%6.02%

Mongolian language group (蒙古语族群)

0.00%

2.97%

Southeast Asia (东南亚) ancestral components

Me (8.71%)

Local average (2.78%)

Viet Nam (越南京族)

2.95%

1.78%

Thai (泰国人)

2.92%

0.47%

Cambodian (柬埔寨人)

2.83%

0.52%

Other (其他)

0.01%

0.01%

Among the data of Guizhou Province, the origins of people in Qiandongnan City are the most similar to mine, with an ancestral similarity index of 17.98%.

Chinese nation (中华民族) ancestral components

Me (89.22%)

Local average (94.89%)

Southern Han (南方汉族)65.61%

53.80%

Miao-Yao language group (苗瑶语族群)8.37%

4.91%

Dai (傣族)

5.88%

3.16%

Mountain group (高山族群)

4.25%

2.19%

Lahu (拉祜族)

2.75%

1.31%

Other (其他)

2.36%

3.01%

Northern Han (北方汉族)0.00%

22.94%

Naxi/Yi (纳西/彝族)0.00%

3.57%

Mongolian language group (蒙古语族群)

0.00%

2.97%

Southeast Asia (东南亚) ancestral components

Me (8.71%)

Local average (3.77%)

Viet Nam (越南京族)

2.95%

2.31%

Thai (泰国人)

2.92%

0.62%

Cambodian (柬埔寨人)

2.83%

0.82%

Other (其他)

0.01%

0.02%

People in Anshun City, Guizhou Province and I have an ancestral similarity index of 9.71%.

Chinese nation (中华民族) ancestral components

Me (89.22%)

Average in Anshun City (94.95%)

Southern Han (南方汉族)65.61%

45.32%

Miao-Yao language group (苗瑶语族群)8.37%

3.49%

Dai (傣族)

5.88%

2.53%

Mountain group (高山族群)

4.25%

1.97%

Lahu (拉祜族)

2.75%

1.04%

Other (其他)

2.36%

2.02%

Northern Han (北方汉族)0.00%

30.24%

Naxi/Yi (纳西/彝族)0.00%

6.10%

Mongolian language group (蒙古语族群)

0.00%

2.24%

Southeast Asia (东南亚) ancestral components

Me (8.71%)

Average in Anshun City (2.79%)

Viet Nam (越南京族)

2.95%

1.71%

Thai (泰国人)

2.92%

0.61%

Cambodian (柬埔寨人)

2.83%

0.47%

Other (其他)

0.01%

0%

Chongqing Province

The ancestral similarity index between me and the people in Chongqing province reached 9.63%.

The darker the color, the more similar to my ancestors.

Among the data of Chongqing province, the origins of people in Xiushan Tujia and Miao Autonomous counties and cities  (秀山土家族苗族自治县市) are the most similar to me, with a similarity index of 9.65%.

Chinese nation (中华民族) ancestral components

Me (89.22%)

Local Average (95.78%)

Southern Han (南方汉族)65.61%

42.35%

Miao-Yao language group (苗瑶语族群)8.37%

1.07%

Dai (傣族)

5.88%

1.32%

Mountain group (高山族群)

4.25%

1.47%

Lahu (拉祜族)

2.75%

0.71%

Other (其他)

2.36%

1.83%

Northern Han (北方汉族)0.00%

39.01%

Naxi/Yi (纳西/彝族)0.00%

4.80%

Mongolian language group (蒙古语族群)

0.00%

3.22%

Southeast Asia (东南亚) ancestral components

Me (8.71%)

Local average (1.59%)

Viet Nam (越南京族)

2.95%

1.12%

Thai (泰国人)

2.92%

0.24%

Cambodian (柬埔寨人)

2.83%

0.23%

Other (其他)

0.01%

0%

Sichuan Province

The ancestral similarity index between me and the people in Sichuan province reached 9.63%.

The darker the color, the more similar to my ancestors.

Among the people of Sichuan province, the origin of people in Ziyang City is the most similar to mine, with a similarity index of 9.65%.

Chinese nation (中华民族) ancestral components

Me (89.22%)

Local average (95.79%)

Southern Han (南方汉族)65.61%

40.50%

Miao-Yao language group (苗瑶语族群)8.37%

1.01%

Dai (傣族)

5.88%

1.41%

Mountain group (高山族群)

4.25%

1.47%

Lahu (拉祜族)

2.75%

0.72%

Other (其他)

2.36%

2.03%

Northern Han (北方汉族)0.00%

38.76%

Naxi/Yi (纳西/彝族)0.00%

5.90%

Mongolian language group (蒙古语族群)

0.00%

3.99%

Southeast Asia (东南亚) ancestral components

Me (8.71%)

Local average (1.12%)

Viet Nam (越南京族)

2.95%

1.12%

Thai (泰国人)

2.92%

0.25%

Cambodian (柬埔寨人)

2.83%

0.25%

Other (其他)

0.01%

0.01%

Northeast Asia (东南亚) ancestral components

Me (0.00%)

Local average (1.12%)

Japanese (日本人)

0.00%

1.58%

Other (其他)

0%

0.71%

Yunnan province

The darker the color, the more similar to my ancestors.


People in Wenshan City, Yunnan Province and I have an ancestral similarity index of 9.69%.

Chinese nation (中华民族) ancestral components

Me (89.22%)

Local average (93.10%)

Southern Han (南方汉族)65.61%

43.48%

Miao-Yao language group (苗瑶语族群)8.37%

2.78%

Dai (傣族)

5.88%

3.55%

Mountain group (高山族群)

4.25%

2.32%

Lahu (拉祜族)

2.75%

2.12%

Other (其他)

2.36%

1.87%

Northern Han (北方汉族)0.00%

25.24%

Naxi/Yi (纳西/彝族)0.00%

8.04%

Mongolian language group (蒙古语族群)

0.00%

3.70%

Southeast Asia (东南亚) ancestral components

Me (8.71%)

Local average (4.96%)

Viet Nam (越南京族)

2.95%

2.29%

Thai (泰国人)

2.92%

0.89%

Cambodian (柬埔寨人)

2.83%

1.77%

Other (其他)

0.01%

0.01%

  • DNA analysis (23Mofang)

I uploaded my DNA from 23andMe into WeGene in March 2021. Below are the preliminary results.

