Evaluation of candidate genes in case-control studies: a statistical method to account for related subjects

Traditional case-control studies provide a powerful and efficient method for evaluation of association between candidate genes and disease. The sampling of cases from multiplex pedigrees, rather than from a catchment area, can increase the likelihood that genetic cases are selected. However, use of all the related cases without accounting for their biological relationship can increase the type I error rate of the statistical test. To overcome this problem, we present an analysis method that is used to compare genotype frequencies between cases and controls, according to a trend in proportions as the dosage of the risk allele increases. This method uses the appropriate variance to account for the correlated family data, thus maintaining the correct type I error rate. The magnitude of the association is estimated by the odds ratio, with the variance of the odds ratio also accounting for the correlated data. Our method makes efficient use of data collected from multiplex families and should prove useful for the analysis of candidate genes among families sampled for linkage studies. An application of our method, to family data from a prostate cancer study, is presented to illustrate the method's utility.     

A Statistical Approach for Rare-Variant Association Testing in Affected Sibships

Sequencing and exome-chip technologies have motivated development of novel statistical tests to identify rare genetic variation that influences complex diseases. Although many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for testing association in families. This is unfortunate, because cosegregation of rare variation and disease status in families can amplify association signals for rare variants. Many researchers have begun sequencing (or genotyping via exome chips) familial samples that were either recently collected or previously collected for linkage studies. Because many linkage studies of complex diseases sampled affected sibships, we propose a strategy for association testing of rare variants for use in this study design. The logic behind our approach is that rare susceptibility variants should be found more often on regions shared identical by descent by affected sibling pairs than on regions not shared identical by descent. We propose both burden and variance-component tests of rare variation that are applicable to affected sibships of arbitrary size and that do not require genotype information from unaffected siblings or independent controls. Our approaches are robust to population stratification and produce analytic p values, thereby enabling our approach to scale easily to genome-wide studies of rare variation. We illustrate our methods by using simulated data and exome chip data from sibships ascertained for hypertension collected as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) study.     

Analysis of family- and population-based samples in cohort genome-wide association studies

Cohort studies typically sample unrelated individuals from a population, although family members of index cases may also be recruited to investigate shared familial risk factors. Recruitment of family members may be incomplete or ancillary to the main cohort, resulting in a mixed sample of independent family units, including unrelated singletons and multiplex families. Multiple methods are available to perform genome-wide association (GWA) analysis of binary or continuous traits in families, but it is unclear whether methods known to perform well on ascertained pedigrees, sibships, or trios are appropriate in analysis of a mixed unrelated cohort and family sample. We present simulation studies based on Multi-Ethnic Study of Atherosclerosis (MESA) pedigree structures to compare the performance of several popular methods of GWA analysis for both quantitative and dichotomous traits in cohort studies. We evaluate approaches suitable for analysis of families, and combined the best performing methods with population-based samples either by meta-analysis, or by pooled analysis of family- and population-based samples (mega-analysis), comparing type 1 error and power. We further assess practical considerations, such as availability of software and ability to incorporate covariates in statistical modeling, and demonstrate our recommended approaches through quantitative and binary trait analysis of HDL cholesterol (HDL-C) in 2,553 MESA family- and population-based African-American samples. Our results suggest linear modeling approaches that accommodate family-induced phenotypic correlation (e.g., variance-component model for quantitative traits or generalized estimating equations for dichotomous traits) perform best in the context of combined family- and population-based cohort GWAS.     

