PURPOSEAfrican Americans who shed JC polyomavirus (JCV) in their urine have reduced rates of nondiabetic chronic kidney disease (CKD). We assessed the associations between urinary JCV and urine BK polyomavirus (BKV) with CKD in African Americans with diabetes mellitus.METHODSAfrican Americans with diabetic kidney disease (DKD) and controls lacking nephropathy from the Family Investigation of Nephropathy and Diabetes Consortium (FIND) and African American-Diabetes Heart Study (AA-DHS) had urine tested for JCV and BKV using quantitative PCR. Of the 335 individuals tested, 148 had DKD and 187 were controls.RESULTSJCV viruria was detected more often in the controls than in the patients with DKD (FIND: 46.6% vs 32.2%; OR, 0.52; 95% CI, 0.29 to 0.93; P = 0.03; AA-DHS: 30.4% vs 26.2%; OR, 0.63; 95% CI, 0.27 to 1.48; P = 0.29). A joint analysis adjusted for age, sex, and study revealed that JC viruria was inversely associated with DKD (OR, 0.56; 95% CI, 0.35 to 0.91; P = 0.02). Statistically significant relationships between BKV and DKD were not observed.MAIN CONCLUSIONSThe results from the present study extend the inverse association between urine JCV and nondiabetic nephropathy in African Americans to DKD. These results imply that common pathways likely involving the innate immune system mediate coincident chronic kidney injury and restriction of JCV replication. Future studies are needed to explore causative pathways and characterize whether the absence of JC viruria can serve as a biomarker for DKD in the African American population.

Linkage Analysis, Model Free† Jane M. Olson, Jane M. OlsonSearch for more papers by this author Jane M. Olson, Jane M. OlsonSearch for more papers by this author First published: 29 September 2014 https://doi.org/10.1002/9781118445112.stat05407 †This article was originally published online in 2005 in Encyclopedia of Biostatistics, © John Wiley & Sons, Ltd and republished in Wiley StatsRef: Statistics Reference Online, 2014. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Wiley StatsRef: Statistics Reference OnlineBrowse other articles of this reference work:BROWSE BY TOPICBROWSE A-Z RelatedInformation

AbstractThe term genetic heterogeneity refers to the underlying diversity in genetic architecture of different individuals in the population with a common phenotype. Two types of genetic heterogeneity are commonly distinguished: locus heterogeneity in which variants at different loci can predispose to the same phenotype, and allelic heterogeneity in which different variants at the same loci can predispose to the same phenotype. Although allelic heterogeneity is certainly of interest, once a susceptibility locus is identified, the presence of locus heterogeneity so dramatically reduces the power of gene mapping for complex traits that it remains a major statistical challenge. In this article, we primarily discuss models of linkage analysis that allow for locus heterogeneity, including more recent models that allow for heterogeneity‐related covariates.

OBJECTIVE:Familial aggregation of fibromyalgia has been increasingly recognized. The goal of this study was to conduct a genome-wide linkage scan to identify susceptibility loci for fibromyalgia.METHODS:We genotyped members of 116 families from the Fibromyalgia Family Study and performed a model-free genome-wide linkage analysis of fibromyalgia with 341 microsatellite markers, using the Haseman-Elston regression approach.RESULTS:The estimated sibling recurrence risk ratio (λs ) for fibromyalgia was 13.6 (95% confidence interval 10.0-18.5), based on a reported population prevalence of 2%. Genome-wide suggestive evidence of linkage was observed at markers D17S2196 (empirical P [Pe ]=0.00030) and D17S1294 (Pe=0.00035) on chromosome 17p11.2-q11.2.CONCLUSION:The estimated sibling recurrence risk ratio (λs ) observed in this study suggests a strong genetic component of fibromyalgia. This is the first report of genome-wide suggestive linkage of fibromyalgia to the chromosome 17p11.2-q11.2 region. Further investigation of these multicase families from the Fibromyalgia Family Study is warranted to identify potential causal risk variants for fibromyalgia.

Genetic association studies are becoming increasingly frequent in the obstetric and gynecologic literature and they are considered central to the deciphering of the genetic basis of complex disease. The purpose, design, execution, analysis, and interpretation of genetic association studies in reproduction are discussed. Frequently used terms are defined (eg, genotype, haplotype, polymorphism, single nucleotide polymorphism, linkage disequilibrium). Guidelines are proposed for the evaluation of reports of genetic association studies (including selection of polymorphisms for study, study design, assay characteristics, sample size, multiple testing, and multivariable analysis). The potential value of this type of investigation in elucidating the mechanisms of disease in reproduction is illustrated. (Am J Obstet Gynecol 2002;187:1299-312.)

