Differentiating population stratification from genotyping error using family data.

Identifying population stratification and genotyping error are important for candidate gene association studies using the Transmission Disequilibrium Test (TDT). Although the TDT retains the prespecified Type I error in the presence of population stratification, the test may have decreased power in the presence of population stratification. Genotyping error can also cause the TDT to have an elevated Type I error. Differentiating population stratification from genotyping error remains a challenge for geneticists. Both genotyping error and population stratification can result in an increase in the observed homozygosity of a sample relative to that expected assuming Hardy-Weinberg Equilibrium (HWE). We show that when family data are available, even if a limited number of markers are genotyped, evaluating the markers that show statistically significant deviation from HWE with the Mating Type Distortion Test (MTDT)--a test based on the mating type distribution--can reliably differentiate genotyping error from population stratification. We simulate data based on several models of genotyping error in previously published literature, and show how this method could be used in practice to assist in differentiating population stratification from systematic genotyping error.

Testing for non-random mating: evidence for ancestry-related assortative mating in the Framingham heart study.

Population stratification leads to a predictable phenomenon-a reduction in the number of heterozygotes compared to that calculated assuming Hardy-Weinberg Equilibrium (HWE). We show that population stratification results in another phenomenon-an excess in the proportion of spouse-pairs with the same genotypes at all ancestrally informative markers, resulting in ancestrally related positive assortative mating. We use principal components analysis to show that there is evidence of population stratification within the Framingham Heart Study, and show that the first principal component correlates with a North-South European cline. We then show that the first principal component is highly correlated between spouses (r = 0.58, p = 0.0013), demonstrating that there is ancestrally related positive assortative mating among the Framingham Caucasian population. We also show that the single nucleotide polymorphisms loading most heavily on the first principal component show an excess of homozygotes within the spouses, consistent with similar ancestry-related assortative mating in the previous generation. This nonrandom mating likely affects genetic structure seen more generally in the North American population of European descent today, and decreases the rate of decay of linkage disequilibrium for ancestrally informative markers.

The power of the Transmission Disequilibrium Test in the presence of population stratification.

The Transmission Disequilibrium Test (TDT) is a family-based test for association based on the rate of transmission of alleles from heterozygous parents to affected offspring, and has gained popularity as this test preserves the Type I error rate. Population stratification results in a decreased number of heterozygous parents compared to that expected assuming Hardy-Weinberg Equilibrium (Wahlund Effect). We show that population stratification changes the relative proportion of the informative mating types. The decrease in the number of heterozygous parents and the change in the relative proportion of the informative mating types result in significant changes to the sample sizes required to achieve the power desired. We show examples of the changes in sample sizes, and provide an easy method for estimating TDT sample sizes in the presence of population stratification. This method potentially aids in reducing the number of false-negative association studies.

Genome-wide association scan for diabetic nephropathy susceptibility genes in type 1 diabetes.

OBJECTIVE: Despite extensive evidence for genetic susceptibility to diabetic nephropathy, the identification of susceptibility genes and their variants has had limited success. To search for genes that contribute to diabetic nephropathy, a genome-wide association scan was implemented on the Genetics of Kidneys in Diabetes collection. RESEARCH DESIGN AND METHODS: We genotyped approximately 360,000 single nucleotide polymorphisms (SNPs) in 820 case subjects (284 with proteinuria and 536 with end-stage renal disease) and 885 control subjects with type 1 diabetes. Confirmation of implicated SNPs was sought in 1,304 participants of the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, a long-term, prospective investigation of the development of diabetes-associated complications. RESULTS: A total of 13 SNPs located in four genomic loci were associated with diabetic nephropathy with P < 1 x 10(-5). The strongest association was at the FRMD3 (4.1 protein ezrin, radixin, moesin [FERM] domain containing 3) locus (odds ratio [OR] = 1.45, P = 5.0 x 10(-7)). A strong association was also identified at the CARS (cysteinyl-tRNA synthetase) locus (OR = 1.36, P = 3.1 x 10(-6)). Associations between both loci and time to onset of diabetic nephropathy were supported in the DCCT/EDIC study (hazard ratio [HR] = 1.33, P = 0.02, and HR = 1.32, P = 0.01, respectively). We demonstratedexpression of both FRMD3 and CARS in human kidney. CONCLUSIONS: We identified genetic associations for susceptibility to diabetic nephropathy at two novel candidate loci near the FRMD3 and CARS genes. Their identification implicates previously unsuspected pathways in the pathogenesis of this important late complication of type 1 diabetes.