Detection of RFLP markers associated with antibody response in meat-type chickens: haplotype/genotype, single-band, and multiband analyses of RFLP in the major histocompatibility complex

Improving disease resistance in poultry by direct selection or by selecting for immune response is hardly feasible due to the quantitative nature of these traits, their low heritability, and the difficulties associated with reliable measurements. In this situation, marker-assisted selection (MAS) is expected to be a more effective breeding approach. The major histocompatibility complex (MHC), known to affect immune response and disease resistance, was examined as a set of candidate genes for association between DNA markers and antibody response. Backcross (BC1) and F2 families were generated from a cross between lines divergently selected for high or low antibody response to Escherichia coli vaccination. Restriction fragment length polymorphism (RFLP) analysis of the highly polymorphic MHC class IV (B-G) region suggested an association with antibody response to several antigens (E. coli, SRBC, NDV). The multiband data generated with the class IV probe were used to compare the efficacies of three alternative analyses: "single-band" (carriers versus noncarriers of each RFLP band separately), "multiband" (multiple regression on all RFLP bands), and "genotype" (determined from family analysis of RFLP patterns/haplotypes). Groups of birds identified by the "multiband" analysis were identical to the haplotype-based genotypes, suggesting that the laborious step of haplotype determination can be omitted without unduly sacrificing power of analysis.     

Angiotensin converting enzyme gene polymorphism and myocardial infarction a large association and linkage study

The DD genotype of the angiotensin converting enzyme (ACE) polymorphism has been associated with myocardial infarction (MI). However, sample sizes of many case-control studies showing positive association were small and data were inconsistent. Furthermore, no family-based study is available.In a case-control study frequencies of the ACE genotypes were compared in 1319 unrelated patients with previous MI before 60 years of age (616 from the MONICA Augsburg region and 703 from rehabilitation centers in south Germany) and in 2381 population controls from the MONICA Augsburg study region). Furthermore, linkage and association of the ACE I/D polymorphism with MI were tested in 246 informative families using the sib-transmission/disequilibrium test (S-TDT).Overall, no excess of the D allele was found in MI patients (frequency 0.53 versus 0.57 in the general population; P=0.2). The ACE DD genotype was even slightly less frequent in groups with MI compared to the general population controls (0.26 versus 0.33 in women and 0.28 versus 0.33 in men). Similar results were also obtained in 247 men with low cardiovascular risk. In the family-based study, the frequency of the D allele was not different in siblings with or without previous MI (0.53 versus 0.50, respectively; S-TDT P=0.15) indicating no linkage or association of the D allele with MI.In a case-control study of MI patients and controls from the general population as well as a family study neither association nor linkage of the ACE D allele with MI was detected despite sample sizes that were among the largest samples studied so far.     

Genetic analysis of 103 candidate genes for coronary artery disease and associated phenotypes in a founder population reveals a new association between endothelin-1 and high-density lipoprotein cholesterol

Coronary artery disease (CAD) is a major health concern in both developed and developing countries. With a heritability estimated at ~50%, there is a strong rationale to better define the genetic contribution to CAD. This project involves the analysis of 884 individuals from 142 families (with average sibships of 5.7) as well as 558 case and control subjects from the Saguenay Lac St-Jean region of northeastern Quebec, with the use of 1,536 single-nucleotide polymorphisms (SNPs) in 103 candidate genes for CAD. By use of clusters of SNPs to generate multiallelic haplotypes at candidate loci for segregation studies within families, suggestive linkage for high-density lipoprotein (HDL) cholesterol is observed on chromosome 1p36.22. Furthermore, several associations that remain significant after Bonferroni correction are observed with lipoprotein-related traits as well as plasma concentrations of adiponectin. Of note, HDL cholesterol levels are associated with an amino acid substitution (lysine/asparagine) at codon 198 (rs5370) of endothelin-1 (EDN1) in a sex-specific manner, as well as with a SNP (rs2292318) located 7.7 kb upstream of lecithin cholesterol acyl-transferase (LCAT). Whereas the other observed associations are described in the current literature, these two are new. Using an independent validation sample of 806 individuals, we confirm the EDN1 association (P<.005), whereas the LCAT association was nonsignificant (P=.12).     

