TEDMED 2018
Yaniv Erlich: How we're building the world's largest family tree
雅尼夫‧埃利希: 我们如何建立世界上最大的家谱树
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计算遗传学家亚尼夫·埃尔利希 (Yaniv Erlich) 帮助建立了世界上最大的家谱——包括1300 万人，可以追溯到 500 多年前。他分享了一些有趣的研究模式——关于我们的爱情生活、我们的健康，甚至几十年前的刑事案件——并展示了众包家谱数据库如何不仅能揭示过去，还能揭示未来。
Yaniv Erlich - Computational geneticist
Yaniv Erlich is fascinated by the connection between DNA and data. Full bio
Yaniv Erlich is fascinated by the connection between DNA and data. Full bio
Double-click the English transcript below to play the video.
00:12
People use the internet
for various reasons.
for various reasons.
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人们因各种原因使用着互联网。
00:17
It turns out that one of the most
popular categories of website
popular categories of website
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一种最受欢迎的网站
00:21
is something that people
typically consume in private.
typically consume in private.
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是人们常常私下浏览的东西。
00:25
It involves curiosity,
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它涉及到好奇心，
00:28
non-insignificant levels
of self-indulgence
of self-indulgence
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无关自我放纵程度，
00:31
and is centered around recording
the reproductive activities
the reproductive activities
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并以记录他人的生殖记录
00:35
of other people.
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为中心。
00:36
(Laughter)
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（笑声）
00:37
Of course, I'm talking about genealogy --
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当然，我讨论的是家谱学——
00:39
(Laughter)
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（笑声）
00:41
the study of family history.
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也就是对家庭历史的研究。
00:43
When it comes to detailing family history,
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当说到详细的家族历史，
00:45
in every family, we have this person
that is obsessed with genealogy.
that is obsessed with genealogy.
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在每个家庭中，我们都有
一个痴迷于家谱的人。
一个痴迷于家谱的人。
00:49
Let's call him Uncle Bernie.
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我们姑且叫他伯尼叔叔吧。
00:51
Uncle Bernie is exactly the last person
you want to sit next to
you want to sit next to
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伯尼叔叔正是你在感恩节晚餐上，
00:54
in Thanksgiving dinner,
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最不想坐在一起的人，
00:56
because he will bore you to death
with peculiar details
with peculiar details
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因为他会用一些远古亲戚的奇特细节
00:59
about some ancient relatives.
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把你烦死。
01:02
But as you know,
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但正如你所知，
01:03
there is a scientific side for everything,
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任何事物都有科学的一面，
01:06
and we found that Uncle Bernie's stories
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我们发现伯尼叔叔的故事
01:09
have immense potential
for biomedical research.
for biomedical research.
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具有巨大的生物医学研究潜力。
01:13
We let Uncle Bernie
and his fellow genealogists
and his fellow genealogists
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我们让伯尼叔叔和他的家谱同行，
01:16
document their family trees through
a genealogy website called geni.com.
a genealogy website called geni.com.
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通过族谱网站 geni.com
记录他们的家谱。
记录他们的家谱。
01:21
When users upload
their trees to the website,
their trees to the website,
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当用户上传他们的家谱树到网站时，
01:23
it scans their relatives,
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网站会扫描他们的亲戚，
01:25
and if it finds matches to existing trees,
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如果它发现匹配上现存的家谱树，
01:27
it merges the existing
and the new tree together.
and the new tree together.
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它会合并现存的和新的家谱树。
01:31
The result is that large
family trees are created,
family trees are created,
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结果是超大的家族树创建起来了，
01:34
beyond the individual level
of each genealogist.
of each genealogist.
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超越了每个家谱学家的个人水平。
01:38
Now, by repeating this process
with millions of people
with millions of people
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现在，凭借着全球数百万人
01:42
all over the world,
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不断重复这个过程，
01:44
we can crowdsource the construction
of a family tree of all humankind.
of a family tree of all humankind.