DNA Analysis of March 26th, 2021

Gene relationship distribution, Hometown distribution

Gene relationship distribution: a genetic relationship refers to people who share a piece of DNA with you. They may have a common ancestor with you in the past three hundred years.

Hometown distribution: we have found your genetic relationship in 17 provinces across the country, the number of genetic relationships is 125.

The distribution of your genetic relationship in each province is as follows:

City/Region (name)

City/Region (number of relationships)

Province (name)

Province (number of relationships)

Chongqing (city area)

14

Chongqing

15

Qiandongnan Miao and Dong Autonomous Prefecture

8

Guizhou

17

Chengdu

6

Sichuan

29

Huaihua

4

Hunan

10

Nanchong

3

Sichuan

29

Yibin

3

Sichuan

29

Bijie

3

Guizhou

17

Liuzhou

3

Guangxi

15

Guilin

3

Guangxi

15

Chaozhou

3

Guangdong

10

Nanjing

2

Jiangsu

5

Meishan

2

Sichuan

29

Zigong

2

Sichuan

29

Shanghai (city area)

2

Shanghai

2

Guiyang

2

Guizhou

17

Hechi

2

Guangxi

15

Baise

2

Guangxi

15

Guigang

2

Guangxi

15

Changde

2

Hunan

10

Shantou

2

Guangdong

10

Taizhou

1

Jiangsu

5

Yangzhou

1

Jiangsu

5

Suzhou

1

Jiangsu

5

Ankang

1

Shaanxi

3

Hanzhong

1

Shaanxi

3

Yulin

1

Shaanxi

3

Guang'an

1

Sichuan

29

Aba Tibetan and Qiang Autonomous Prefecture

1

Sichuan

29

Leshan

1

Sichuan

29

Luzhou

1

Sichuan

29

Suining

1

Sichuan

29

Guangyuan

1

Sichuan

29

Deyang

1

Sichuan

29

Neijang

1

Sichuan

29

Dazhou

1

Sichuan

29

Mianyang

1

Sichuan

29

Ziyang

1

Sichuan

29

Yellowstone

1

Hubei

2

Wuhan

1

Hubei

2

Yichun

1

Jiangxi

2

Nanchang

1

Jiangxi

2

Zhangzhou

1

Fujian

2

Quanzhou

1

Fujian

2

Tongren

1

Guizhou

17

Liupanshui

1

Guizhou

17

Anshun

1

Guizhou

17

Qiannan Buyi and Miao Autonomous Prefecture

1

Guizhou

17

Chongqing (county)

1

Chongqing

15

Nanning

1

Guangxi

15

Yulin

1

Guangxi

15

North Sea

1

Guangxi

15

Zhangjiajie

1

Hunan

10

Xiangtan

1

Hunan

10

Changsha

1

Hunan

10

Loudi

1

Hunan

10

Zhaoqing

1

Guangdong

10

Huizhou

1

Guangdong

10

Meizhou

1

Guangdong

10

Dongguan

1

Guangdong

10

Shaoguan

1

Guangdong

10

Kaifeng

1

Henan

1

Tai'an

1

Shandong

1

Altay area

1

Xinjiang

1

Beijing (city area)

1

Beijing

1

(user did not fill in hometown)



6

User did not fill in information (Zheijang)


Zheijang

3

This explores my own national lineage through DNA and the migration paths of my ancestors.

The following was hypothesized my 23Mofang:

  • Chinese nation: 100%
    • Miao (100%)

  • DNA analysis (23andMe)

I tested my DNA via the commercial company 23andMe.com, hoping to find my relatives. On February 4th, 2021 I received my DNA testing kit. On March 20th, 2021 I received my first DNA results analysis.

Maternal haplogroups identify ancient lines of women that all trace back to the same common ancestor. My haplogroup, shared with the Han, traces the long line of women in my family tree.

The maternal haplogroup of my DNA is B5a1 (which traces back to a woman who lived approximately 22,000 years ago, that is nearly 880 generations ago), which means I am a member of the haplogroup B5 (a haplogroup shared with the Han Chinese), and a member of the haplogroup B (a woman who lived approximately 50,000 years ago).

The common ancestor of haplogroup B, also known as B4’5, likely lived in Central or East Asia nearly 50,000 years ago. Her descendants remain common across Asia today, from Iran to Japan. The haplogroup is extremely common in parts of China, southeastern Asia and far beyond; the presence of B4’5 at levels of up to 50% among the Maori of New Zealand and native Hawaiians indicates their shared Southeast Asian heritage.

Recently, archaeologists uncovered the remains of dozens of prehistoric skeletons from present-day Korea. Using the latest technology to extract DNA from the remains, scientists found two individuals – both about 10,000 years old – belonging to Haplogroup B. The fact that Haplogroup B4’5 existed in East Asia so many years ago suggests that some of the earliest Koreans may have migrated from Central Asia.

On mainland Asia, there are two main sub-lineages of Haplogroup B: B4 and B5. Both are more common in Southeast Asia, with lower levels as one travels north across China and into southern Siberia. Haplogroup B4 is found in up to 30% of people from the Guizhou province of southern China, while B5 is found in 18% of people from the neighboring Hainan province.

One branch of B4, perhaps confusingly called B2, spread all the way to North and South America. In Mongolia and southern China there are members of haplogroup B4’5 who bear a distinctive DNA marker that is also common in the U.S. Southwest – an indication of common descent. The similarity could be coincidence. But it might also indicate that unlike the other haplogroups that spread into North America during the Ice Age, haplogroup B4’5 began its journey not in Siberia but in the heart of Asia.