Objective: p Values are inaccurate for model-free linkage analysis using the conditional logistic model if we assume that the LOD score is asymptotically distributed as a simple mixture of chi-square distributions. When analyzing affected relative pairs alone, permuting the allele sharing of relative pairs does not lead to a useful permutation distribution. As an alternative, we have developed regression prediction models that provide more accurate p values. Methods: Let Eα be the empirical p value, which is the proportion of statistical tests whose LOD score under the null hypothesis exceeds a threshold determined by α, the nominal single test significance value. We used simulated data to obtain values of Eα and compared them with α. We also developed a regression model, based on sample size, number of covariates in the model, α and marker density, to derive predicted p values for both single-point and multipoint analyses. To evaluate our predictions we used another set of simulated data, comparing the Eα for these data with those obtained by using the prediction model, referred to as predicted p values (Pα). Results: Under almost all circumstances the values of Pα were closer to the Eα than were the values of α. Conclusion: The regression models suggested by our analysis provide more accurate alternative p values for model-free linkage analysis when using the conditional logistic model.

Pre-eclampsia (PE) affects 5–7% of pregnancies in the US, and is a leading cause of maternal death and perinatal morbidity and mortality worldwide. To identify genes with a role in PE, we conducted a large-scale association study evaluating 775 SNPs in 190 candidate genes selected for a potential role in obstetrical complications. SNP discovery was performed by DNA sequencing, and genotyping was carried out in a high-throughput facility using the MassARRAYTM System. Women with PE (n = 394) and their offspring (n = 324) were compared with control women (n = 602) and their offspring (n = 631) from the same hospital-based population. Haplotypes were estimated for each gene using the EM algorithm, and empirical p values were obtained for a logistic regression-based score test, adjusted for significant covariates. An interaction model between maternal and offspring genotypes was also evaluated. The most significant findings for association with PE were COL1A1 (p = 0.0011) and IL1A (p = 0.0014) for the maternal genotype, and PLAUR (p = 0.0008) for the offspring genotype. Common candidate genes for PE, including MTHFR and NOS3, were not significantly associated with PE. For the interaction model, SNPs within IGF1 (p = 0.0035) and IL4R (p = 0.0036) gave the most significant results. This study is one of the most comprehensive genetic association studies of PE to date, including an evaluation of offspring genotypes that have rarely been considered in previous studies. Although we did not identify statistically significant evidence of association for any of the candidate loci evaluated here after adjusting for multiple testing using the false discovery rate, additional compelling evidence exists, including multiple SNPs with nominally significant p values in COL1A1 and the IL1A region, and previous reports of association for IL1A, to support continued interest in these genes as candidates for PE. Identification of the genetic regulators of PE may have broader implications, since women with PE are at increased risk of death from cardiovascular diseases later in life.

Juvenile rheumatoid arthritis (JRA) comprises a group of chronic systemic inflammatory disorders that primarily affect joints and can cause long-term disability. JRA is likely to be a complex genetic trait, or a series of such traits, with both genetic and environmental factors contributing to the risk for developing the disease and to its progression. The HLA region on the short arm of chromosome 6 has been intensively evaluated for genetic contributors to JRA, and multiple associations, and more recently linkage, has been detected. Other genes involved in innate and acquired immunity also map to near the HLA cluster on 6p, and it is possible that variation within these genes also confers risk for developing JRA. We examined the TPSN gene, which encodes tapasin, an endoplasmic reticulum chaperone that is involved in antigen processing, to elucidate its involvement, if any, in JRA. We employed both a case-control approach and the transmission disequilibrium test, and found linkage and association between the TPSN allele (Arg260) and the systemic onset subtype of JRA. Two independent JRA cohorts were used, one recruited from the Rheumatology Clinic at Cincinnati Children's Hospital Medical Center (82 simplex families) and one collected by the British Paediatric Rheumatology Group in London, England (74 simplex families). The transmission disequilibrium test for these cohorts combined was statistically significant (chi2 = 4.2, one degree of freedom; P = 0.04). Linkage disequilibrium testing between the HLA alleles that are known to be associated with systemic onset JRA did not reveal linkage disequilibrium with the Arg260 allele, either in the Cincinnati systemic onset JRA cohort or in 113 Caucasian healthy individuals. These results suggest that there is a weak association between systemic onset JRA and the TPSN polymorphism, possibly due to linkage disequilibrium with an as yet unknown susceptibility allele in the centromeric part of chromosome 6.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder involving genetic and environmental factors. Previously, our group showed that SLE females with affected male relatives have higher prevalence of renal disease than SLE females with no affected male relatives in a sample of 372 individuals from 159 families. By adding 392 individuals from 181 new families, we replicated this finding in the largest collection of families with affected males, confirming our hypothesis that multiplex SLE families with at least one affected male member ("male families") comprise a distinct subpopulation of SLE multiplex families. We studied 64 male families by a genome-wide scan for SLE and found the largest signal (lod=3.08) at 13q32 in 18 African American male families using an affected-relative-pair model-free linkage method. Closer examination of IBD sharing at this region suggested a dominant mode of inheritance. Multipoint model-based linkage analysis generated a lod score of 3.13 in the same chromosomal region with a low-disease allele frequency of 0.0004 and a disease penetrance of 0.5 for the 18 African American male families. We performed fine mapping in these and three additional African American male families and the SLE predisposing locus was localized to a region tightly linked to the marker D13S892. We have therefore confirmed the linkage of SLE to 13q32, which was reported previously, and suggested that an SLE susceptibility gene in this region is specific to predisposition of African Americans to a specific form of SLE, with males at high risk.