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我们可以众包全人类家谱树的建设。
01:51
Using this website,
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使用这个网站，
01:52
we were able to connect 125 million people
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我们能够在一颗家族树上连接
01:57
into a single family tree.
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1.25 亿人。
02:00
I cannot draw the tree
on the screens over here
on the screens over here
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我无法在这里的屏幕上画家谱树，
02:03
because they have less pixels
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因为它们的像素比
02:05
than the number of people in this tree.
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在这棵树上的人还少。
02:08
But here is an example of a subset
of 6,000 individuals.
of 6,000 individuals.
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但这里有一个 6000 人的子集例子。
02:14
Each green node is a person.
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每一个绿色的节点是一个人。
02:17
The red nodes represent marriages,
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红色的节点代表婚姻，
02:19
and the connections represent parenthood.
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连接代表亲子关系。
02:22
In the middle of this tree,
you see the ancestors.
you see the ancestors.
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在这个树的中央是祖先。
02:24
And as we go to the periphery,
you see the descendants.
you see the descendants.
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外围是后代。
这棵树大约有 7 代。
02:27
This tree has seven
generations, approximately.
generations, approximately.
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02:31
Now, this is what happens
when we increase the number of individuals
when we increase the number of individuals
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而这是当我们增加人数到 7 万人时
02:34
to 70,000 people --
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的样子——
02:36
still a tiny subset
of all the data that we have.
of all the data that we have.
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仍然是我们拥有的所有
数据集的一小部分。
数据集的一小部分。
02:41
Despite that, you can already see
the formation of gigantic family trees
the formation of gigantic family trees
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即便如此，你已经能够看到由许多远亲
02:46
with many very distant relatives.
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组成的一棵巨大家谱树。
02:49
Thanks to the hard work
of our genealogists,
of our genealogists,
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感谢家谱学家的努力工作，
02:52
we can go back in time
hundreds of years ago.
hundreds of years ago.
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我们可以回到数百年前。
02:56
For example, here is Alexander Hamilton,
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比如，这是亚历山大·汉密尔顿，
02:59
who was born in 1755.
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他出生于 1755 年。
03:02
Alexander was the first
US Secretary of the Treasury,
US Secretary of the Treasury,
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亚历山大是首任美国财政部长，
03:06
but mostly known today
due to a popular Broadway musical.
due to a popular Broadway musical.
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但主要由于一部流行的百老汇
音乐剧而广为人知。
音乐剧而广为人知。
03:11
We found that Alexander has deeper
connections in the showbiz industry.
connections in the showbiz industry.
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我们发现亚历山大在娱乐圈
有更深厚的人脉。
有更深厚的人脉。
03:16
In fact, he's a blood relative of ...
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事实上，他是——
03:18
Kevin Bacon!
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凯文·贝肯的血亲！
03:20
(Laughter)
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（笑声）
03:22
Both of them are descendants
of a lady from Scotland
of a lady from Scotland
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他们都是13世纪一位来自
03:24
who lived in the 13th century.
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苏格兰的女士的后代。
03:27
So you can say that Alexander Hamilton
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所以你可以说亚历山大·汉密尔顿
03:30
is 35 degrees of Kevin Bacon genealogy.
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是 35 度凯文·贝肯的宗谱。
03:33
(Laughter)
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（笑声）
03:34
And our tree has millions
of stories like that.
of stories like that.
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我们的家谱树有数百万类似的故事。
03:40
We invested significant efforts
to validate the quality of our data.
to validate the quality of our data.
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我们投入了不小的工作
在验证数据的质量上。
在验证数据的质量上。
03:45
Using DNA, we found that .3 percent of
the mother-child connections in our data
the mother-child connections in our data
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使用DNA，我们发现我们
数据中有 0.3% 的母子关系
数据中有 0.3% 的母子关系
03:50
are wrong,
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是错误的，
03:51
which could match the adoption rate
in the US pre-Second World War.
in the US pre-Second World War.