Members of haplogroups B4 and B5 are quite frequent in both northern and southern Han Chinese populations. The Han people, who all share the same language and similar cultural practices, are the largest ethnic group in the world, with about 1.2 billion people. Historical evidence shows that Han people are descendants of the ancient Huaxia tribes that come from northern China, centered in Zhongyuan, China’s Central Plain. The spread of Han people, language and culture from northern to southern China only occurred in the last 2,000 years, and was likely driven by warfare and famine in the north. The roots of the Han lie in Zhongyuan, China’s Central Plain.

B5a1 is relatively uncommon among 23andMe customers. Only 1 in every 1,500 23andMe customers share my haplogroup assignment B5a1.

DNA Analysis of March 20th, 2021

Currently I share DNA with 808 other 23andMe customers, of which 333 are 3-4th cousins. 89% of my relatives have Chinese ancestry.

Ancestor location

In the last 200 years, my ancestors may have lived in the locations indicated below.

23andMe found evidence of my recent ancestry in Mainland China (highly likely match).

China has 31 administrative regions, and 23andMe found the strongest evidence of my ancestry in the following 10 regions, ranked form stronger evidence to weaker evidence:

  1. Guangdong province
  2. Zheijiang province
  3. Shandong province
  4. Jiangsu province
  5. Shanghai
  6. Yunnan province
  7. Sichuan province
  8. Jiangxi province
  9. Hubei province
  10. Liaoning province

23andMe did not detect enough evidence of recent ancestry from Taiwan.

23andMe also presented me with the following map on my dashboard, with orange roughly indicating (it just shows Yunnan province) ‘Chinese Dai’ ancestry, and red indicating ‘Chinese’ ancestry (it just shows Mainland China):

Recent ancestor locations (found in your Ancestry Detail reports) are intended to complement your ancestral breakdown and provide a more recent and granular view of your ancestry. To determine these results, we look for identical pieces of DNA that you have in common with individuals of known ancestry from around the world. Reference populations for recent ancestor locations are comprised of over 400,000 customers, and this number will continue to grow as our customer database expands. Each recent ancestor location has its own unique demographic history, so we’ve calibrated our algorithm to better reflect these differences. For a given recent ancestor location, we indicate our confidence in the result, reported as “possible match,” “likely match,” or “highly likely match.” If we are not able to detect recent ancestry from a location with confidence, we report this to you as “not detected.”

The map is a visual representation of your recent ancestor locations down to the state or county level. It is generated by aggregating the ancestral origins of individuals who share a minimum amount of DNA with me. Darker regions represent places where I have DNA in common with more people who report ancestry from that particular region. Because these results reflect the ancestries of individuals currently in the 23andMe reference database, expect to see my results change over time as that database grows (as more people with known ancestry join the 23andMe gene pool).

Ancestry composition

My ancestry composition indicates the ancestral origins of my family in the past 500 years (rough estimate; the results may also reflect ancestry from a much broader time window than the past 500 years). Click on the tabs below to change the statistical prediction interval of the results:

If you create a free 23andMe profile, you may also explore my ancestry composition report in an interactive fashion.

An ancestry composition algorithm calculates my ancestry by comparing my DNA to the DNA of people whose ancestries we already know. The reference datasets (gene pools) used for this consist of DNA from 14,437 people who were chosen generally to reflect populations that existed before transcontinental travel and migration were common (at least 500 years ago). However, because different parts of the world have their own unique demographic histories, some Ancestry Composition results may reflect ancestry from a much broader time window than the past 500 years.

A 90% prediction interval is more conservative statistically and means a higher level of confidence in the prediction. A 50% prediction interval is more speculative statistically and means a lower level of confidence in the prediction.

The colours represent my chromosomes; painted with my ancestry composition results. The first 22 are called autosomes and come in pairs of two, each represented by one of the colored horizontal lines in the graphic below. Chromosomes have different lengths, and are named 1 through 22, when sorted by size. Lastly, ancestry on my X chromosome were analysed: of which I have two copies since my sex is female.

In the reference datasets which were used in order to obtain my ancestral breakdown, there were 14,4437 individuals with whom my DNA was compared (1,315 in the group ‘Chinese & Southeast Asian’; of which 795 in the category ‘Chinese’; 82 in the category ‘Chinese Dai’.

Why are there some results indicuating ‘Broadly Chinese & Southeast Asian’? Broadly assigned ancestry can tell a different story about my genetic history than narrowly assigned ancestry. My DNA segments with broadly assigned ancestry may match reference individuals from a relatively wide range of ancestral populations – possibly reflecting widespread migrations that occurred earlier than the timeframe for 23andMe’s ancestry composition algorithm. However, broadly assigned ancestries could also be capturing unique populations for which 23andMe currently doesn’t have data.

Thus, these analyses will update and improve over time as more people with known ancestry join the 23andMe gene pool.

In total, there were 227 tested ancestry categories (also called populations) in the reference datasets. These are visualized in this image:

Of the 14,4437 individuals in the reference datasets, there were 2,245 individuals in the known ancestry group ‘East Asian & Native American’. The amount of people in the subgroups and categories of that group is shown below:

  • 2,245 East Asian & Native American
    • 1,315 Chinese & Southeast Asian
      • 795 Chinese (Chinese, Han, Hong Kongese, Taiwanese)
      • 82 Chinese Dai
      • 164 Filipino & Austronesian
      • 77 Indonesian, Thai, Khmer & Myanmar (Indonesian, Cambodian, Thai, Myanmar, Malaysian)
      • 197 Vietnamese
    • 815 Japanese & Korean
      • 474 Japanese
      • 341 Korean (North Korean, South Korean)
    • 74 Native American (Colombian, Kantiana, Maya, Pima, Surui, Guatemalan)
    • 41 Northern Asian
      • 19 Manchurian & Mongolian (Daur, Mongolian, Oroqen)
      • 22 Siberian (Yakut)

Other than the ‘East Asian & Native American’ group, there were the following amount of individuals with known ancestry in the reference datasets (I have only summed up the main groups here, with the subgroups within parentheses; but I have omitted the individual categories):

  • 1,612 Central & South Asian (Bengali & Northeast Indian, Central Asian, Gujarati Patidar, Northern Indian & Pakistani)
  • 6,350 European (Ashkenazi Jewish, Eastern European, Northwestern European, Southern European)
  • 29 Melanesian (Broadly Melanesian)
  • 1,991 Sub-Saharan African (African Hunter-Gatherer, Congolese & Southern East African, Northern East African, West African)
  • 2,210 Western Asian & North African (Arab & Egyptian & Levantine, North African, Northern West Asian)

In total, this amounts to 14,437 individuals (= 2,245+1,612+6,350+29+1,991+2,210) in the population reference datasets.

