Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

A number of new metrics based on social media platforms—grouped under the term “altmetrics”—have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters’ Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators.     

Systematic Differences in Impact across Publication Tracks at PNAS

Background

Citation data can be used to evaluate the editorial policies and procedures of scientific journals. Here we investigate citation counts for the three different publication tracks of the Proceedings of the National Academy of Sciences of the United States of America (PNAS). This analysis explores the consequences of differences in editor and referee selection, while controlling for the prestige of the journal in which the papers appear.

Methodology/Principal Findings

We find that papers authored and “Contributed” by NAS members (Track III) are on average cited less often than papers that are “Communicated” for others by NAS members (Track I) or submitted directly via the standard peer review process (Track II). However, we also find that the variance in the citation count of Contributed papers, and to a lesser extent Communicated papers, is larger than for direct submissions. Therefore when examining the 10% most-cited papers from each track, Contributed papers receive the most citations, followed by Communicated papers, while Direct submissions receive the least citations.

Conclusion/Significance

Our findings suggest that PNAS “Contributed” papers, in which NAS–member authors select their own reviewers, balance an overall lower impact with an increased probability of publishing exceptional papers. This analysis demonstrates that different editorial procedures are associated with different levels of impact, even within the same prominent journal, and raises interesting questions about the most appropriate metrics for judging an editorial policy''s success.     

Citing a Data Repository: A Case Study of the Protein Data Bank

The Protein Data Bank (PDB) is the worldwide repository of 3D structures of proteins, nucleic acids and complex assemblies. The PDB’s large corpus of data (> 100,000 structures) and related citations provide a well-organized and extensive test set for developing and understanding data citation and access metrics. In this paper, we present a systematic investigation of how authors cite PDB as a data repository. We describe a novel metric based on information cascade constructed by exploring the citation network to measure influence between competing works and apply that to analyze different data citation practices to PDB. Based on this new metric, we found that the original publication of RCSB PDB in the year 2000 continues to attract most citations though many follow-up updates were published. None of these follow-up publications by members of the wwPDB organization can compete with the original publication in terms of citations and influence. Meanwhile, authors increasingly choose to use URLs of PDB in the text instead of citing PDB papers, leading to disruption of the growth of the literature citations. A comparison of data usage statistics and paper citations shows that PDB Web access is highly correlated with URL mentions in the text. The results reveal the trend of how authors cite a biomedical data repository and may provide useful insight of how to measure the impact of a data repository.     

The Kardashian index: a measure of discrepant social media profile for scientists

In the era of social media there are now many different ways that a scientist can build their public profile; the publication of high-quality scientific papers being just one. While social media is a valuable tool for outreach and the sharing of ideas, there is a danger that this form of communication is gaining too high a value and that we are losing sight of key metrics of scientific value, such as citation indices. To help quantify this, I propose the ‘Kardashian Index’, a measure of discrepancy between a scientist’s social media profile and publication record based on the direct comparison of numbers of citations and Twitter followers.     

Multiple Citation Indicators and Their Composite across Scientific Disciplines

Many fields face an increasing prevalence of multi-authorship, and this poses challenges in assessing citation metrics. Here, we explore multiple citation indicators that address total impact (number of citations, Hirsch H index [H]), co-authorship adjustment (Schreiber Hm index [Hm]), and author order (total citations to papers as single; single or first; or single, first, or last author). We demonstrate the correlation patterns between these indicators across 84,116 scientists (those among the top 30,000 for impact in a single year [2013] in at least one of these indicators) and separately across 12 scientific fields. Correlation patterns vary across these 12 fields. In physics, total citations are highly negatively correlated with indicators of co-authorship adjustment and of author order, while in other sciences the negative correlation is seen only for total citation impact and citations to papers as single author. We propose a composite score that sums standardized values of these six log-transformed indicators. Of the 1,000 top-ranked scientists with the composite score, only 322 are in the top 1,000 based on total citations. Many Nobel laureates and other extremely influential scientists rank among the top-1,000 with the composite indicator, but would rank much lower based on total citations. Conversely, many of the top 1,000 authors on total citations have had no single/first/last-authored cited paper. More Nobel laureates of 2011–2015 are among the top authors when authors are ranked by the composite score than by total citations, H index, or Hm index; 40/47 of these laureates are among the top 30,000 by at least one of the six indicators. We also explore the sensitivity of indicators to self-citation and alphabetic ordering of authors in papers across different scientific fields. Multiple indicators and their composite may give a more comprehensive picture of impact, although no citation indicator, single or composite, can be expected to select all the best scientists.     

