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      Confounding Underlies the Apparent Month of Birth Effect in Multiple Sclerosis

      research-article
      , MRCP 1 , , PhD 2 , , PhD 1 , , PhD 1 , , FRCP, PhD 1 , , FRCP, PhD 1
      Annals of Neurology
      Blackwell Publishing Ltd

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          Abstract

          Objective

          Several groups have reported apparent association between month of birth and multiple sclerosis. We sought to test the extent to which such studies might be confounded by extraneous variables such as year and place of birth.

          Methods

          Using national birth statistics from 2 continents, we assessed the evidence for seasonal variations in birth rate and tested the extent to which these are subject to regional and temporal variation. We then established the age and regional origin distribution for a typical multiple sclerosis case collection and determined the false-positive rate expected when comparing such a collection with birth rates estimated by averaging population-specific national statistics.

          Results

          We confirm that seasonality in birth rate is ubiquitous and subject to highly significant regional and temporal variations. In the context of this variation we show that birth rates observed in typical case collections are highly likely to deviate significantly from those obtained by the simple unweighted averaging of national statistics. The significant correlations between birth rates and both place (latitude) and time (year of birth) that characterize the general population indicate that the apparent seasonal patterns for month of birth suggested to be specific for multiple sclerosis (increased in the spring and reduced in the winter) are expected by chance alone.

          Interpretation

          In the absence of adequate control for confounding factors, such as year and place of birth, our analyses indicate that the previous claims for association of multiple sclerosis with month of birth are probably false positives. ANN NEUROL 2013;73:714–720

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          Most cited references24

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          An Introduction to Categorical Data Analysis

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            Population stratification and spurious allelic association.

            Great efforts and expense have been expended in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, technology for detection and scoring of single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a means for investigation of the genetic causes of complex human diseases. For many diseases, population-based studies of unrelated individuals--in which case-control and cohort studies serve as standard designs for genetic association analysis--can be the most practical and powerful approach. However, extensive debate has arisen about optimum study design, and considerable concern has been expressed that these approaches are prone to population stratification, which can lead to biased or spurious results. Over the past decade, a great shift has been noted, away from case-control and cohort studies, towards family-based association designs. These designs have fewer problems with population stratification but have greater genotyping and sampling requirements, and data can be difficult or impossible to gather. We discuss past evidence for population stratification on genotype-phenotype association studies, review methods to detect and account for it, and present suggestions for future study design and analysis.
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              Association study designs for complex diseases.

              Assessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to skepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.
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                Author and article information

                Journal
                Ann Neurol
                Ann. Neurol
                ana
                Annals of Neurology
                Blackwell Publishing Ltd
                0364-5134
                1531-8249
                June 2013
                02 July 2013
                : 73
                : 6
                : 714-720
                Affiliations
                [1 ]University of Cambridge, Department of Clinical Neurosciences, Addenbrooke's Hospital Cambridge, United Kingdom
                [2 ]Medical Research Council Biostatistics Unit Cambridge, United Kingdom
                Author notes
                Address correspondence to Dr Sawcer, University of Cambridge, Department of Clinical Neuroscience, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, United Kingdom. E-mail: sjs1016@ 123456mole.bio.cam.ac.uk
                Article
                10.1002/ana.23925
                3748787
                23744589
                ba6f6be0-a31a-4095-b5c2-e1e376f84068
                © 2013 American Neurological Association

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

                History
                : 29 January 2013
                : 23 April 2013
                : 26 April 2013
                Categories
                Original Articles

                Neurology
                Neurology

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