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      A cautionary note on using secondary phenotypes in neuroimaging genetic studies

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      NeuroImage
      Elsevier BV

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          Abstract

          <p class="first" id="P1">Almost all genome-wide association studies (GWASs), including Alzheimer’s Disease Neuroimaging Initiative (ADNI), are based on the case-control study design, implying that the resulting case-control data are likely a biased, not random, sample of the target population. Although association analysis of the disease (e.g. Alzheimer’s disease in the ADNI) can be conducted using a standard logistic regression by ignoring the biased case-control sampling, a standard linear regression analysis on a secondary phenotype (e.g. any neuroimaging phenotype in the ADNI) may in general lead to biased inference, including biased parameter estimates, inflated Type I errors and reduced power for association testing. Despite of this well known result in genetic epidemiology, to our surprise, all the published studies on secondary phenotypes with the ADNI data have ignored this potential problem. Here we aim to answer whether such a standard analysis of a secondary phenotype is valid or problematic with the ADNI data. Through both real data analyses and simulation studies, we found that, strikingly, such an analysis was generally valid (with only small biases or slightly inflated Type I errors) for the ADNI data, though cautions must be taken when analyzing other data. We also illustrate applications and possible problems of two methods specifically developed for valid analysis of secondary phenotypes. </p>

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          Most cited references30

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          A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.

          We conducted a genome-wide association study (GWAS) of breast cancer by genotyping 528,173 SNPs in 1,145 postmenopausal women of European ancestry with invasive breast cancer and 1,142 controls. We identified four SNPs in intron 2 of FGFR2 (which encodes a receptor tyrosine kinase and is amplified or overexpressed in some breast cancers) that were highly associated with breast cancer and confirmed this association in 1,776 affected individuals and 2,072 controls from three additional studies. Across the four studies, the association with all four SNPs was highly statistically significant (P(trend) for the most strongly associated SNP (rs1219648) = 1.1 x 10(-10); population attributable risk = 16%). Four SNPs at other loci most strongly associated with breast cancer in the initial GWAS were not associated in the replication studies. Our summary results from the GWAS are available online in a form that should speed the identification of additional risk loci.
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            Alzheimer disease in the US population: prevalence estimates using the 2000 census.

            Current and future estimates of Alzheimer disease (AD) are essential for public health planning. To provide prevalence estimates of AD for the US population from 2000 through 2050. Alzheimer disease incidence estimates from a population-based, biracial, urban study, using a stratified random sampling design, were converted to prevalence estimates and applied to US Census Bureau estimates of US population growth. A geographically defined community of 3 adjacent neighborhoods in Chicago, Ill, applied to the US population. Alzheimer disease incidence was measured in 3838 persons free of AD at baseline; 835 persons were evaluated for disease incidence. Main Outcome Measure Current and future estimates of prevalence of clinically diagnosed AD in the US population. In 2000, there were 4.5 million persons with AD in the US population. By 2050, this number will increase by almost 3-fold, to 13.2 million. Owing to the rapid growth of the oldest age groups of the US population, the number who are 85 years and older will more than quadruple to 8.0 million. The number who are 75 to 84 years old will double to 4.8 million, while the number who are 65 to 74 years old will remain fairly constant at 0.3 to 0.5 million. The number of persons with AD in the US population will continue to increase unless new discoveries facilitate prevention of the disease.
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              The Mayo Clinic Study of Aging: Design and Sampling, Participation, Baseline Measures and Sample Characteristics

              Background: The objective of this study was to establish a prospective population-based cohort to investigate the prevalence, incidence and risk factors for mild cognitive impairment (MCI) and dementia. Methods: The Olmsted County, Minn., population, aged 70–89 years on October 1, 2004, was enumerated using the Rochester Epidemiology Project. Eligible subjects were randomly selected and invited to participate. Participants underwent a comprehensive in-person evaluation including the Clinical Dementia Rating Scale, a neurological evaluation and neuropsychological testing. A consensus diagnosis of normal cognition, MCI or dementia was made by a panel using previously published criteria. A subsample of subjects was studied via telephone interview. Results: Four hundred and two subjects with dementia were identified from a detailed review of their medical records but were not contacted. At baseline, we successfully evaluated 703 women aged 70–79 years, 769 women aged 80–89 years, 730 men aged 70–79 years and 517 men aged 80–89 years (total n = 2,719). Among the participants, 2,050 subjects were evaluated in person and 669 via telephone. Conclusions: Strengths of the study are that the subjects were randomly selected from a defined population, the majority of the subjects were examined in person, and MCI was defined using published criteria. Here, we report the design and sampling, participation, baseline measures and sample characteristics.
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                Author and article information

                Journal
                NeuroImage
                NeuroImage
                Elsevier BV
                10538119
                November 2015
                November 2015
                : 121
                :
                : 136-145
                Article
                10.1016/j.neuroimage.2015.07.058
                4604049
                26220747
                e0b07f4b-67da-482e-8cfa-284229de56ea
                © 2015
                History

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