Citation Classics in Systematic Reviews and Meta-Analyses: Who Wrote the Top 100 Most Cited Articles?

Background

Systematic reviews of the literature occupy the highest position in currently proposed hierarchies of evidence. The aims of this study were to assess whether citation classics exist in published systematic review and meta-analysis (SRM), examine the characteristics of the most frequently cited SRM articles, and evaluate the contribution of different world regions.

Methods

The 100 most cited SRM were identified in October 2012 using the Science Citation Index database of the Institute for Scientific Information. Data were extracted by one author. Spearman’s correlation was used to assess the association between years since publication, numbers of authors, article length, journal impact factor, and average citations per year.

Results

Among the 100 citation classics, published between 1977 and 2008, the most cited article received 7308 citations and the least-cited 675 citations. The average citations per year ranged from 27.8 to 401.6. First authors from the USA produced the highest number of citation classics (n=46), followed by the UK (n=28) and Canada (n=15). The 100 articles were published in 42 journals led by the Journal of the American Medical Association (n=18), followed by the British Medical Journal (n=14) and The Lancet (n=13). There was a statistically significant positive correlation between number of authors (Spearman’s rho=0.320, p=0.001), journal impact factor (rho=0.240, p=0.016) and average citations per year. There was a statistically significant negative correlation between average citations per year and year since publication (rho = -0.636, p=0.0001). The most cited papers identified seminal contributions and originators of landmark methodological aspects of SRM and reflect major advances in the management of and predisposing factors for chronic diseases.

Conclusions

Since the late 1970s, the USA, UK, and Canada have taken leadership in the production of citation classic papers. No first author from low or middle-income countries (LMIC) led one of the most cited 100 SRM.     

Impact of GDP,Spending on R&D,Number of Universities and Scientific Journals on Research Publications among Asian Countries

Objectives

This study aimed to compare the impact of Gross Domestic Product (GDP) per capita, spending on Research and Development (R&D), number of universities, and Indexed Scientific Journals on total number of research documents (papers), citations per document and Hirsch index (H-index) in various science and social science subjects among Asian countries.

Materials and Methods

In this study, 40 Asian countries were included. The information regarding Asian countries, their GDP per capita, spending on R&D, total number of universities and indexed scientific journals were collected. We recorded the bibliometric indicators, including total number of research documents, citations per document and H-index in various science and social sciences subjects during the period 1996–2011. The main sources for information were World Bank, SCI-mago/Scopus and Web of Science; Thomson Reuters.

Results

The mean per capita GDP for all the Asian countries is 14448.31±2854.40 US$, yearly per capita spending on R&D 0.64±0.16 US$, number of universities 72.37±18.32 and mean number of ISI indexed journal per country is 17.97±7.35. The mean of research documents published in various science and social science subjects among all the Asian countries during the period 1996–2011 is 158086.92±69204.09; citations per document 8.67±0.48; and H-index 122.8±19.21. Spending on R&D, number of universities and indexed journals have a positive correlation with number of published documents, citations per document and H-index in various science and social science subjects. However, there was no association between the per capita GDP and research outcomes.

Conclusion

The Asian countries who spend more on R&D have a large number of universities and scientific indexed journals produced more in research outcomes including total number of research publication, citations per documents and H-index in various science and social science subjects.     

A Genetic Variant in 12q13, a Possible Risk Factor for Bipolar Disorder,Is Associated with Depressive State,Accounting for Stressful Life Events