A genome screen of multiplex sibships with prostate cancer

Analysis of a genome screen of 504 brothers with prostate cancer (CaP) who were from 230 multiplex sibships identified five regions with nominally positive linkage signals, on chromosomes 2q, 12p, 15q, 16p, and 16q. The strongest signal in these data is found on chromosome 16q, between markers D16S515 and D16S3040, a region suspected to contain a tumor-suppressor gene. On the basis of findings from previous genome screens of families with CaP, three preplanned subanalyses were carried out, in the hope of increasing the subgroup homogeneity. Subgroups were formed by dividing the sibships into a group with a positive family history (FH+) that met criteria for "hereditary" CaP (n=111) versus those which did not meet the criteria (n=119) and by dividing the families into those with a mean onset age below the median (n=115) versus those with a mean onset age above the median (n=115). A separate subanalysis was carried out for families with a history of breast cancer (CaB+ [n=53]). Analyses of these subgroups revealed a number of potentially important differences in regions that were nonsignificant when all the families were analyzed together. In particular, the subgroup without a positive family history (FH-) had a signal in a region that is proximal to the putative site of the HPC1 locus on chromosome 1, whereas the late-age-at-onset group had a signal on 4q. The CaB+ subgroup revealed a strong linkage signal at 1p35.1.     

Genomics and genome-wide association studies: an integrative approach to expression QTL mapping

Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results.     

Nonparametric tests of association of multiple genes with human disease

The genetic basis of many common human diseases is expected to be highly heterogeneous, with multiple causative loci and multiple alleles at some of the causative loci. Analyzing the association of disease with one genetic marker at a time can have weak power, because of relatively small genetic effects and the need to correct for multiple testing. Testing the simultaneous effects of multiple markers by multivariate statistics might improve power, but they too will not be very powerful when there are many markers, because of the many degrees of freedom. To overcome some of the limitations of current statistical methods for case-control studies of candidate genes, we develop a new class of nonparametric statistics that can simultaneously test the association of multiple markers with disease, with only a single degree of freedom. Our approach, which is based on U-statistics, first measures a score over all markers for pairs of subjects and then compares the averages of these scores between cases and controls. Genetic scoring for a pair of subjects is measured by a "kernel" function, which we allow to be fairly general. However, we provide guidelines on how to choose a kernel for different types of genetic effects. Our global statistic has the advantage of having only one degree of freedom and achieves its greatest power advantage when the contrasts of average genotype scores between cases and controls are in the same direction across multiple markers. Simulations illustrate that our proposed methods have the anticipated type I-error rate and that they can be more powerful than standard methods. Application of our methods to a study of candidate genes for prostate cancer illustrates their potential merits, and offers guidelines for interpretation.     

Possibility of the current segregation analysis to discriminate between monogenic and multifactorial types of inheritance of traits. The effect of the structure of family data on model robustness and power of the analysis

The influence of sampling designs for robustness of the autosomal major locus model and the multifactorial model as well as possibility of segregation analysis to discriminate these models was studied. Nuclear families and 3-generation pedigrees were considered. It was found that robustness of models increased, when the size of sibships in nuclear families grows and when configuration of pedigrees is complicated. The resolution power of the analysis is always increased with size elevation of sibships, the highest effect of the analysis being observed for sibships of the size 3 or 4. Consideration of new generations is only advisable, if attracting sibs of these generations, the resolution power being increased, provided that the parameters of models are of high value.     

