Recently The New York Times reported the following:
The Best Genetic Risk Tools Don’t Work Equally for
Everyone (1/2)
Genetic prediction models are poised to revolutionize
medicine. But because they have been trained on European DNA, they threaten to
widen health care disparities.
The NYT - By Emily Baumgaertner Nunn - Emily Baumgaertner
Nunn is a national health reporter for The Times, focusing on public health
issues that primarily affect vulnerable communities.
July 20, 2026, 5:01 a.m. ET
The National Institutes of Health recently announced a
remarkable achievement: the world’s largest database of human genomes, a
repository of information that the agency says will help usher in a new era in
personalized medicine.
Scientists are already developing clinical tools and techniques based on genomic information gathered from the agency’s All of Us program. One of the most promising? Polygenic risk scores. These can help predict a person’s likelihood of developing complex diseases, like cardiovascular disease and breast cancer, from as early as infancy.
But there’s a glaring problem: The tools were trained overwhelmingly on the DNA of people of European descent, so they often fail to accurately assess risks for anyone else. For some conditions, the forecasts for people of color are almost no better than flipping a coin.
Researchers are racing to correct the biases through advancements in modeling and widespread recruitment of minority groups. They fear that failing to close the gaps could worsen health care disparities — and prevent the technology from realizing its promise of helping to decrease chronic disease.
“It really warrants attention,” said Eimear Kenny, the director of the Institute for Genomic Health at Mount Sinai and a principal investigator in a national consortium that tests the genetic scores.
Geneticists measure a polygenic risk score by aggregating hundreds or thousands of tiny variations across the genome, many of which might nudge a person’s disease risk by only a fraction. By bundling them together, they can generate a cumulative score, ranking a person’s predisposition to common killers.
For patients at the extremely high end of the risk distribution, the scores can offer a medical advantage, potentially giving doctors a decades-long runway to intervene. Those in the highest percentiles for a cardiovascular disease score, for instance, can be prescribed cholesterol-lowering statins earlier, potentially helping prevent some heart attacks and strokes.
But the entire premise rests on the data used to train the model in the first place. The UK Biobank — established in the early 2000s and widely considered a leading genomic trove — is overwhelmingly homogenous; roughly 94 percent of its participants are white. The Million Veteran Program, a repository run by the U.S. Department of Veterans Affairs, is almost three-quarters white.
The cohort in the All of Us program, built by the N.I.H. with the goal of diversifying data, is still about 50 percent European, according to Alicia Martin, a statistical geneticist at the Broad Institute. The effort’s future is also at risk: One of its major funding streams, the 21st Century Cures Act, is set to expire at the end of this fiscal year. (The program’s budget has already been reduced by 72 percent since 2023.)
“You can’t just snap your fingers and overnight have a biobank from Africa, a biobank from India, a biobank from China that are all as large and open and comprehensive as the UK Biobank,” Dr. Martin said. “We can develop all the fancy statistical methods we want, but still not be able to overcome without more diverse representation.”
Other clinical tools could be affected by the ancestry bias, such as integrated artificial intelligence models that draw upon genomics and other biomedical data. Doctors who sequence a genome to look for rare mutations may also find genetic variants in patients of color that researchers have not studied enough to understand.
Genetic blind spots can also cause scientists to miss discoveries that benefit everyone. One example: the PCSK9 gene. Scientists found that a rare mutation that occurred mostly in a small proportion of Black people could naturally reduce the risk of developing coronary heart disease by 88 percent. That set off a race to develop drugs that mimic the mutation for people of all races and ethnicities. But scientists only found the variant because one study in Dallas had ensured that its participant pool was more than half Black.
(to be continued)
Translation
最佳基因風險評估工具並非對每個人都同樣有效（1/2）
基因預測模型有望徹底改變醫學。但由於這些模型是基於歐洲人的DNA進行訓練的，它們有可能加劇醫療保健方面的差異
美國國立衛生研究院（NIH）最近宣布了一項非凡的成就：建立了世界上最大的人類基因組資料庫。該機構表示，這個資訊庫將有助於開啟個人化醫療的新時代。
科學家已經開始基於NIH的「我們所有人」（All of Us）計劃所收集的基因組資訊來開發臨床工具和技術。其中最有前景的是什麼？多基因風險評分。這些評分可以幫助預測一個人從嬰兒時期開始患上複雜疾病（如心血管疾病和乳癌）的可能性。
但有一個顯而易見的問題：這些工具主要基於歐洲裔族群的DNA進行訓練，因此往往無法準確評估其他族群的風險。對於某些疾病，對有色人種的預測結果幾乎不比擲硬幣好到哪裡去。
研究人員正爭分奪秒地透過改進建模技術和廣泛招募少數族裔群體來糾正這些偏差。他們擔心，如果無法彌合這些差距，可能會加劇醫療保健方面的差異，並阻礙這項技術實現其降低慢性病發病率的承諾。
西奈山基因組健康研究所 所長、同時負責測試基因評分的國家聯盟首席研究員Eimear Kenny表示:「這確實值得關注」。
遺傳學家透過匯集基因組中數百或數千個微小變異來測量多基因風險評分，其中許多變異可能只會對一個人的患病風險產生微乎其微的影響。而透過將這些變異組合在一起，他們可以產生一個累積評分，從而評估一個人罹患常見致命疾病的傾向。
對於處於風險分佈極高端的患者而言，這些評分可以提供醫療優勢，使醫生有長達數十年的干預時間。例如，心血管疾病評分處於最高百份位的患者可以更早服用降膽固醇的他汀類藥物，這可能有助於預防部分心臟病和中風。
但這一切的前提都建立在最初用來訓練模型的資料之上。英國生物銀行（UK Biobank）－成立於21世紀初，被廣泛認為是領先的基因組資料庫－其參與者構成極為單一，約94%為白人。由美國退伍軍人事務部營運的「百萬退伍軍人計劃」（Million Veteran Program）作為一個資料庫其參與者中近四分之三為白人。
據Broad Institute的統計遺傳學家Alicia Martin稱，由美國國立衛生研究院（NIH）建立的「我們所有人」（All of Us）計劃是旨在實現數據多樣化，但該計劃的參與者中仍有約50%為歐洲人。而這項工作的未來也面臨風險：其主要資金來源之一 - 《21世紀治癒法案》 - 將於本財政年度結束時到期。 （自2023年以來，該項目的預算已被削減了72%。）
Martin博士說: 「 你不能打個響指就可以可以在一夜間建立一個像英國生物銀行一樣規模龐大、全面開放、覆蓋面廣闊的非洲生物銀行、印度生物銀行、中國生物銀行」; 「我們可以發展出各種炫麗的統計方法，但如果沒有更多樣化的代表性，我們仍然無法克服這個問題」。
其他臨床工具也可能受到種族偏差的影響，例如利用基因組學和其他生物醫學數據的整合人工智能模型。醫生在進行基因組定序以尋找罕見突變時，也可能在有色人種患者身上發現一些研究人員尚未充分研究的基因變異。
基因盲點也可能導致科學家錯過一些對所有人都有益處的發現。例如：PCSK9基因。科學家發現，一種主要存在於少數黑人中的罕見基因突變，能夠自然地將罹患冠心病的風險降低88%。這引發了一場旨在模仿這種突變研發藥物的競賽，做出適用於所有種族和民族的人藥物。但科學家之所以能夠發現這種變異，是因為達拉斯的一項研究確保了其參與者中的黑人佔超過一半。
（待續）
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