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这可能与二战前美国的收养率相当。
03:56
For the father's side,
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父亲方面，
03:58
the news is not as good:
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消息也并不乐观：
04:02
1.9 percent of the father-child
connections in our data are wrong.
connections in our data are wrong.
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我们的数据中 1.9% 的
父子关系是错误的。
父子关系是错误的。
04:07
And I see some people smirk over here.
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我看到有人在这儿讪笑。
04:10
It is what you think --
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这是你们在想的——
04:11
there are many milkmen out there.
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外面有很多挤牛奶的人。
04:13
(Laughter)
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（笑声）
04:14
However, this 1.9 percent error rate
in patrilineal connections
in patrilineal connections
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然而这 1.9% 的父子关系错误率
04:18
is not unique to our data.
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不是我们数据独有的。
04:20
Previous studies found
a similar error rate
a similar error rate
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早先使用临床级血统的研究
04:23
using clinical-grade pedigrees.
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也发现了类似的错误率。
04:26
So the quality of our data is good,
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所以我们的数据质量是良好的，
04:28
and that should not be a surprise.
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并且这也不应该是个意外。
04:30
Our genealogists have
a profound, vested interest
a profound, vested interest
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我们的系谱学家对正确记录
04:34
in correctly documenting
their family history.
their family history.
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他们的家族史有着浓厚的兴趣。
04:40
We can leverage this data to learn
quantitative information about humanity,
quantitative information about humanity,
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我们可以利用这些数据来
了解人类的定量信息，
了解人类的定量信息，
04:45
for example, questions about demography.
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比如，有关人口统计学的问题。
04:47
Here is a look at all our profiles
on the map of the world.
on the map of the world.
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这是我们的资料在世界地图上的样子。
04:52
Each pixel is a person
that lived at some point.
that lived at some point.
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每个像素代表一个生活在特定位置的人。
04:56
And since we have so much data,
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由于我们有很多数据，
04:58
you can see the contours
of many countries,
of many countries,
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你可以看到很多国家的轮廓，
05:01
especially in the Western world.
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尤其在西方世界。
05:03
In this clip, we stratified
the map that I've showed you
the map that I've showed you
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在这个视频片段中，
我们把给你展示的地图
我们把给你展示的地图
05:06
based on the year of births of individuals
from 1400 to 1900,
from 1400 to 1900,
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根据 1400-1900 年出生
的人口进行分层，
的人口进行分层，
05:12
and we compared it
to known migration events.
to known migration events.
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并且跟已知的迁移事件比较。
05:15
The clip is going to show you
that the deepest lineages in our data
that the deepest lineages in our data
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这个视频将向你展示
我们数据中最深的血统，
我们数据中最深的血统，
05:18
go all the way back to the UK,
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可以追溯到英国，
05:20
where they had better record keeping,
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这里有更好的记录保存，
05:22
and then they spread along
the routes of Western colonialism.
the routes of Western colonialism.
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然后他们沿着西方殖民主义
的道路传播。
的道路传播。
05:25
Let's watch this.
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让我们来看看这个。
05:27
(Music)
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（音乐）
05:28
[Year of birth: ]
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【出生年份：】
05:31
[1492 - Columbus sails the ocean blue]
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【1492 - 哥伦布蓝色海洋航行时期】
05:35
[1620 - Mayflower lands in Massachusetts]
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【1620 - 五月花号在马萨诸塞州着陆】
05:38
[1652 - Dutch settle in South Africa]
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【1652 - 荷兰人在南非定居】
05:44
[1788 - Great Britain penal
transportation to Australia starts]
transportation to Australia starts]
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【1788 - 英国开始向澳大利亚
进行刑事流放】】
进行刑事流放】】
05:47
[1836 - First migrants use Oregon Trail]
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【1836 - 第一批移民来到俄勒冈小道】
05:50
[all activity]
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【所有活动】
05:55
I love this movie.