Genome-wide association studies (GWASs) have identified a number of susceptibility genes for schizophrenia (SCZ) and bipolar disorder (BD). However, the identification of risk genes for major depressive disorder (MDD) has been unsuccessful because the etiology of MDD is more influenced by environmental factors; thus, gene–environment (G×E) interactions are important, such as interplay with stressful life events (SLEs). We assessed the G×E interactions and main effects of genes targeting depressive symptoms. Using a case–control design, 922 hospital staff members were evaluated for depressive symptoms according to Beck Depressive Inventory (BDI; “depression” and “control” groups were classified by scores of 10 in the BDI test), SLEs, and personality. A total of sixty-three genetic variants were selected on the basis of previous GWASs of MDD, SCZ, and BD as well as candidate-gene (SLC6A4, BDNF, DBH, and FKBP5) studies. Logistic regression analysis revealed a marginally significant interaction (genetic variant × SLE) at rs4523957 (P_uncorrected = 0.0034) with depression and a significant association of single nucleotide polymorphism identified from evidence of BD GWAS (rs7296288, downstream of DHH at 12q13.1) with depression as the main effect (P_uncorrected = 9.4×10⁻⁴, P_corrected = 0.0424). We also found that SLEs had a larger impact on depression (odds ratio∼3), as reported previously. These results suggest that DHH plays a possible role in depression etiology; however, variants from MDD or SCZ GWAS evidence or candidate genes showed no significant associations or minimal effects of interactions with SLEs on depression.     

Looking for Landmarks: The Role of Expert Review and Bibliometric Analysis in Evaluating Scientific Publication Outputs

Objective

To compare expert assessment with bibliometric indicators as tools to assess the quality and importance of scientific research papers.

Methods and Materials

Shortly after their publication in 2005, the quality and importance of a cohort of nearly 700 Wellcome Trust (WT) associated research papers were assessed by expert reviewers; each paper was reviewed by two WT expert reviewers. After 3 years, we compared this initial assessment with other measures of paper impact.

Results

Shortly after publication, 62 (9%) of the 687 research papers were determined to describe at least a ‘major addition to knowledge’ –6 were thought to be ‘landmark’ papers. At an aggregate level, after 3 years, there was a strong positive association between expert assessment and impact as measured by number of citations and F1000 rating. However, there were some important exceptions indicating that bibliometric measures may not be sufficient in isolation as measures of research quality and importance, and especially not for assessing single papers or small groups of research publications.

Conclusion

When attempting to assess the quality and importance of research papers, we found that sole reliance on bibliometric indicators would have led us to miss papers containing important results as judged by expert review. In particular, some papers that were highly rated by experts were not highly cited during the first three years after publication. Tools that link expert peer reviews of research paper quality and importance to more quantitative indicators, such as citation analysis would be valuable additions to the field of research assessment and evaluation.     

Unraveling the Genetic Etiology of Adult Antisocial Behavior: A Genome-Wide Association Study

Crime poses a major burden for society. The heterogeneous nature of criminal behavior makes it difficult to unravel its causes. Relatively little research has been conducted on the genetic influences of criminal behavior. The few twin and adoption studies that have been undertaken suggest that about half of the variance in antisocial behavior can be explained by genetic factors. In order to identify the specific common genetic variants underlying this behavior, we conduct the first genome-wide association study (GWAS) on adult antisocial behavior. Our sample comprised a community sample of 4816 individuals who had completed a self-report questionnaire. No genetic polymorphisms reached genome-wide significance for association with adult antisocial behavior. In addition, none of the traditional candidate genes can be confirmed in our study. While not genome-wide significant, the gene with the strongest association (p-value = 8.7×10⁻⁵) was DYRK1A, a gene previously related to abnormal brain development and mental retardation. Future studies should use larger, more homogeneous samples to disentangle the etiology of antisocial behavior. Biosocial criminological research allows a more empirically grounded understanding of criminal behavior, which could ultimately inform and improve current treatment strategies.     

IDOL N342S Variant,Atherosclerosis Progression and Cardiovascular Disorders in the Italian General Population

Inducible degrader of the low density lipoprotein receptor (IDOL), is an E3 ubiquitin ligase that negatively modulates low density lipoprotein receptor (LDL-R) expression. Genome-wide association studies (GWAS) indicated that genetic variants in IDOL gene contributes to variation in LDL-C plasma levels and the detailed analysis of a specific locus resulted in the identification of the functional common single nucleotide polymorphism (SNP) rs9370867 (c.G1025A, p.N342S) associates with increased LDL-R degradation and increased LDL-C levels. These findings, however, were not confirmed in two other independent cohorts and no data about the impact of this variant on atherosclerosis progression and cardiovascular risk are available. Aim of this study was to investigate the association between a functional variant in IDOL and atherosclerosis progression in an Italian general population. 1384 subjects enrolled in the PLIC study (Progression of Lesions in the Intima of Carotid) were genotyped by Q-PCR allelic discrimination and the association with anthropometric parameters, plasma lipids and the carotid intima media thickness (cIMT) and the impact on cardiovascular disease (CVD) incidence were investigated. The N342S variant was not associated with changes of the plasma lipid profile among GG, AG or AA carriers, including total cholesterol (249±21, 249±19 and 248±21 mg/dl respectively), LDL-C (158±25, 161±22 and 160±23 mg/dL), cIMT (0.74±0.14, 0.75±0.17 and 0.77±0.15 mm) and CVD incidence. In agreement, the expression of LDLR and the uptake of LDL was similar in macrophages derived from GG and AA carriers. Taken together our findings indicate that the N342S variant does not impact plasma lipid profile and is not associated with atherosclerosis progression and CVD in the general population, suggesting that other variants in the IDOL gene might be functionally linked with cholesterol metabolism.     

