Genome-wide linkage and association study implicates the 10q26 region as a major genetic contributor to primary nonsyndromic vesicoureteric reflux

Abstract Vesicoureteric reflux (VUR) is the commonest urological anomaly in children. Despite treatment improvements, associated renal lesions – congenital dysplasia, acquired scarring or both – are a common cause of childhood hypertension and renal failure. Primary VUR is familial, with transmission rate and sibling risk both approaching 50%, and appears highly genetically heterogeneous. It is often associated with other developmental anomalies of the urinary tract, emphasising its etiology as a disorder of urogenital tract development. We conducted a genome-wide linkage and association study in three European populations to search for loci predisposing to VUR. Family-based association analysis of 1098 parent-affected-child trios and case/control association analysis of 1147 cases and 3789 controls did not reveal any compelling associations, but parametric linkage analysis of 460 families (1062 affected individuals) under a dominant model identified a single region, on 10q26, that showed strong linkage (HLOD = 4.90; ZLRLOD = 4.39) to VUR. The ~9Mb region contains 69 genes, including some good biological candidates. Resequencing this region in selected individuals did not clearly implicate any gene but FOXI2, FANK1 and GLRX3 remain candidates for further investigation. This, the largest genetic study of VUR to date, highlights the 10q26 region as a major genetic contributor to VUR in European populations

Comparison of methods to account for relatedness in genome-wide association studies with family-based data.

Approaches based on linear mixed models (LMMs) have recently gained popularity for modelling population substructure and relatedness in genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. Given their strong concordance, in most applications, the choice of precise LMM implementation cannot be based on power/type I error considerations but must instead be based on considerations such as speed and ease-of-use

Summary of methods/software packages investigated.

<p>Summary of methods/software packages investigated.</p

Genomic control factors obtained using different software packages and different strategies for modelling kinships.

<p>PLINK =  analysis in PLINK with no adjustment made for relatedness. Other methods/software packages are listed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004445#pgen-1004445-t001" target="_blank">Table 1</a> (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004445#pgen-1004445-t002" target="_blank">Table 2</a> for abbreviated names of methods). Pedigree  =  theoretical kinships based on known pedigree relationships used to adjust for relatedness. Thinned  =  kinships based on 1900 ‘thinned’ SNPs used to adjust for relatedness. Pruned  =  kinships based on 50,129 ‘pruned’ SNPs used to adjust for relatedness. Full  =  kinships based on 545,433 SNPs used to adjust for relatedness.</p

Genomic control factors achieved in naive analysis of a single replicate of the simulated longitudinal data sets.

a<p>See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004445#pgen-1004445-t002" target="_blank">Table 2</a> for description of methods.</p