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我爱这个视频。
05:57
Now, since these migration events
are giving the context of families,
are giving the context of families,
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因为这些移民时间
提供了家庭的背景，
提供了家庭的背景，
06:02
we can ask questions such as:
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我们可以问诸如此类的问题：
06:04
What is the typical distance
between the birth locations
between the birth locations
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丈夫和妻子出生地
06:08
of husbands and wives?
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的特定距离是多少？
06:11
This distance plays
a pivotal role in demography,
a pivotal role in demography,
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这一距离在人口统计学中
起着重要的作用，
起着重要的作用，
06:14
because the patterns in which
people migrate to form families
people migrate to form families
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因为人们迁移形成家庭的模式
06:18
determine how genes spread
in geographical areas.
in geographical areas.
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决定了基因如何在地理位置上传播。
06:22
We analyzed this distance using our data,
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我们使用我们的数据分析了这个距离，
06:25
and we found that in the old days,
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我们发现在古时候，
06:27
people had it easy.
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人们过得很轻松。
06:28
They just married someone
in the village nearby.
in the village nearby.
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他们只是跟村子附近的某人结婚。
06:31
But the Industrial Revolution
really complicated our love life.
really complicated our love life.
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但工业革命复杂化了
我们的爱情生活。
我们的爱情生活。
06:35
And today, with affordable flights
and online social media,
and online social media,
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今天，凭着可负担的航班
和网络社交媒体，
和网络社交媒体，
06:40
people typically migrate more than
100 kilometers from their place of birth
100 kilometers from their place of birth
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人们通常从出生地迁移 100 多公里
06:45
to find their soul mate.
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来寻找灵魂伴侣。
06:48
So now you might ask:
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所以现在你可能会问：
06:49
OK, but who does the hard work
of migrating from places to places
of migrating from places to places
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好吧，但是谁会卖力从一个地方
迁移到另一个地方
迁移到另一个地方
06:54
to form families?
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去构建家庭呢？
06:55
Are these the males or the females?
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是男人还是女人？
06:59
We used our data to address this question,
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我们使用我们的数据解答了这个问题，
07:01
and at least in the last 300 years,
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至少在过去 300 年中，
07:04
we found that the ladies do the hard work
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我们发现女性从一个地方
迁移到另一个地方
迁移到另一个地方
07:08
of migrating from places
to places to form families.
to places to form families.
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去构建家庭上是最辛苦的。
这些结果在统计上很显著，
07:11
Now, these results
are statistically significant,
are statistically significant,
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07:14
so you can take it as scientific fact
that males are lazy.
that males are lazy.
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所以你可以把男性懒惰当作科学事实。
07:18
(Laughter)
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（笑声）
07:21
We can move from questions
about demography
about demography
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我们可以把问题从人口统计学开始
07:23
and ask questions about human health.
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转向人类健康问题。
07:26
For example, we can ask
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比如，我们可以问
07:28
to what extent genetic variations
account for differences in life span
account for differences in life span
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遗传变异能在多大程度上影响个体的
07:33
between individuals.
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寿命差异。
07:34
Previous studies analyzed the correlation
of longevity between twins
of longevity between twins
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之前的研究通过分析
双胞胎寿命的相关性
双胞胎寿命的相关性
07:39
to address this question.
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来解答这个问题。
07:41
They estimated that the genetic
variations account for
variations account for
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他们估计出遗传变异
07:44
about a quarter of the differences
in life span between individuals.
in life span between individuals.
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对个体寿命差异的影响大约占 1/4。
07:48
But twins can be correlated
due to so many reasons,
due to so many reasons,
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但双胞胎之间的关联有很多原因，
07:51
including various environmental effects
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包括多样的环境影响
07:53
or a shared household.
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或共同的家庭。
07:56
Large family trees give us the opportunity
to analyze both close relatives,
to analyze both close relatives,
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庞大的家谱树给了我们分析这些近亲，
08:00
such as twins,
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比如双胞胎，
08:01
all the way to distant relatives,
even fourth cousins.
even fourth cousins.