High-density single nucleotide polymorphism genome-wide linkage scan for susceptibility genes for diabetic nephropathy in type 1 diabetes: discordant sibpair approach.

OBJECTIVE: Epidemiological and family studies have demonstrated that susceptibility genes play an important role in the etiology of diabetic nephropathy, defined as persistent proteinuria or end-stage renal disease (ESRD) in type 1 diabetes. RESEARCH DESIGN AND METHODS: To efficiently search for genomic regions harboring diabetic nephropathy genes, we conducted a scan using 5,382 informative single nucleotide polymorphisms on 100 sibpairs concordant for type 1 diabetes but discordant for diabetic nephropathy. In addition to being powerful for detecting linkage to diabetic nephropathy, this design allows linkage analysis on type 1 diabetes via traditional affected sibpair (ASP) analysis. In weighing the evidence for linkage, we considered maximum logarithm of odds score (maximum likelihood score [MLS]) values and corresponding allelic sharing patterns, calculated and viewed graphically using the software package SPLAT. RESULTS: Our primary finding for diabetic nephropathy, broadly defined, is on chromosome 19q (MLS = 3.1), and a secondary peak exists on chromosome 2q (MLS = 2.1). Stratification of discordant sibpairs based on whether disease had progressed to ESRD suggested four tertiary peaks on chromosome 1q (ESRD only), chromosome 20p (proteinuria only), and chromosome 3q (two loci 58 cm apart, one for ESRD only and another for proteinuria only). Additionally, analysis of 130 ASPs for type 1 diabetes confirmed the linkage to the HLA region on chromosome 6p (MLS = 9.2) and IDDM15 on chromosome 6q (MLS = 3.1). CONCLUSIONS: This study identified several novel loci as candidates for diabetic nephropathy, none of which appear to be the sole genetic determinant of diabetic nephropathy in type 1 diabetes. In addition, this study confirms two previously reported type 1 diabetes loci.

A genome-wide linkage scan for genes controlling variation in renal function estimated by serum cystatin C levels in extended families with type 2 diabetes.

We performed a variance components linkage analysis of renal function, measured as glomerular filtration rate (GFR), in 63 extended families with multiple members with type 2 diabetes. GFR was estimated from serum concentrations of cystatin C and creatinine in 406 diabetic and 428 nondiabetic relatives. Results for cystatin C were summarized because they are superior to creatinine results. GFR aggregates in families with significant heritability (h(2)) in diabetic (h(2) = 0.45, P < 1 x 10(-5)) and nondiabetic (h(2) = 0.36, P < 1 x 10(-3)) relatives. Genetic correlation (r(G) = 0.35) between the GFR of diabetic and nondiabetic relatives was less than one (P = 0.01), suggesting that genes controlling GFR variation in these groups are different. Linkage results supported this interpretation. In diabetic relatives, linkage was strong on chromosome 2q (logarithm of odds [LOD] = 4.1) and suggestive on 10q (LOD = 3.1) and 18p (LOD = 2.2). In nondiabetic relatives, linkage was suggestive on 3q (LOD = 2.2) and 11p (LOD = 2.1). When diabetic and nondiabetic relatives were combined, strong evidence for linkage was found only on 7p (LOD = 4.0). In conclusion, partially distinct sets of genes control GFR variation in relatives with and without diabetes on chromosome 2q, possibly on 10q and 18p in the former, and on 7p in both. None of these genes overlaps with genes controlling variation in urinary albumin excretion.

