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Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature.

Pan Z, Trikalinos TA, Kavvoura FK, Lau J, Ioannidis JP - PLoS Med. (2005)

Bottom Line: Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001).The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se).Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se).

View Article: PubMed Central - PubMed

Affiliation: Department of Rheumatology, Shandong Provincial Hospital, Jinan 250021, Shandong, China.

ABSTRACT

Background: Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases.

Methods and findings: We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14-35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2-21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se).

Conclusion: Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general.

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Categorization of the Examined Genetic Association StudiesIQR, interquartile range; N, sample size (as median and interquartile range); StatSig, statistically significant at the 0.05 level.
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pmed-0020334-g001: Categorization of the Examined Genetic Association StudiesIQR, interquartile range; N, sample size (as median and interquartile range); StatSig, statistically significant at the 0.05 level.

Mentions: Thirteen published meta-analyses were found with at least 15 non-Chinese studies [16–26]. Data on any Chinese studies could be retrieved for 12 of those, and these 12 topics are considered from now on (for the association of DRD2 TaqIA polymorphism with alcoholism [26], no Chinese study was identified; Table 1). Overall, there were 161 eligible Chinese studies, only 20 of which were indexed in PubMed. Of the 20 Chinese studies indexed in PubMed (two on ID1, two on ID2, one on ID3, two on ID4, five on ID10, one on ID11, and seven on ID12; Table 1), only six had already been included in the published meta-analyses (one on ID11 and five on ID12), while the others were more recent; only seven of the 20 were published in full-text English journals. Of the 309 non-Chinese studies already included in the published meta-analyses, 44 pertained to populations of Asian descent (Japan, n = 25; Korea, n = 7; Chinese people outside of China, n = 5; Taiwan, n = 4; Malaysia, n = 2; and Singapore n = 1), and 265 to people of non-Asian descent (Figure 1).


Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature.

Pan Z, Trikalinos TA, Kavvoura FK, Lau J, Ioannidis JP - PLoS Med. (2005)

Categorization of the Examined Genetic Association StudiesIQR, interquartile range; N, sample size (as median and interquartile range); StatSig, statistically significant at the 0.05 level.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1285066&req=5

pmed-0020334-g001: Categorization of the Examined Genetic Association StudiesIQR, interquartile range; N, sample size (as median and interquartile range); StatSig, statistically significant at the 0.05 level.
Mentions: Thirteen published meta-analyses were found with at least 15 non-Chinese studies [16–26]. Data on any Chinese studies could be retrieved for 12 of those, and these 12 topics are considered from now on (for the association of DRD2 TaqIA polymorphism with alcoholism [26], no Chinese study was identified; Table 1). Overall, there were 161 eligible Chinese studies, only 20 of which were indexed in PubMed. Of the 20 Chinese studies indexed in PubMed (two on ID1, two on ID2, one on ID3, two on ID4, five on ID10, one on ID11, and seven on ID12; Table 1), only six had already been included in the published meta-analyses (one on ID11 and five on ID12), while the others were more recent; only seven of the 20 were published in full-text English journals. Of the 309 non-Chinese studies already included in the published meta-analyses, 44 pertained to populations of Asian descent (Japan, n = 25; Korea, n = 7; Chinese people outside of China, n = 5; Taiwan, n = 4; Malaysia, n = 2; and Singapore n = 1), and 265 to people of non-Asian descent (Figure 1).

Bottom Line: Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001).The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se).Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se).

View Article: PubMed Central - PubMed

Affiliation: Department of Rheumatology, Shandong Provincial Hospital, Jinan 250021, Shandong, China.

ABSTRACT

Background: Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases.

Methods and findings: We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14-35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2-21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se).

Conclusion: Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general.

Show MeSH
Related in: MedlinePlus