The reference datasets are made up of individuals from publicly available datasets including the Human Genome Diversity Project, HapMap, and the 1000 Genomes project, as well as individuals from private 23andMe data collections and a large number of 23andMe customers who have consented to participate in research. It was indicated that, of these 14,4437 individuals, there were 11,774 research-consented 23andMe customers and 2,663 non-customers.

More background information about the ‘ancestry composition’ algorithms of 23andMe can be found in their ‘Ancestry Composition Guide’.

Above are the results of the 23andMe ‘Ancestry Composition v5.9’ (genotyping chip: Version 5). The classification tool used to compare my DNA (24 to 149 windows per chromosome, consisting of 7,400 to 45,000 markers per chromosome, depending on the chromosome’s length) with the DNA in the reference datasets is a String-Kernel Support Vector Machine (SKSVM). You can find more technical information in 23andMe’s Ancestry Composition algorithm white paper (version of December 2020).

The Dai people of southern China belong to the larger Tai ethnolinguistic group that currently lives in parts of China, Burma, Laos, Vietnam, and Thailand. In China, the Dai are one of over 50 officially recognized ethnic minority groups, and are united by unique cultural traditions anchored in Dai folk religion or Buddhism. Most Chinese Dai live in southern and western Yunnan Province, and are genetically more similar to their Vietnamese neighbors than they are to the Han Chinese.

Ancestry timeline

My ancestry timeline tries to predict how many generations ago my most recent ancestor for each population was born.

The 23andMe algorithm estimated the following rough ‘ancestry timeline’ possibilities:

  • I likely had a parent (1 generation before me), or grandparent (2 generations before me) who was 100% Chinese. Based on this generation range (1 to 2), this person was likely born between 1940 and 1970 (based on statistically average child-bearing ages which might be very wrong for my genealogy).
  • I likely had a parent (1 generation before me), grandparent (2 generations before me), or great-grandparent (3 generations before me) who was 100% Chinese Dai. Based on this generation range (1 to 3), this person was likely born between 1910 and 1970 (based on statistically average child-bearing ages which might be very wrong for my genealogy).

It is important to know that these predictions are simplistic and might be very wrong. For example, the model assumes that my ancestry from each population originally comes from a single (recent or distant) ancestor. This might be very wrong for my particular genealogy.

  • An important caveat of the ‘ancestry timeline’ feature is that it assumes that your ancestry from each population originally comes from a single (recent or distant) ancestor. Though there are many possible ways that one can inherit ancestry (from any number of genealogical ancestors going back in time), to reduce the parameter space, the prediction model simplistically assumed that exactly one ancestor contributed to an ancestry. This means that this model might be very wrong for my particular genealogy.
  • It also says nothing about where a particular ancestor was born, it only assumes their genetics.
  • Population geneticists have estimated that the average generation time, or the number of years, on average, between the birth of an individual and their child’s birth, is about 30 years. This of course, represents an average, and may be wrong or inaccurate for my particular genealogy. If some of my ancestors had their children at much younger or older ages, then the ‘ancestry timeline’ estimate of the number of generations converted into years might be very wrong for my particular genealogy.

You can find more technical information in 23andMe’s Ancestry Timeline white paper (version of March 10th, 2017).

DNA relatives

On March 26th, 2021 I downloaded the CSV file of my DNA relatives as presented via 23andMe. This aggregated data contains shared DNA segments and profile data for the corresponding relatives. Different data is available for each relative based on their personal privacy settings and sharing level.

There were 1,112 rows in the CSV (23andMe claims these are 808 relatives). It should thus be noted that some rows were duplicates. I will remove the duplicates if I have time in the future. I made a list of both the ‘first’ as well as the ‘family’ names altogether, and from this list the following names were the most common:

Name Frequency
Vang 123
Yang 120
Lee 117
Xiong 98
Thao 54
Chang 34
Li 31
Nguyen 30
Chen 29
Wong 24
Moua 24
Lor 24
Vue 21
Zhang 20
Liu 19
Huang 18
Cha 17
Mai 17
Xu 17
Yu 16
M 16
Her 16
L 15
Chan 14
Y 14
Sun 14
K 12
Saelee 12
P 12
T 11
Sarah 10
Tran 10
Zhou 10
Lin 10
S 9
C 9
Jessica 9
Kong 9
Tang 8
Jobst 8
Dang 8
V 8
Lu 8
Hang 8
Lily 8
F 8
Meyer 8
Xyooj 8
Sayaovang 8
Ma 8
Andrew 7
H 7
Yi 7
Kim 7
Julie 7
A 7
B 7
Luo 6
Song 6
Bui 6
Kue 6
Kang 6
Dai 6
Tan 6
Kane 6
Thor 6
Cheng 6
Saechao 6
Williams 6
Yuan 6
Su 6
Chue 6
Vong 6
Pang 6
Khang 6
Meng 6
Torres 6
Walker 6
Hong 6
Young 6
Yin 6
Scott 6
Zhao 6
Phonseya 6
Cui 6
Springirth 6
Lo 6
Lynn 5
Jamie 5
David 5
Eric 5
Daniel 5
James 5
Peter 5
D 5
Kevin 5
Xia 5
Pa 5
N 5
Lam 5
Mee 4
Jenny 4
Hoang 4
Chiamee 4
Chow 4
Tiffany 4
Fu 4
So 4
Lalor 4
Michael 4
Trinh 4
Vicente 4
Saly 4
Burton 4
Wu 4
Cana 4
Hsiung 4
Tom 4
Ren 4
Kia 4
Christine 4
Anthony 4
Bienenfeld 4
Johnson 4
Ley 4
Michelle 4
Leung 4
Linda 4
Hou 4
Sharp 4
Lisa 4
Ger 4
R 4
Lang 4
Emily 4
Zhong 4
Foo 4
Dejoy-elliott 4
Ashley 4
Chung 4
Sebolino 4
Liang 4
Palmquist 4
Bee 4
Xai 4
Halaholo 4
Maogang 4
Tracy 4
Luedtke 4
Robinson 4
Melissa 4
Zhu 4
Rinehart 4
Silver 4
King 4
Boyle 4
Gordy 4
Rudstrã¶M 4
Marshall 4
Ayog 4
Way 4
Tham 4
Gong 4
Smith 4
Delisle 4
Elizabeth 4
Ho 4
Tzeo 4
Pha 4
Wechsler 4
Wilson 4
Banks 4
Minervino 4
Zheng 4
Cathy 4
Duong 4
Strickland 4
Gu 4
Jake 4
Cheng-kinnander 4
Caby 4
Chua 4
Bergeson 4
Millstein 4
Jerry 4
Wilkison 4
Mcinnis 4
Suhargo 4
Berger 4
Burrows 4
Tibbs 4
Luke 4
Soewargo 4
Quinn 4
Levin 4
Huerta 4
Tamon 4
Soendjaya 4
Chiu 4
Helmuth 4
Fung 4
Ss 3
Katherine 3
Vincent 3
Cy 3
Yer 3
Andy 3
Tou 3
Jy 3
Nhia 3
Dalee 3
Matthew 3
Miranda 3
Mv 3
Simon 3
Elmo 3
Carmen 3
Stephen 3
Sean 3
Gozong 3
Sunny 3
Liachoua 3
Jiang 3
Mei 3
Philip 3
Boon 3
Jennifer 3
Gina 3
Jean 3
Jonathan 3
Tseb 3
Blia 3
John 3
Eia 3
Zoe 3
Qa 3
Olivia 3
Stephanie 3
Julia 3
Parker 3
Christy 3
Grace 3
Tian 3
Yun 3
Chong 3
Tina 3
Max 3
Linh 3
Yp 3
Doua 3
Che 3
Ml 3
Yileng 3
Zong 3
Phoua 3
Gao 3
Anetta 3
Zang 3
Jenna 3
Cl 3
Ge 3
Epps 2
Namkoong 2
Smeets 2
Len 2
Susan 2
Bourland 2
Kao 2
Huali 2
Bower 2
Ha 2
Mary 2
Sandra 2
Dedicke 2
Pfarr 2
Darilyn 2
Esser 2
Maria 2
Cindy 2
Cortez 2
Peters 2
Ivan 2
Chinte 2
Kay 2
Cz 2
Sarabia 2
Tapia 2
Daeyee 2
Gonzalez 2
Anderson 2
ǐ¼ 2
Janelle 2
Saetern 2
Mcglynn 2
Glen 2
Fang 2
Depew 2
Wagner 2
Isabella 2
Speer 2
Nan 2
Kanboa 2
Qun 2
Kaitlyn 2
Cp 2
Zeidler 2
Rautenberg 2
Juliano 2
Jr 2
Quitugua 2
De 2
Soto 2
Sy 2
Ellen 2
Chee 2
Liv 2
Sim 2
Nguy 2
Hallie 2
Fuchs 2
Mikey 2
Jensen 2
Za 2
Teng 2
Chavez 2
Aaron 2
Mann 2
Osato 2
Lindsey 2
Betty 2
Tong 2
Francis 2
Natalie 2
Schumacher 2
Won 2
Provencio 2
Sequeira 2
Stauffer 2
Vee 2
Murphy 2
Po-chuan 2
Pujin 2
Si 2
Elvis 2
Hecht 2
Marygrace 2
Patshiab 2
Amy 2
Shumate 2
Liao 2
Rachel 2
Slack 2
Chester 2
Yias 2
Maiya 2
Bernardo 2
Xijing 2
Kou 2
Fong 2
Rufo 2
Gonzales 2
Taing 2
Sukserm 2
Greve 2
Nicholas 2
Choe 2
Wn 2
Colebrook 2
Wright 2
Moi 2
Siddikova 2
Junghwan 2
Erin 2
Durant 2
Rossett 2
Sienna 2
Siti 2
Xt 2
Alrich 2
Kent 2
Bt 2
Formeister 2
- 2
Zeizel 2
Olson 2
Carney 2
Lansigan 2
Chau 2
Caracas 2
Shirley 2
Jiaoyang 2
Da 2
Mata 2
Rist 2
Xiangxi 2
Tevita 2
Trat 2
Rockrohr 2
Evelee 2
Matsuoka 2
Garman 2
Thielman 2
Horstmanshoff 2
Dansunankul 2
Go 2
Calle 2
Thang 2
Kate 2
Mcmahon 2
Asialyn 2
Bambilla 2
Pane 2
Faye 2
Elsa 2
Felipe 2
Bai 2
Janice 2
Vy 2
Dong 2
Bibar 2
Angoco 2
Tori 2
Mf 2
Nancy 2
Xuan 2
Sw 2
Mclaughlin 2
Almedilla 2
Michell 2
Penner 2
Gunsalus 2
Mingmin 2
Lan 2
Saepan 2
Sia 2
Alessandra 2
Cisneros 2
Justin 2
Zoesch 2
Susie 2
Sherry 2
Ngo 2
Kyaw 2
White 2
Yuen 2
Zara 2
Sara 2
Pearce 2
Na 2
Sang 2
Elvin 2
Lem 2
Baxter 2
Peng 2
Sedewar 2
True 2
Koi 2
Nakano 2
Pahoua 2
Castaneda 2
Satsatin 2
Xiang 2
Douglas 2
Centeno 2
Xoua 2
Khowong 2
Learkena 2
Geis 2
Juliette 2
Leena 2
Callie 2
Coleman 2
Marks 2
Tiamchat 2
Yee 2
Lili 2
Yue 2
Truli 2
Garcia 2
Cahn 2
Caberte 2
Ka 2
Paige 2
Alexa 2
Kathleen 2
Stumpner 2
Ying 2
Mavandi 2
Pan 2
Xiyao 2
Maclean 2
Mona 2
Foerster 2
Cheo 2
Guo 2
Hsu 2
Steven 2
Oei 2
Mccollum 2
Thia 2
Umstead 2
Junaedy 2
Ap 2
Valerie 2
Leong 2
Mia 2
Jack 2
Annie 2
Sok 2
Silvela 2
Shao 2
Yan 2
Ronald 2
Ab 2
Castillo 2
Jolly 2
Forte 2
Chou 2
Holman 2
Pindar 2
Chin 2
Mariah 2
Terence 2
Edward 2
Chiang 2
Lianne 2
Xee 2
Siyu 2
Jiear 2
Comey 2
Donald 2
Bo 2
Yongyi 2
Am 2
Nasso 2
Tswvyim 2
Shan 2
Xong 2
Neng 2
Thai 2
Yeo 2
Stiessberger 2
Sophia 2
Chris 2
Nghiem 2
Degong 2
Smit 2
Johnston 2
Anna 2
Mellman 2
Mor 2
Yuda 2
Guojing 2
Silva 2
Cotronis 2
Larsson 2
Myo 2
Jiaming 2
Sofie 2
Wynne 2
Long 2
Mosey 2
G 2
Preston 2
Angelia 2
Choua 2
Ep. 2
Scarlett 2
Dolbear 2
San 2
Christina 2
Ellis 2
Hu 2
Louie 2
Yeng 2
Mandy 2
Huehoua 2
Guan 2
Phetdavone 2
Jl 2
Born 2
Lee-yang 2
Lortongsy 2