Background— Abdominal aortic aneurysm (AAA) is a relatively common disease, with 1% to 2% of the population harboring aneurysms. Genetic risk factors are likely to contribute to the development of AAAs, although no such risk factors have been identified. Methods and Results— We performed a whole-genome scan of AAA using affected-relative-pair (ARP) linkage analysis that includes covariates to allow for genetic heterogeneity. We found strong evidence of linkage (logarithm of odds [LOD] score=4.64) to a region near marker D19S433 at 51.88 centimorgans (cM) on chromosome 19 with 36 families (75 ARPs) when including sex and the number of affected first-degree relatives of the proband (N aff ) as covariates. We then genotyped 83 additional families for the same markers and typed additional markers for all families and obtained a LOD score of 4.75 ( P =0.00014) with sex, N aff , and their interaction as covariates near marker D19S416 (58.69 cM). We also identified a region on chromosome 4 with a LOD score of 3.73 ( P =0.0012) near marker D4S1644 using the same covariate model as for chromosome 19. Conclusions— Our results provide evidence for genetic heterogeneity and the presence of susceptibility loci for AAA on chromosomes 19q13 and 4q31.

Recently, we reported evidence of linkage on chromosome 20 for Alzheimer disease (AD) using a novel statistical approach to incorporate covariates (e.g., age, ApoE genotype) into the analysis. These results suggest that very elderly subjects (>85 years), and individuals who carry an epsilon2 allele at the ApoE locus are more likely to be linked to this candidate region. The region on chromosome 20 includes a strong candidate gene, cystatin C (CST3), which has previously been associated with AD in case-control studies. We investigated these findings further by genotyping additional markers to narrow the candidate region, and to identify evidence of linkage disequilibrium as additional support for a susceptibility locus on chromosome 20. We selected 43 elderly sibships (89 subjects) from the NIMH AD Genetics Initiative based on current age older than 84 years, and identified 129 unrelated control subjects who were older than 84 years from the Oregon Brain Aging Study to conduct linkage and association studies in this region. Fourteen additional markers were evaluated, including 4 markers located within or near CST3. We narrowed the candidate region on chromosome 20 to an 11.8-cM region between markers D20S174 and D20S471, which includes the CST3 candidate gene. In addition, we observed evidence of association for markers located near the CST3 candidate gene, with P values between 0.002 and 0.08 for two-locus haplotypes. These results support the presence of a susceptibility locus for AD in the vicinity of CST3 for very elderly subjects with AD.

We recently reported a two-stage genomewide screen of 48 sib pairs affected with intracranial aneurysms (IAs) that revealed suggestive linkage to chromosome 19q13, with a LOD score of 2.58. The region supporting linkage spanned approximately 22 cM. Here, we report a follow-up study of the locus at 19q13, with a sample size expanded to 139 affected sib pairs, along with 83 other affected relative pairs (222 affected relative pairs in total). Suggestive linkage was observed in both independent sample sets, and linkage was significant in the combined set at 70 cM (LOD score 3.50; P=.00006) and at 80 cM (LOD score 3.93; P=.00002). Linkage was highly significant at 70 cM (LOD score 5.70; P=.000001) and at 80 cM (LOD score 3.99; P=.00005) when a covariate measuring the number of affected individuals in the nuclear family was included. To evaluate further the contribution to the linkage signal from families with more than two affected relatives, we performed model-based linkage analysis with a recessive model and a range of penetrances, and we obtained maximum linkage at 70 cM (LOD score 3.16; P=.00007) with a penetrance of 0.3. We then estimated location by using GENEFINDER. The most likely location for a gene predisposing to IAs in the Finnish population is in a region with a 95% confidence interval of 11.6 cM (P=.00007) centered 2.0 cM proximal to D19S246.

