Catalogue of Bias<\/a>, ascertainment bias occurs when a sample being studied is not representative of the target population. This can produce misleading or even false conclusions, and it can be hard to detect since it cannot usually be identified by examining the sample alone. This is why many studies try to use variables other than participation in the study to make sure their samples are as representative as possible.<\/p>\nStudies examining how a particular treatment affects a particular health outcome often try to handle ascertainment bias by adjusting for \u201ccovariates,\u201d things like education level or socioeconomic status, that could affect health outcomes independently of the treatment. But Stefania Benonisdottir and Augustine Kong at Oxford\u2019s Big Data Institute have just demonstrated that we can determine if genetic studies are biased using nothing but the genes of the participants.<\/p>\n
And they used that technique to show that there\u2019s a genetic contribution that influences the tendency to participate in genetic studies.<\/p>\n
Finding bias<\/h2>\n
You may wonder how this was done\u2014quite reasonably, since we can\u2019t very well compare the genes of participants to those of non-participants. The analysis done by Kong and his student relies on the key idea that a genetic sequence that occurs more frequently in participants than in nonparticipants will also occur more frequently in the genetic regions that are shared by two related participants.<\/p>\n
Put differently, a bit of DNA that is common in the population will show up frequently in the study. But it will still only have a 50\/50 chance of showing up in the child of someone who carried a copy. If a bit of DNA makes people more likely to enroll in genetic studies, it will be more common both in the overall data and among closely related family members.<\/p>\n
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