A New Bibliometric Index Based on the Shape of the Citation Distribution

In order to improve the h-index in terms of its accuracy and sensitivity to the form of the citation distribution, we propose the new bibliometric index . The basic idea is to define, for any author with a given number of citations, an “ideal” citation distribution which represents a benchmark in terms of number of papers and number of citations per publication, and to obtain an index which increases its value when the real citation distribution approaches its ideal form. The method is very general because the ideal distribution can be defined differently according to the main objective of the index. In this paper we propose to define it by a “squared-form” distribution: this is consistent with many popular bibliometric indices, which reach their maximum value when the distribution is basically a “square”. This approach generally rewards the more regular and reliable researchers, and it seems to be especially suitable for dealing with common situations such as applications for academic positions. To show the advantages of the -index some mathematical properties are proved and an application to real data is proposed.     

Estimates of the Continuously Publishing Core in the Scientific Workforce

Background

The ability of a scientist to maintain a continuous stream of publication may be important, because research requires continuity of effort. However, there is no data on what proportion of scientists manages to publish each and every year over long periods of time.

Methodology/Principal Findings

Using the entire Scopus database, we estimated that there are 15,153,100 publishing scientists (distinct author identifiers) in the period 1996–2011. However, only 150,608 (<1%) of them have published something in each and every year in this 16-year period (uninterrupted, continuous presence [UCP] in the literature). This small core of scientists with UCP are far more cited than others, and they account for 41.7% of all papers in the same period and 87.1% of all papers with >1000 citations in the same period. Skipping even a single year substantially affected the average citation impact. We also studied the birth and death dynamics of membership in this influential UCP core, by imputing and estimating UCP-births and UCP-deaths. We estimated that 16,877 scientists would qualify for UCP-birth in 1997 (no publication in 1996, UCP in 1997–2012) and 9,673 scientists had their UCP-death in 2010. The relative representation of authors with UCP was enriched in Medical Research, in the academic sector and in Europe/North America, while the relative representation of authors without UCP was enriched in the Social Sciences and Humanities, in industry, and in other continents.

Conclusions

The proportion of the scientific workforce that maintains a continuous uninterrupted stream of publications each and every year over many years is very limited, but it accounts for the lion’s share of researchers with high citation impact. This finding may have implications for the structure, stability and vulnerability of the scientific workforce.     

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES''s false positive rate is correct, and that TATES''s statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.     

A Genome-Wide Association Study of Hypertension and Blood Pressure in African Americans

The evidence for the existence of genetic susceptibility variants for the common form of hypertension (“essential hypertension”) remains weak and inconsistent. We sought genetic variants underlying blood pressure (BP) by conducting a genome-wide association study (GWAS) among African Americans, a population group in the United States that is disproportionately affected by hypertension and associated complications, including stroke and kidney diseases. Using a dense panel of over 800,000 SNPs in a discovery sample of 1,017 African Americans from the Washington, D.C., metropolitan region, we identified multiple SNPs reaching genome-wide significance for systolic BP in or near the genes: PMS1, SLC24A4, YWHA7, IPO7, and CACANA1H. Two of these genes, SLC24A4 (a sodium/potassium/calcium exchanger) and CACNA1H (a voltage-dependent calcium channel), are potential candidate genes for BP regulation and the latter is a drug target for a class of calcium channel blockers. No variant reached genome wide significance for association with diastolic BP (top scoring SNP rs1867226, p = 5.8×10⁻⁷) or with hypertension as a binary trait (top scoring SNP rs9791170, p = 5.1×10⁻⁷). We replicated some of the significant SNPs in a sample of West Africans. Pathway analysis revealed that genes harboring top-scoring variants cluster in pathways and networks of biologic relevance to hypertension and BP regulation. This is the first GWAS for hypertension and BP in an African American population. The findings suggests that, in addition to or in lieu of relying solely on replicated variants of moderate-to-large effect reaching genome-wide significance, pathway and network approaches may be useful in identifying and prioritizing candidate genes/loci for further experiments.     