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到远房亲戚，
甚至四代表亲这样的机会。
甚至四代表亲这样的机会。
08:04
This way we can build robust models
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这样我们可以构建稳健的模型，
08:07
that can tease apart the contribution
of genetic variations
of genetic variations
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从环境因素中分离出
08:11
from environmental factors.
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遗传变异的贡献来。
08:13
We conducted this analysis using our data,
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我们使用数据执行了这个分析，
08:16
and we found that genetic variations
explain only 15 percent
explain only 15 percent
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发现遗传变异只解释了 15% 的
08:22
of the differences in life span
between individuals.
between individuals.
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个体寿命差异。
08:26
That is five years, on average.
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平均而言， 就是 5 年之差。
08:30
So genes matter less than
what we thought before to life span.
what we thought before to life span.
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所以基因对寿命的重要性
比我们之前想象的少。
比我们之前想象的少。
08:35
And I find it great news,
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我发现这是个好消息，
08:38
because it means that
our actions can matter more.
our actions can matter more.
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因为这意味着我们的行动更为重要。
08:42
Smoking, for example, determines
10 years of our life expectancy --
10 years of our life expectancy --
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举个例子，吸烟会影响
大约10年的预期寿命——
大约10年的预期寿命——
08:46
twice as much as what genetics determines.
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是基因所能影响的两倍。
08:50
We can even have more surprising findings
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随着我们从家谱树展开，
08:52
as we move from family trees
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让我们的家谱学专家建档，
08:54
and we let our genealogists
document and crowdsource DNA information.
document and crowdsource DNA information.
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并且众包DNA信息，
我们能有更多惊奇的发现。
我们能有更多惊奇的发现。
08:58
And the results can be amazing.
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结果将是惊人的。
09:01
It might be hard to imagine,
but Uncle Bernie and his friends
but Uncle Bernie and his friends
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可能令人难以想象，
伯尼叔叔和他的朋友
伯尼叔叔和他的朋友
09:05
can create DNA forensic capabilities
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能够创建 DNA 法医能力，
09:07
that even exceed
what the FBI currently has.
what the FBI currently has.
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甚至超过了 FBI 目前拥有的水平。
09:12
When you place the DNA
on a large family tree,
on a large family tree,
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当你把 DNA 放在一棵大的家谱树中，
09:15
you effectively create a beacon
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你就有效地创造了一个照亮
09:17
that illuminates the hundreds
of distant relatives
of distant relatives
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数百个远亲的灯塔，
09:20
that are all connected to the person
that originated the DNA.
that originated the DNA.
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他们都与 DNA 的拥有者有联系。
09:24
By placing multiple beacons
on a large family tree,
on a large family tree,
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通过在一棵大的家谱树中
放置不同的灯塔，
放置不同的灯塔，
09:27
you can now triangulate the DNA
of an unknown person,
of an unknown person,
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你现在可以对一个陌生人
的 DNA 进行三角测量，
的 DNA 进行三角测量，
09:31
the same way that the GPS system
uses multiple satellites
uses multiple satellites
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就跟 GPS 系统利用不同的卫星
09:35
to find a location.
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来定位一样。
09:37
The prime example
of the power of this technique
of the power of this technique
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这种技术威力一个的主要例子
09:40
is capturing the Golden State Killer,
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是追捕“金州杀手”，
09:44
one of the most notorious criminals
in the history of the US.
in the history of the US.
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美国历史上最臭名昭著的罪犯之一。
09:49
The FBI had been searching
for this person for over 40 years.
for this person for over 40 years.
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FBI 已经寻找这人超过 40 年。
09:55
They had his DNA,
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他们有他的 DNA，
09:57
but he never showed up
in any police database.
in any police database.
