今天我看到一篇 博客 ，里面有一个关于 Feynman 如何判断问题重要与否的故事。1966年，他的一个学生Koichi Mano给他写信，祝贺他拿到诺奖。Feynman问他最近做什么工作。Koichi回复他说自己在做一件很low的事情，（原文是 studying the Coherence theory with some applications to the propagation of electromagnetic waves through turbulent atmosphere ... a humble and down-to-earth type of problem .）。Feynman 看到他的答复很不安，于是有了下面一封信。里面表达了Feynman如何看待一个问题是不是值得自己去研究。我深以为然！我已经划重点了！ Dear Koichi, I was very happy to hear from you, and that you have such a position in the Research Laboratories. Unfortunately your letter made me unhappy for you seem to be truly sad. It seems that the influence of your teacher has been to give you a false idea of what are worthwhile problems. The worthwhile problems are the ones you can really solve or help solve, the ones you can really contribute something to. A problem is grand in science if it lies before us unsolved and we see some way for us to make some headway into it. I would advise you to take even simpler, or as you say, humbler, problems until you find some you can really solve easily, no matter how trivial . You will get the pleasure of success, and of helping your fellow man, even if it is only to answer a question in the mind of a colleague less able than you. You must not take away from yourself these pleasures because you have some erroneous idea of what is worthwhile. You met me at the peak of my career when I seemed to you to be concerned with problems close to the gods. But at the same time I had another Ph.D. Student (Albert Hibbs) whose thesis was on how it is that the winds build up waves blowing over water in the sea. I accepted him as a student because he came to me with the problem he wanted to solve. With you I made a mistake, I gave you the problem instead of letting you find your own; and left you with a wrong idea of what is interesting or pleasant or important to work on (namely those problems you see you may do something about). I am sorry, excuse me. I hope by this letter to correct it a little. I have worked on innumerable problems that you would call humble, but which I enjoyed and felt very good about because I sometimes could partially succeed. For example, experiments on the coefficient of friction on highly polished surfaces, to try to learn something about how friction worked (failure). Or, how elastic properties of crystals depends on the forces between the atoms in them, or how to make electroplated metal stick to plastic objects (like radio knobs). Or, how neutrons diffuse out of Uranium. Or, the reflection of electromagnetic waves from films coating glass. The development of shock waves in explosions. The design of a neutron counter. Why some elements capture electrons from the L-orbits, but not the K-orbits. General theory of how to fold paper to make a certain type of child’s toy (called flexagons). The energy levels in the light nuclei. The theory of turbulence (I have spent several years on it without success) . Plus all the “grander” problems of quantum theory. No problem is too small or too trivial if we can really do something about it. You say you are a nameless man. You are not to your wife and to your child. You will not long remain so to your immediate colleagues if you can answer their simple questions when they come into your office. You are not nameless to me. Do not remain nameless to yourself – it is too sad a way to be. Know your place in the world and evaluate yourself fairly, not in terms of your naïve ideals of your own youth, nor in terms of what you erroneously imagine your teacher’s ideals are. Best of luck and happiness. Sincerely, Richard P. Feynman
自从2015年初奥巴马提出了精准医学计划之后，基因检测被迅速推上神坛，国内外公司随即如雨后春笋般破土而出。其实，当我们静下心来去从文献中研究这一命题时，我们发现，美国早在2007年就已经有了直接面向消费者的基因检测产品。有遗传学基础的同学应该知道，随着遗传流行病学和关联分析的日渐发展，基于已知SNP构建的遗传风险模型在一定程度上能够对大多数疾病或者性转做出预测。然而，当你真正去仔细阅读一份检测报告时，你能辨别出这其中有多少具有可靠的科学依据，有多少是忽悠人的花花肠子吗？今天，就让我们抛开火热的市场宣传，从科学的角度刨析personal genome testing这件事。 What we talk about when we talk about genome testing？ 首先要回答这个问题，我们所关心的基因检测到底有怎么的能力。小编认为，要评价一个基因检测产品的好坏，最需要关注的是两个指标：predictive ability和predictive risk。Predictive ability是评价预测模型好坏的直接指标，这是一个群体水平的概念，即该产品是否有能力将人群中高（低）风险的人判定为有（无）风险，通常用ROC曲线下面积（AUC）来衡量。Predictive risk则是对于一个特定个体而言，他（她）对应于某一个疾病的风险值。 如何计算predictive risk 本质上，个体对于一种疾病的风险是指在其确定已知的基因型下，相对于人群平均风险比。简要地，可以用如下图1来描述计算过程。 图1，遗传风险大致计算流程 不得不说的秘密 下面该讲些核心的东西了，如何判断你手上的检测报告靠不靠谱，这也是大家最关心的。没有对比就没有伤害，我们让数据说话。参考一篇2014年发表在《Genet Med》上的文章，其对比了美国当时主流的3家提供遗传检测的公司（23andMe, deCODEme, Navigenics, 后两者现在已停止服务）对6种疾病的预测表现，并详细讨论了影响predictive ability和predictive risk的因素。 首先，在进行结果对比之前，作者先统计了不同公司所使用的SNP种类，也就是所谓的panel。如图2，可以明显地看到公司之间使用的panel差别很大。除此之外，3个公司使用的疾病平均风险也相差巨大。 图2，Differences in population risks and SNP panels among companies. panel决定predictive ability 可想而知，各个公司之间的预测能力应该是不同的。如图3，通过AUC结果的比较，可以得出以下两个结论： For each disease, the AUC of the tests differed among companies. The AUC values were also substantially different among diseases. 让我们来分析一下造成这种差异的原因。对于同一种疾病，不同公司间的主要区别在于SNP的选择；对于不同的疾病，预测能力跟risk位点的效应值有很大关系。因此，SNP的选择是影响predictive ability的主要因素。当然了，不同公司之间寻在算法不同的问题，但对预测模型的好坏贡献不大，除非有一种算法将被遗传因素也考虑到模型中。 多个因素决定predictive risk 由之前的讨论得知，预测能力是群体水平的判定，还需评价产品对个体风险的预测准确度。我们还是用一张图来说明，如图3，直接比较了各个公司之间对单个个体的风险预测值。 图3，各个公司对不同疾病的个体风险预测值对比 我们直接给出结论： higher population risk produce higher predictive risk； higher effect size lead overestimated risk; SNP lists also play a determinant role. 该不该做基因检测 终于要回答这个终极问题了，答案自然是肯定的。虽然检测结果存在着不一致性，但在可控的范围内（选用合适的数据），基因检测完全可以给出可信的报告。需要注意的是，对于有些遗传力较大的疾病或性状（大部分表性变异由遗传因素决定），且有一批效应值较大的易感位点，当检测公司使用针对客户人群适用的平均人群风险，allele frequency，OR等信息时，得到的结果往往具有较高的可信度。 转自生信草堂公众号，已授权
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