Mc 2
Wai 2
Mah 2
Feingold 2
Peralta 2
Ning 2
Lui 2
Lun 2
Hsieh 2
Russell 2
Frey 2
Lunow-luke 2
Jasengnou 2
Funakoshi 2
Shi 2
Jiangcheng 2
Nc 2
Werho 2
Al 2
Haruk 2
Ky 2
Michel 2
Mapi 2
Cartier 2
Hongamata 2
Craig 2
Fisco 2
X 2
Yong 2
W 2
Suen 2
Maxwell 2
Ji 2
Deng 2
Kuan 2
Cao 2
Mcguire 2
Lindholm 2
Teal 2
Rubel 2
Jang 2
Maung 2
Mac 2
Zuniga 2
Catuar 2
Rodriguez 2
Tanaka 2
Pangilinan-lane 2
Sl 2
Kwon 2
Lapitan 2
Boudville 2
Dalman 2
Rotgans 2
Ong 2
Sengsoulichanh 2
Lim 2
Lever 2
Ramos 2
Chandler 2
Wang 2
Yiqi 1
Liping 1
Rt 1
Trong 1
Mon 1
Destiney 1
J 1
Roos 1
Claire 1
Allan 1
Leana 1
Fiona 1
Anida 1
Hazel 1
Jeffrey 1
Annaliesa 1
Polly 1
Shantelle 1
Jillian 1
Truwe 1
Kristine 1
Margaret 1
Rielyn 1
Ronelito 1
Jada 1
Howard 1
Leila 1
Sachi 1
Tram-diana 1
ĸ½ 1
Melody 1
Leanna 1
Tousue 1
Ariel 1
Karissa 1
Priscilla 1
Jg 1
Harrison 1
Jt 1
Loretta 1
Phonexay 1
Stefan 1
Brenna 1
Jorge 1
Tuazon 1
Joyce 1
Kristin 1
Alexander 1
Judy 1
May 1
Hl 1
Pathur 1
Kendra 1
Zachary 1
Penny 1
Ht 1
Hideki 1
Kha 1
Wen 1
Melanie 1
Sabrina 1
Jia 1
Diana 1
Rosa 1
Kwi 1
Kyoko 1
Kelsey 1
Alycia 1
Bi 1
Jae-lyn 1
Afton 1
Yangjiao 1
Xz 1
Jiao 1
Kathy 1
Eliana 1
Rebecca 1
Yves 1
Natcha 1
Xifan 1
Mina 1
Minh 1
Nina 1
Zemin 1
Ia 1
Joe 1
Zukhra 1
Casima 1
Faofizah 1
Lawkong 1
Terry 1
Kf 1
Alyssa 1
Katy 1
Kristie 1
Tricia 1
('ray') 1
Clarita 1
Elyse 1
Nathan 1
Alfred 1
Haley 1
Starr 1
Kiki 1
Avery 1
Ernest 1
Xiaoyan 1
Ll 1
Zan 1
Hua 1
Auyporn 1
Franklin 1
Chelsie 1
Sandy 1
Naciacien 1
Nzer 1
Allen 1
Mark 1
Hongfei 1
Art 1
Carolina 1
Mengyao 1
Kaoqi 1
Ziqi 1
Jill 1
Yu-wen 1
Weijie 1
Aimee 1
Tony 1
Ethan 1
Ricky 1
Valouny 1
Jo 1
Debbie 1
Macy 1
Ew 1
Kin 1
Danielle 1
Maele 1
Charles 1
Lila 1
Kristina 1
Elvira 1
Felicia 1
Aidan 1
Ping 1
Thomas 1
Elsie 1
Aoyi 1
Chartchuea 1
Pax 1
Pengyu 1
Naomi 1
Edwin 1
Rp 1
Shuning 1
Iskandar 1
Elise 1
Shuang 1
Liming 1
Emi 1
Eveline 1
Holly 1
Randolph 1
Kazoua 1
Chor 1
Chantha 1
Corazon 1
Qian 1
Charlton 1
Esmeralda 1
Audrey 1
Erina 1
Kasey 1
Jialin 1
Ming 1
Pongfoua 1
Pauletta 1
Rozalie 1
Pamela 1
Huiling 1
Phillip 1
Malayna 1
Lydia 1
Hanna 1
Yc 1
Yibo 1
Annamarie 1
Emmie 1
Kirk 1
Shangyuan 1
Stella 1
Nuntana 1
Peiyi 1
Keng 1
Jinsha 1
Pheng 1
June 1
Sherie 1
Bounme 1
Dv 1
Theechoua 1
Cd 1
Anita 1
Guizhou 1
Micaela 1
Kn 1
Pk 1
Katie 1
Rg 1
Pl 1
Kx 1
Dee 1
Dia 1
Nt 1
Ad 1
Tze 1
Nj 1
I 1
Pm 1
Yanan 1
Xy 1
Emma 1
Rot 1
Tx 1
Maia 1
Dh 1
Lanee 1
Av 1
Nl 1
Nv 1
Taying 1
Sheng 1
An 1
Violet 1
Evie 1
Theresa 1
Arianna 1
Maleena 1
Yh 1
Terri 1
Shuyang 1
Gail 1
Lela 1
Alicia 1
Mh 1
Peiyuan 1
Yonghao 1
Gaoshia 1
Naly 1
Cameron 1
Kk 1
Zongyan 1
Isabel 1
Johanna 1
Jude 1
Norachart 1
Khanh 1
Lc 1
Bella 1
Robert 1
Sam 1
Bl 1
Jb 1
Gk 1
Tam 1
Wz 1
Hongyi 1
Hairui 1
Ae 1
Jacky 1
Yuhua 1
Gm 1
Janet 1
Km 1
Frank 1
Sen 1
Silvie 1
Marianne 1
Dan 1
Meia 1
Rd 1
Xp 1
Lv 1
Pye 1
Phyo 1
Francesca 1
Ziwen 1
Bm 1
Mx 1
Eva 1
Molly 1
At 1
Dy 1
Shih-te 1
Angie 1
Bp 1
Rl 1
Ke 1
Marina 1
Dl 1
Feng 1
Samantha 1
Vs 1
Francisco 1
Dixuchang 1
Namiko 1
Jinsey 1
Dao 1
Benjamin 1
Js 1
Evelyn 1
El 1
Emanuel 1
Karl 1
Dd 1
Pt 1
Wd 1
Faith 1
Joann 1
Wyatt 1
Lx 1
Cristorey 1
Yt 1
Meichen 1
Hiroko 1
Joy 1
Miriam 1
Teresa 1
Kc 1
Liying 1
Sylvia 1
Cyrus 1
Leonard 1
Herminia 1
Jannette 1
Yinyu 1
Yueqi 1
Tw 1
  1. In your 23andMe profile, first navigate to DNA Relatives (you can navigate there via the menu by clicking ‘Family & Friends’ and then ‘List’)
  2. At the bottom of that page, click ‘Download aggregate data’. This will let you download your DNA relatives data as a CSV file.
  3. Now open that CSV file with Microsoft Excel.
  4. We will now use a tool to split all the entered names (which might be entered with spaces) into chunks (without spaces) to hopefully get a more accurate picture. To do this, first select all data of column A, then copy all of data in a string-splitting tool called https://onlinestringtools.com/split-string. Now below the last line in that tool, we will copy all data of column B (from the CSV opened in Microsoft Excel) into that same tool as well. I chose to use both columns A and B, because some people put their surname in the ‘Display Name’ column and vice versa. This way, we will capture all names, both first and last names, but the last names will be most common anyway. Now, we have split all the names which included spaces into their chunks. So in the online tool under ‘Chunks’ you can now click ‘Copy to clipboard’.
  5. Now we are going to paste this into a tool which counts the number of duplicates. We also want to find a counting tool which disregards capitalizations. You could paste it in the tool https://www.browserling.com/tools/word-frequency for example.
  6. Afterwards you can re-capitalize each row, if you want, for example using https://capitalizemytitle.com/.