Data errors and marker allele frequency misspecification can lead to incorrect inference in linkage analysis. Here we demonstrate the effect of each on an allele-sharing statistic in a sample of sib pairs. In the context of relationship testing, we propose a new test that compares the sample genome-wide sib-pair allele sharing to its expectation and show that this test can detect the presence of large-scale data and model errors.

Regression methods offer a common framework to analyze linkage for quantitative trait loci as well as linkage for affection status using affected sib-pairs. Although numerous papers on regression methods for linkage have been published, some common themes and important caveats tend to be scattered across the literature. For example, the typical approach is to regress a function of traits on identical-by-descent (IBD) information, but the reversal (regression of IBD on a function of traits) offers important insights. A second example is the use of regression equations to assess linkage heterogeneity or gene-environment interaction, and why these two different etiologies are difficult to distinguish with affected sib-pair data. A third example has to do with the differences, and similarities, between linear regression and non-linear regression methods for affected sib-pair data. The purposes of this paper are to review some recent developments in the linkage regression framework, to emphasize strengths and weaknesses of various proposed methods, and to highlight some important assumptions and caveats.

Genetic Epidemiology 25 (Supplement 1): S1–S4 (2003) Genetic Analysis Workshop 13: Introduction to Workshop Summaries Laura Almasy, 1 n L. Adrienne Cupples, 2 E. Warwick Daw, 3 Daniel Levy, 4 Duncan Thomas, 5 John P. Rice, 6 Susan Santangelo, 7 and Jean W. MacCluer 1 Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts Department of Preventive Medicine, University of Southern California, Los Angeles, California Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts Grant sponsor: National Institutes of Health; Grant number: GM31575. Correspondence to: Laura Almasy, Department of Genetics, Southwest Foundation for Biomedical Research, P.O. Box 760549, San Antonio, TX 78245-0549. E-mail: almasy@darwin.sfbr.org Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/gepi.10278 n INTRODUCTION The Genetic Analysis Workshops (GAWs) began in 1982 as a collaborative effort among researchers of various disciplines to evaluate and compare statistical genetics methods. For each GAW, one or more topics are chosen that relate to current analytical and methodological issues in statistical genetics of complex phenotypes. For each work- shop, sets of simulated and real data are dis- tributed to researchers worldwide who submit the results of their analyses of these data for pre- sentation at GAW. The workshop itself is a 2 12 -day meeting comparing and contrasting the many different approaches used to analyze the data. New methods are introduced, old methods are evaluated in new contexts, and diverse analytical schemes are explored on the level playing field of a common data set. More information about GAW, including details of upcoming workshops, may be found at http://www.sfbr.org/external/gaw/ welcome.html. GAW13 was held November 11–14, 2002, in New Orleans, Louisiana. The 117 contributions sub- mitted to GAW13 were organized into 11 pre- sentation groups of 6–14 papers each. Within each group, a co-author with previous GAW experience was asked to serve as group leader to facilitate group discussion, organize an oral presentation for the group, and take the lead in writing the group summary papers collected in this volume. Eight presentation groups were organized around com- mon methodological themes: derived phenotypes, & 2003 Wiley-Liss, Inc. methods for longitudinal analysis, consistency of genetic analyses across time, missing data and pedigree or genotyping errors, effects of covari- ates, pleiotropy and multivariate analyses, data mining/neural networks/tree-based methods, and development and extension of linkage methods. The remaining three presentation groups con- tained papers united by a common focus on particular phenotypes: analysis of blood pressure and hypertension phenotypes, analysis of obesity/ diabetes/lipid phenotypes, and analysis of tobacco and alcohol phenotypes. Of the original 117 GAW13 contributions, 101 were published as a supplement to BMC Genetics [Almasy et al., 2003]. It is these 101 peer-reviewed, published papers that are summarized in the present volume. Summaries have long been a part of the GAW proceedings. For many years, a single individual was charged with the Herculean task of summar- izing all the GAW contributions, using a particular data set. As of GAW11, there were as many as 67 individual contributions to be summarized for a given data set, and the task was divided among pairs of individuals. With the increasing GAW participation, it also became increasingly difficult to accommodate individual oral presentation of each paper within the schedule of the workshop. For GAW12, a new format was introduced both for workshop presentations and for summary papers. Individual contributions with common themes were assigned to presentation groups that had a single oral presentation at the work- shop. In the GAW12 proceedings [Wijsman et al.,