Our understanding of hereditary disease risks has blossomed during the past decade, largely due to thousands of genome-wide association studies (GWAS). These studies involve scanning through genomes to identify single-nucleotide polymorphisms (SNPs) that are associated with common diseases and traits. For example, individuals who have a “G” allele at rs7329174 (a SNP located on chromosome 13) are more likely to have Crohn’s disease than individuals who have an “A” allele at this SNP.
不幸的是，GWAS的结果描绘了人类健康和疾病的偏见。这是因为绝大多数GWA都使用了欧洲研究队列。欧洲人口中发现的疾病协会可能并不总是能很好地概括到其他人群中。对于经历了不同进化史的种群（例如，来自北欧和撒哈拉以南非洲的人口），这个问题被放大。
These biases hinder our ability to generalize results from one part of the world to other parts of the world.
GWAS中使用的基因分型技术也可能引起问题。这是因为大多数GWA都使用基因分阵列，而不是整个基因组测序。这些基因分型阵列倾向于包含最初在欧洲人群中确定的SNP。
In a recently publishedGenome Biologypaper, we found evidence of systematic biases in GWAS results. These biases hinder our ability to generalize results from one part of the world to other parts of the world. Specifically, we found that genetic predictions of disease risks can be grossly misestimated. This problem is particularly acute for African individuals, and it is caused by the biases that are described above.
Using computer simulations, we also found that African GWAS results generalize better across populations than non-African GWAS results. This asymmetry arises because non-African populations have experienced a loss of genetic diversity following the out-of-Africa migration (evolutionary history matters!).
Applications of genome-wide association study data
Genomic information is beginning to be applied in clinical settings. Because of this, our findings are timely. Using GWAS results, physician-scientists can count the number of risk-increasing alleles in each person’s genome to generate genetic risk scores for specific diseases. For example, genetic risk scores are able to identify women who are most at risk for breast cancer and determine the optimal age to begin mammogram screening.
可以改善遗传风险评分的另一种方法是纠正不同基因分型技术的偏见。
However, it is important to keep in mind that the utility of genetic risk scores depends on the quality of the source data. Genetic risk scores that work well for one population need not work well for other populations.
How can we improve genetic predictions of health and disease? Given unlimited funds, one option would be to repeat every GWAS in every population. Needless to say, this isn’t very feasible. A more practical solution is to generate genetic risk scores that correct for existing biases. For example, GWAS results may generalize well from the United Kingdom to Denmark, but substantial corrections may be needed to generalize results from the United Kingdom to Ghana.
可以改善遗传风险评分的另一种方法是纠正不同基因分型技术的偏见。校正的遗传风险评分也可以纳入进化信息，如我们最近的Genome Biology纸。只有纳入这些细节才能将基因组医学的好处扩展到来自不同全球人群的个人。
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