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但他从未出现在警方的数据库中。
10:01
About a year ago, the FBI
consulted a genetic genealogist,
consulted a genetic genealogist,
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大约一年前，FBI 咨询了
一位基因谱系学家，
一位基因谱系学家，
10:06
and she suggested that they submit
his DNA to a genealogy service
his DNA to a genealogy service
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她建议他们提交他的 DNA
到可以定位远房亲戚
到可以定位远房亲戚
10:10
that can locate distant relatives.
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的家谱服务平台上。
10:13
They did that,
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FBI 这样做了，
10:14
and they found a third cousin
of the Golden State Killer.
of the Golden State Killer.
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他们找到了金州杀手的第三代表亲。
10:18
They built a large family tree,
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他们构建了一棵巨大的家谱树，
10:20
scanned the different
branches of that tree,
branches of that tree,
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扫描树上的不同分支，
直到他们找到完美匹配
10:22
until they found a profile
that exactly matched
that exactly matched
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10:25
what they knew about
the Golden State Killer.
the Golden State Killer.
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他们所了解的金州杀手信息的人。
10:27
They obtained DNA from this person
and found a perfect match
and found a perfect match
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他们从这人身上取得 DNA 并发现
10:31
to the DNA they had in hand.
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跟他们手上的 DNA 一致。
10:33
They arrested him
and brought him to justice
and brought him to justice
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过了这么些年，他们终于逮捕了他，
10:35
after all these years.
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并绳之与法。
10:38
Since then, genetic genealogists
have started working with
have started working with
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自那之后，基因谱系学家开始
10:41
local US law enforcement agencies
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跟美国当地执法机构合作，
10:44
to use this technique
in order to capture criminals.
in order to capture criminals.
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使用这种技术来抓捕罪犯。
10:47
And only in the past six months,
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仅仅在过去的 6 个月，
10:50
they were able to solve
over 20 cold cases with this technique.
over 20 cold cases with this technique.
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他们使用这个技术就破获了
超过 20 个铁证悬案。
超过 20 个铁证悬案。
10:56
Luckily, we have people like Uncle
Bernie and his fellow genealogists
Bernie and his fellow genealogists
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幸好，我们有这群人，
像伯尼叔叔和他的家谱学同行，
像伯尼叔叔和他的家谱学同行，
11:01
These are not amateurs
with a self-serving hobby.
with a self-serving hobby.
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他们不只是业余爱好者。
11:04
These are citizen scientists
with a deep passion to tell us who we are.
with a deep passion to tell us who we are.
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他们是满怀热情的公民科学家，
想要揭开我们所有人身份的秘密。
想要揭开我们所有人身份的秘密。
11:11
And they know that the past
can hold a key to the future.
can hold a key to the future.
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他们知道，过去是通向未来的钥匙。
11:16
Thank you very much.
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谢谢大家。
11:17
(Applause)
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（鼓掌）
ABOUT THE SPEAKER
Yaniv Erlich - Computational geneticistYaniv Erlich is fascinated by the connection between DNA and data.
Why you should listen
As a professor and researcher at Columbia University and as CSO of MyHeritage.com, Yaniv Erlich has performed foundational work in genetic privacy and large-scale studies of crowdsourced genomic data. Dubbed a "genome hacker" by the journal Nature, Erlich and his team discovered a privacy loophole enabling reidentification of allegedly anonymous male research participants using just internet searches and their Y chromosome. Later, he discovered that 60 percent of all US individuals with European descent can be identified by forensic genetics using open genetic genealogy databases, which Science magazine called one of the top 10 breakthroughs of 2018.
Erlich is also responsible for the construction of the world's largest family tree, comprising 13 million people, as well as the development of the website DNA.land, which has compiled the genotypes of more than 150,000 donors. He has also worked to discover the genetic bases for several conditions in Israeli families. His team has demonstrated stable DNA data storage, reaching a density of 215 petabyte per gram of DNA. He's been awarded numerous prizes, has published more than 45 papers and authored seven patents.
Yaniv Erlich | Speaker | TED.com