Genetics of Kidneys in Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes.

The Genetics of Kidneys in Diabetes (GoKinD) study is an initiative that aims to identify genes that are involved in diabetic nephropathy. A large number of individuals with type 1 diabetes were screened to identify two subsets, one with clear-cut kidney disease and another with normal renal status despite long-term diabetes. Those who met additional entry criteria and consented to participate were enrolled. When possible, both parents also were enrolled to form family trios. As of November 2005, GoKinD included 3075 participants who comprise 671 case singletons, 623 control singletons, 272 case trios, and 323 control trios. Interested investigators may request the DNA collection and corresponding clinical data for GoKinD participants using the instructions and application form that are available at http://www.gokind.org/access. Participating scientists will have access to three data sets, each with distinct advantages. The set of 1294 singletons has adequate power to detect a wide range of genetic effects, even those of modest size. The set of case trios, which has adequate power to detect effects of moderate size, is not susceptible to false-positive results because of population substructure. The set of control trios is critical for excluding certain false-positive results that can occur in case trios and may be particularly useful for testing gene-environment interactions. Integration of the evidence from these three components into a single, unified analysis presents a challenge. This overview of the GoKinD study examines in detail the power of each study component and discusses analytic challenges that investigators will face in using this resource.

A novel framework for sib pair linkage analysis.

Sib pair linkage analysis of a dichotomous trait is a popular method for narrowing the search for genes that influence complex diseases. Although the pedigree structures are uncomplicated and the underlying genetic principles straightforward, a surprising degree of complexity is involved in implementing a sib pair study and interpreting the results. Ascertainment may be based on affected, discordant, or unaffected sib pairs, as well as on pairs defined by threshold values for quantitative traits, such as extreme discordant sib pairs. To optimize power, various domain restrictions and null hypotheses have been proposed for each of these designs, yielding a wide array of choices for the analyst. To begin, we systematically classify the major sources of discretion in sib pair linkage analysis. Then, we extend the work of Kruglyak and Lander (1995), to bring the various forms into a unified framework and to facilitate a more general approach to the analysis. Finally, we describe a new, freely available computer program, Splat (Sib Pair Linkage Analysis Testing), that can perform any sib pair statistical test currently in use, as well as any user-defined test yet to be proposed. Splat uses the expectation maximization algorithm to calculate maximum-likelihood estimates of sharing (subject to user-specified conditions) and then plots LOD scores versus chromosomal position. It includes a novel grid-scanning capability that enables simultaneous visualization of multiple test statistics. This can lead to further insight into the genetic basis of the disease process under consideration. In addition, phenotype definitions can be modified without the recalculation of inheritance vectors, thereby providing considerable flexibility for exploratory analysis. The application of Splat will be illustrated with data from studies on the genetics of diabetic nephropathy.

Identification of a common risk haplotype for diabetic nephropathy at the protein kinase C-beta1 (PRKCB1) gene locus.

Abnormal activation of protein kinase C-beta isoforms in the diabetic state has been implicated in the development of diabetic nephropathy. It is thus plausible that DNA sequence differences in the protein kinase C-beta1 gene (PRKCB1), which encodes both betaI and betaII isoforms, may influence susceptibility to nephropathy. Nine single-nucleotide polymorphisms (SNP) in PRKCB1 were tested for association with diabetic nephropathy in type I diabetes mellitus, by using both case-control and family-study designs. Allele and genotype distributions of two SNP in the promoter (--1504C/T and --546C/G) differed significantly between case patients and control patients (P < 0.05). These associations were particularly strong with diabetes mellitus duration of <24 yr (P = 0.002). The risk of diabetic nephropathy was higher among carriers of the T allele of the --1504C/T SNP, compared with noncarriers (odds ratio, 2.54; 95% confidence interval, 1.39 to 4.62), and among carriers of the G allele of the --546C/G SNP (odds ratio, 2.45; 95% confidence interval, 1.37 to 4.38). Among individuals with diabetes mellitus duration of >/==" BORDER="0">24 yr, these two SNP were not associated with diabetic nephropathy. These positive findings were confirmed by using the family-based transmission disequilibrium test. The T-G haplotype, with both risk alleles, was transmitted more frequently than expected from heterozygous parents to offspring who developed diabetic nephropathy during the first 24 yr of diabetes mellitus. It is concluded that DNA sequence differences in the promoter of PRKCB1 contribute to diabetic nephropathy susceptibility in type I diabetes mellitus.