If I create a new list, only for people who have a minimum of 0.25% shared DNA with me (198 rows), then the results are as follows:

Name Frequency
Yang 52
Lee 45
Vang 44
Thao 22
Xiong 14
Moua 10
Vue 10
Hang 8
Jobst 8
Xyooj 8
LI 8
Meyer 8
Mai 7
Wong 6
Kane 6
Young 6
Dang 6
Dai 6
Chang 6
YU 6
Springirth 6
Lor 6
Sarah 6
Kong 6
K 5
Chue 5
Lynn 4
Rudstrã¶M 4
Helmuth 4
Huerta 4
Suhargo 4
Boyle 4
Sayaovang 4
Rinehart 4
Way 4
Tang 4
Zhang 4
Ger 4
Y 4
P 4
Cathy 4
Chiamee 4
Marshall 4
Ashley 4
V 3
Philip 3
ML 3
Eia 3
Yileng 3
QA 3
Elizabeth 3
Jessica 3
Blia 3
Tian 3
Carmen 3
Zang 3
Luke 3
Elmo 3
David 3
Dalee 3
Stephanie 3
Lang 3
Andrew 3
Jenna 3
Tou 3
Nhia 3
Gina 3
Doua 3
Che 3
Zong 3
Gozong 3
Yp 3
Phoua 3
GAO 2
Born 2
Ma 2
Lee-Yang 2
Fung 2
Lortongsy 2
Tran 2
Mah 2
Feingold 2
Stauffer 2
Peralta 2
Guan 2
Jl 2
CL 2
M 2
Elsa 2
Ep. 2
Wynne 2
Huehoua 2
King 2
Terence 2
Xong 2
Yias 2
Kate 2
Melissa 2
Linda 2
Erin 2
Olivia 2
Tori 2
Kathleen 2
Yan 2
Anita 1
Micaela 1
N 1
DV 1
James 1
Guizhou 1
Theechoua 1
CD 1
Zoe 1
G 1
KN 1
PK 1
Ad 1
Kevin 1
Debbie 1
Katie 1
Rg 1
Grace 1
MC 1
Stephen 1
Theresa 1
Tracy 1
PL 1
Kx 1
Dee 1
Wai 1
Tze 1
Rachel 1
Kelsey 1
Dia 1
Nt 1
NJ 1
Jake 1
June 1
Neanderthal Ancestry

On my dashboard of 23andMe it is claimed that I have more Neanderthal DNA than 97% of other 23andMe customers. It should be noted that this percentage might be very inaccurate. You can read the comments at the bottom of this 23andMe blog post Celebrate your Ancient DNA with a New Neanderthal Report where customers complain about the sudden spikes in percentages.