Contribution of Genome-Wide Association Studies to Scientific Research: A Pragmatic Approach to Evaluate Their Impact

The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.     

Academic Impact of Qualitative Studies in Healthcare: Bibliometric Analysis

Context

Although qualitative studies are becoming more appreciated in healthcare, the number of publications of quality studies remains low. Little is known about the frequency and characteristics of citation in qualitative studies.

Objective

To compare the academic impact of qualitative studies to that of two quantitative studies: systematic reviews and randomized controlled trials.

Methods

Publications in BMJ between 1997 and 2006 (BMJ’s median impact factor was 7.04 during this period) employing qualitative methods were matched to two quantitative studies appearing the same year using PubMed. Using Web of Science, citations within a 24-month publication period were determined. Additionally, three hypotheses were examined: qualitative studies are 1) infrequently cited in original articles or reviews; 2) rarely cited by authors in non-English-speaking countries; and 3) more frequently cited in non-medical disciplines (e.g., psychology or sociology).

Results

A total of 121 qualitative studies, 270 systematic reviews, and 515 randomised controlled trials were retrieved. Qualitative studies were cited a total of 1,089 times, with a median of 7.00 times (range, 0–34) for each study. Matched systematic reviews and randomized controlled trials were cited 2,411times and 1,600 times, respectively. With respect to citing documents, original articles and reviews exceeded 60% for each study design. Relative to quantitative studies, qualitative studies were cited more often by authors in English-speaking countries. With respect to subject area, medical disciplines were more frequently cited than non-medical disciplines for all three study designs (>80%).

Conclusion

The median number of citations for qualitative studies was almost the same as the median of BMJ’s impact factor during the survey period. For a suitable evaluation of qualitative studies in healthcare, it will be necessary to develop a reporting framework and include explicit discussions of clinical implications when reporting findings. Coordination between researchers and editors will be needed to achieve this goal.     

Validation of Type 2 Diabetes Risk Variants Identified by Genome-Wide Association Studies in Han Chinese Population: A Replication Study and Meta-Analysis

Background

Several genome-wide association studies (GWAS) involving European populations have successfully identified risk genetic variants associated with type 2 diabetes mellitus (T2DM). However, the effects conferred by these variants in Han Chinese population have not yet been fully elucidated.

Methods

We analyzed the effects of 24 risk genetic variants with reported associations from European GWAS in 3,040 Han Chinese subjects in Taiwan (including 1,520 T2DM cases and 1,520 controls). The discriminative power of the prediction models with and without genotype scores was compared. We further meta-analyzed the association of these variants with T2DM by pooling all candidate-gene association studies conducted in Han Chinese.

Results

Five risk variants in IGF2BP2 (rs4402960, rs1470579), CDKAL1 (rs10946398), SLC30A8 (rs13266634), and HHEX (rs1111875) genes were nominally associated with T2DM in our samples. The odds ratio was 2.22 (95% confidence interval, 1.81-2.73, P<0.0001) for subjects with the highest genetic score quartile (score>34) as compared with subjects with the lowest quartile (score<29). The incoporation of genotype score into the predictive model increased the C-statistics from 0.627 to 0.657 (P<0.0001). These estimates are very close to those observed in European populations. Gene-environment interaction analysis showed a significant interaction between rs13266634 in SLC30A8 gene and age on T2DM risk (P<0.0001). Further meta-analysis pooling 20 studies in Han Chinese confirmed the association of 10 genetic variants in IGF2BP2, CDKAL1, JAZF1, SCL30A8, HHEX, TCF7L2, EXT2, and FTO genes with T2DM. The effect sizes conferred by these risk variants in Han Chinese were similar to those observed in Europeans but the allele frequencies differ substantially between two populations.

Conclusion

We confirmed the association of 10 variants identified by European GWAS with T2DM in Han Chinese population. The incorporation of genotype scores into the prediction model led to a small but significant improvement in T2DM prediction.