Genetic studies of late diabetic complications: the overlooked importance of diabetes duration before complication onset.

Genes play a role in many processes underlying late diabetic complications, but efforts to identify genetic variants have produced disappointing and contradictory results. Here, we evaluate whether the study designs and analytic methods commonly being used are optimal for finding susceptibility genes for diabetic complications. We do so by generating plausible genetic models and assessing the performance of case-control and family-based trio study designs. What emerges as a key determinant of success is duration of diabetes. This perspective focuses on duration of diabetes before complication onset and its influence on the ability to detect major and minor gene effects. It does not delve into the distinct effect of duration after complication onset, which can enrich case subjects with genotypes conferring survival advantage. We use clinically diagnosed nephropathy in type 1 diabetes to show how ignoring duration can result in considerable power loss in both case-control and family-based trio designs. We further show how, under certain circumstances, disregard for duration information can paradoxically lead to implicating nonrisk alleles as causative. Our results indicate that problems can be minimized by selecting case subjects with short diabetes duration and, to a lesser extent, control subjects with long duration or, perhaps, by adjusting for duration during analysis.

Polymorphism in ecto-nucleotide pyrophosphatase/phosphodiesterase 1 gene (ENPP1/PC-1) and early development of advanced diabetic nephropathy in type 1 diabetes.

A polymorphism in the ecto-nucleotide pyrophosphatase/phosphodiesterase 1 gene (ENPP1) (previously known as PC-1), resulting in an amino acid change from lysine to glutamine at codon 121 (K121Q), is associated with insulin resistance. A small follow-up study of patients with type 1 diabetes and proteinuria found that renal function declines more rapidly in carriers of the Q variant than in noncarriers. To examine this finding further, we conducted a large case-control study and a family-based study. Genomic DNA was obtained from 659 patients: 307 with normal urinary albumin excretion despite diabetes duration of >15 years (control subjects) and 352 with advanced diabetic nephropathy, of whom 200 had persistent proteinuria and 152 had end-stage renal disease (ESRD). Individuals were genotyped for Q and K variants using a previously described protocol. The frequency of Q variant carriers was 21.5% in control subjects, 31.5% in subjects with proteinuria, and 32.2% in subjects with ESRD (P = 0.012). In a stratified analysis according to duration of diabetes, the risk of early-onset ESRD for carriers of the Q variant was 2.3 times that for noncarriers (95% CI, 1.2-4.6). The Q variant was not associated with late-onset ESRD. Similar findings were obtained in a family-based study. We conclude that carriers of the Q variant of ENPP1 are at increased risk for developing ESRD early in the course of type 1 diabetes.

A method for developing high-density SNP maps and its application at the type 1 angiotensin II receptor (AGTR1) locus.

Evaluating the potential genetic components of complex disease will likely be aided through the use of dense polymorphism maps. Previously, we reported evidence for linkage with diabetic nephropathy on chromosome 3q in a region encompassing the type 1 angiotensin II receptor (AGTR1) gene. To further investigate any role for this gene in disease onset, we set out to design a dense polymorphism map spanning the AGTR1 locus for the purpose of association studies. Toward this goal, we have developed a technique for rapid identification of polymorphisms in long stretches of genomic DNA. This approach uses long-range PCR, DNA pooling, and transposon-based DNA sequencing. Using this technique, we efficiently validated and genotyped 18 polymorphisms spanning the 60.5-kb AGTR1 locus. Our panel of polymorphisms has an average spacing of 3.2 kb and an average minor allele frequency of 24%.