I inherited a small amount of DNA from my Neanderthal ancestors. Out of the 7,462 variants which 23andMe tested, they found 315 variants in my DNA that trace back to the Neanderthals. All together, my Neanderthal ancestry accounts for less than ~2 percent of my DNA.

Neanderthals were prehistoric humans who interbred with modern humans before disappearing around 40,000 years ago.

Who has Neanderthal ancestry today? European, Asian, and indigenous American populations today have between 1–2 percent Neanderthal DNA, but Sub-Saharan African populations have significantly less. While Neanderthal remains have been found close to Africa there is no evidence that Neanderthals ever called the continent home. 

23andMe tests for Neanderthal ancestry at 3,731 markers scattered across the genome. At each of these markers you can have a genetic variant that evolved in Neanderthals and came back into the human lineage when the two groups interbred. Because you inherit variants from both of your parents, you can have 0, 1, or 2 copies of the Neanderthal variant at each marker. 23andMe reports your total number of Neanderthal variant copies, which is therefore a number between 0 and 7,462. However, nobody has all 7,462 — the most 23andMe has ever seen in a customer is less than 500.

A variant is a difference in the DNA sequence between individuals. For example, some people might have an “A” at a certain location, whereas other people might have a “T.” Many variants have no effect, but some variants can be associated with diseases, traits, or ancestral groups. Because we inherit our DNA from both parents, we can have up to two variants at a given position – one copy from each parent.

I have the following genotypes for the following tested markers (I have no variants associated with Neanderthal traits):

  • rs72686076: G
  • rs62405860: T
  • rs11434494:2 C
  • rs62569478: T
  • rs75906140: G
  • rs75859481: (my genotype platform does not have this variant)
  • rs62243065: C
  • rs17068342: C
  • rs113229445: G
  • rs2308321: A
  • rs7169404: G
  • rs17404153: G
  • rs62405860: T
  • rs3807714: A
  • rs12440878: G
  • rs74606019: A
  • rs17672692: C
  • rs2562762: T
  • rs12912713: A
  • rs114344942: C
  • rs117334853: A
  • rs78329842: A
  • rs3807714: A
  • rs61740705: A
  • rs13097409: A
  • rs1566479: T
  • rs4849721: G
  • rs11213819: C
  • rs3818532: A
  • rs1364405: G
  • rs3818532: A
  • rs4849721: G

23andMe always reports genotypes based on the ‘positive’ strand of the human genome reference sequence (build 37). Other sources sometimes report genotypes using the opposite strand. This test cannot distinguish which copy you received from which parent.

For more information, you can consult the 23andMe Neanderthal Ancestry Inference white paper (last updated December 14th, 2015).

  • DNA analysis (GEDmatch)

I also uploaded the 23andMe DNA to GEDmatch.

As a general note, you must be sure to disregard the following matches (these names always wrongly show up for Chinese adoptees who used 23andMe to upload their data to GEDmatch):

Kit

NameE-mailTesting Company

TP5754308

*anna

⚫⚫ihuiqian0413@⚫⚫⚫.com

-

KY4975086

*Theo

⚫⚫eo@⚫⚫⚫.kr

23mofang

FV7251194

*Theo

⚫⚫eo@⚫⚫⚫.kr

23mofang

ZM2557852

huangxin

⚫⚫024@⚫⚫⚫.com

23mofang

AG2135149

Chinese Korean

⚫⚫4769402@⚫⚫⚫.com

-

UJ5043670

Dongguan chen

⚫⚫4769402@⚫⚫.com

-

RN9067922

Acheng Zhaoye

⚫⚫75981066@⚫⚫⚫.com

WeGene

SK9814599

Guangzhou

⚫⚫4769402@⚫⚫⚫.com

23mofang

SF3481477

Michelle

⚫⚫yuanxin@⚫⚫⚫.com

23andMe

The ability to compare DNA from various platforms is one of GedMatch’s strengths, but in a sense it is also a weakness. In programing the weakness if called “GIGO,” or “Garbage in, garbage out.” Not all DNA processing companies are created equal for matching purposes. The gold standard is 23andMe, which currently tests 640,000 SNPs (segments of your genome that are different among people). The more SNPs that are tested, the finer the “resolution” is for your DNA. It is like a TV — the more pixels, the better the picture. 23andMe has the highest number of “pixels” (SNPs) in the industry. Ancestry, FamilyTree, and MyHeritage compare a similar number, but there is small variation between companies as to which “SNPs” are tested. For matching purposes, it doesn’t matter because with that many data points being compared, true relationships can be accurately determined. Thus, if you match to a relative that used 23andMe, or Ancestry, or one of the other premium testing companies, the match you see will be a solid, accurate match.

The problem is when the match is to a non-US company like 23Mofang or WeGene.

The problem lies in the fact that 23Mofang and WeGene look at different SNPs than 23andMe, Ancestry, and other western processors. As 23Mofang detailed it, “We undertook modifications to some of the loci of the array to improve its applicability to the genome of the Chinese population.” In other words, 23Mofang (as well as WeGene) tests different genetic markers than 23andMe, Ancestry, etc. That is why kits from these Chinese data bases often show up near the top of Chinese adoptee’s relatives lists. But sadly, these relationships are almost always exaggerated. 

And matches from 23Mofang and WeGene are not commonly usable for either purpose.

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