在此錦上添花，如有不妥，還請樓主直接讓版主刪了此貼文。本人毫無芥蒂。
以下原文取自：
https://www.theverge.com/2019/1/ ... ure-nurture-science
論文本身的確是有在期刊刊登。
https://www.nature.com/articles/s41588-018-0313-7
以下附上粗淺的譯文，謹供大家參考。
Data from a private insurance company has given scientists a new way to study whether nature or nurture matters more when it comes to staying healthy in the face of disease. Though the answer isn’t definitive or exact — it varies according to each of the 560 diseases that were studied — the technique holds promise for bringing more insights in the future.
保險公司提供了科學家一個新的方式來研究“先天基因”和“後天環境”對人類健康和疾病的影響。針對其研究的560種疾病，結果雖然沒有全面地提供確實或者準確的答案，還是為將來更深入的研究帶來希望。
The traditional way of studying nature versus nurture relies on twins. Because identical twins share the same genetic code, comparing the health of twins can help determine whether genetic or environmental factors play more of a role in their health. Problem is, it can be hard to find many pairs of twins, so most twin studies use small datasets and look at one disease at a time. The new study, published this week in Nature Genetics, uses a database of 45 million people, including over 56,000 pairs of twins.
傳統研究先後天影響的研究仰賴雙胞胎的案例，因為同卵雙生的基因完全相同，比較他們的健康狀態，進而可推測基因抑或是環境，對其健康造成了影響。這份刊登在Nature Genetics的論文使用了四千五百萬人的資料，其中包含五萬六千對雙胞胎。
The hope is that the results will help guide further research into the causes of various conditions
Some conditions, like Huntington’s, are 100 percent influenced by genetics, meaning that if you inherit a genetic mutation, there’s a 100 percent chance you’ll get the disease, no matter how wealthy you are or where you live or what you eat. The chances of getting other diseases, like asthma, are far more influenced by factors in a person’s environment, like climate and wealth, than by their genetic code. The Nature Genetics study found that genes influenced at least 40 percent of the 560 diseases, with cognitive disorders being the most influenced by genetics. About a quarter of the diseases were at least partly caused by environment, with eye diseases having the largest environmental influence.
有些疾病，例如亨丁頓疾病，是百分之一百受到基因影響。意即如果你遺傳了這項基因變異，那麼百分之一百將會發病，無論你多有錢，住在什麼環境，或三餐吃些什麼。其他疾病，如氣喘，則是個人環境裡的因素影響較大，氣候，經濟能力的影響多過遺傳基因。這份Nature Genetics的研究發現最少560種疾病中的40%種，其中尤其是認知功能障礙，為遺傳基因所影響。
The data in question comes from the private health insurer Aetna, which shared this data (stripped of identifying information) with Harvard University’s Department of Biomedical Informatics. The researchers had the idea for this study when they noticed that the data included the dependents of the primary subscriber. Basically, they could see the insurance information of children who were on their parents’ Aetna insurance, explains study co-author Chirag Patel, a Harvard University professor of biomedical informatics. Next, the researchers looked at the birth dates to figure out if they were twins. Then, they used a statistical technique to figure out the likelihood that the twins were identical or fraternal. (Fraternal twins only share half of their DNA.)
研究資料來自保險公司，Aetna。他們將去除個人身份證明的資料和哈佛大學的研究團隊分享，研究團隊利用被保險人的生日推斷是否為雙胞胎，再進一步利用統計學的工具判定兩人是否為同卵雙生，因為異卵雙生這有一半基因會完全相同。
The database included zip codes (which the scientists used to extrapolate factors like socioeconomic status and air pollution), a record of doctor visits, and diagnosis codes from the International Classification of Diseases, explains first author Chirag Lakhani, a research fellow in biomedical informatics at Harvard. By combining and analyzing all of this data, the scientists were able to tease out the relative contribution of genetic versus environmental factors for those 560 conditions, which include everything from heart disease to connective tissue disease to blood disease.
這份資料包括了被保險人居住的社區郵遞區號（科學家以此獲得社經地位和空氣品質的環境因素），就醫記錄以及確診的疾病分類。藉由整合以及分析所有資料，科學家排除了這560種疾病相對性的先後天因素的影響，例如心血管，結締組織和血液方面的疾病，
The hope, Lakhani says, is that the results will help guide further research into the causes of various conditions. “For example, if you’re interested in lead poisoning, genetics plays a very small role and we need to think about the environment,” he says. “But for other cases like ADHD that are more likely to have a hard genetic component, we can think about other ways to interrogate the disease,” he says.
這份研究希望能提供其他研究者一些指標。例如鉛中毒和基因的關聯性很小，但是過動兒的研究則該專注於遺傳基因的方向，尋求了解分析這項疾病的新方向。
Patel and Lakhani point out that their study has limitations. For one, they didn’t look at ultra rare diseases, and because they were looking at twins young enough to still be on their parents’ health insurance, the analysis excludes diseases like Parkinson’s or Alzheimer’s that develop in old age.
這份研究並未檢視罕見疾病，並且雙胞胎的年齡都偏低（因為他們要在父母名下保險，通常都是未成年，或是仍在學），故分析也不包含年長才會發生的帕金森以及失智症。
Dan Belsky, a professor of epidemiology at Columbia University’s Mailman School of Public Health who was not involved in the study, said that the study method helps solve a big problem in medical research: that the people who sign up to participate in studies could be fundamentally different from the people who don’t. That makes the results incomplete and not representative. “Nobody is going to go out and collect data from this many people, but you can partner with the people who hold this data to leverage the extraordinary data capture that’s occurring in our lives to advance science,” he says. “This is a very carefully done study and I think it’s exciting to see this scale of data put to this question.”
這份研究也同時排除了一個實驗常見的問題：就是志願參加者本身就是一個影響數據的因素，意即志願者也許有相同的特徵，有一定傾向，這樣會扭曲了數據的分析。這份資料是由第三方提供，而且人數之多，實驗者樂見這個新的合作模式用以解答這類問題。
That said, today’s study doesn’t perfectly circumvent that problem either. As Patel points out, the people who have private insurance like Aetna have different circumstances from those who have, for example, Medicare. The next step is to try to use the method on many different databases. “I think there’s tremendous possibility of leveraging these same methods in those populations to better get a grasp on other factors that matter that weren’t showing up as strong,” he says.
雖然如此，今天這個研究還是不能稱得上是完美地研究以及解答了這個問題。能負擔得起私人保險（或受僱因而讓公司保險）的族群，和使用社會福利保險的族群不盡相同。將來的目標是把這次的研究方法使用在更廣泛性的資料上。
Correction Jan. 16th, 2019 6PM EST: An earlier version of this article incorrectly stated that Dan Belsky was a professor at Duke University. He is now a professor at Columbia University.
樓主
發表於 2019-6-7 01:25:36