The ACTN3 R577X Polymorphism across Three Groups of Elite Male European Athletes

The ACTN3 R577X polymorphism (rs1815739) is a strong candidate to influence elite athletic performance. Yet, controversy exists in the literature owing to between-studies differences in the ethnic background and sample size of the cohorts, the latter being usually low, which makes comparisons difficult. In this case:control genetic study we determined the association between elite athletic status and the ACTN3 R577X polymorphism within three cohorts of European Caucasian men, i.e. Spanish, Polish and Russian [633 cases (278 elite endurance and 355 power athletes), and 808 non-athletic controls]. The odds ratio (OR) of a power athlete harbouring the XX versus the RR genotype compared with sedentary controls was 0.54 [95% confidence interval (CI): 0.34–0.48; P = 0.006]. We also observed that the OR of an endurance athlete having the XX versus the RR genotype compared with power athletes was 1.88 (95%CI: 1.07–3.31; P = 0.028). In endurance athletes, the OR of a “world-class” competitor having the XX genotype versus the RR+RX genotype was 3.74 (95%CI: 1.08–12.94; P = 0.038) compared with those of a lower (“national”) competition level. No association (P>0.1) was noted between the ACTN3 R577X polymorphism and competition level (world-class versus national-level) in power athletes. Our data provide comprehensive support for the influence of the ACTN3 R577X polymorphism on elite athletic performance.     

X-APL: an improved family-based test of association in the presence of linkage for the X chromosome

Family-based association methods have been developed primarily for autosomal markers. The X-linked sibling transmission/disequilibrium test (XS-TDT) and the reconstruction-combined TDT for X-chromosome markers (XRC-TDT) are the first association-based methods for testing markers on the X chromosome in family data sets. These are valid tests of association in family triads or discordant sib pairs but are not theoretically valid in multiplex families when linkage is present. Recently, XPDT and XMCPDT, modified versions of the pedigree disequilibrium test (PDT), were proposed. Like the PDT, XPDT compares genotype transmissions from parents to affected offspring or genotypes of discordant siblings; however, the XPDT can have low power if there are many missing parental genotypes. XMCPDT uses a Monte Carlo sampling approach to infer missing parental genotypes on the basis of true or estimated population allele frequencies. Although the XMCPDT was shown to be more powerful than the XPDT, variability in the statistic due to the use of an estimate of allele frequency is not properly accounted for. Here, we present a novel family-based test of association, X-APL, a modification of the test for association in the presence of linkage (APL) test. Like the APL, X-APL can use singleton or multiplex families and properly infers missing parental genotypes in linkage regions by considering identity-by-descent parameters for affected siblings. Sampling variability of parameter estimates is accounted for through a bootstrap procedure. X-APL can test individual marker loci or X-chromosome haplotypes. To allow for different penetrances in males and females, separate sex-specific tests are provided. Using simulated data, we demonstrated validity and showed that the X-APL is more powerful than alternative tests. To show its utility and to discuss interpretation in real-data analysis, we also applied the X-APL to candidate-gene data in a sample of families with Parkinson disease.     

Maternal effects on fingertip dermatoglyphics.

Significantly larger variation between sibships within families of male MZ twins than between sibships within families of female MZ twins, indicative of maternal influences, was found for 10 of 41 dermatoglyphic fingertip variables. Of these, five were thumb-related with the effect primarily on the thumb radial and ridge count (larger of radial and ulnar count). These same variables were previously found to have unequal variances in MZ twins of known placental type, and the results indicate maternal influences in singletons as well as twins for these variables. Although the total ridge count (TRC), previously shown to differ in MZ twins of known placental type (paralleling the thumb radial and ridge counts) did not reach significance, the trend indicated that the observed thumb changes may be reflected in the TRC as well. Little finger pattern type and ulnar counts also showed less variability in families of female MZ twins, but the interpretation is complicated by the concomitant differences in mean squares within-sibships for these little finger variables.     

PLANTING DENSITY INFLUENCES THE EXPRESSION OF GENETIC VARIATION IN SEED MASS IN WILD RADISH (RAPHANUS SATIVUS L.: BRASSICACEAE)

To determine the effects of density, genotype, and their interaction on individual seed mass in Raphanus sativus L., we replicated maternal and paternal families of seed across two planting densities in an experimental garden. Seeds were produced by a nested breeding design performed in the greenhouse. Among garden-raised plants, density had a strong negative effect on the mass of seeds produced. At low density, the identity of the greenhouse-grown maternal plants had a strong effect on F₂ seed mass, while in high-density plots, there were no significant parental effects on mean seed mass. Significant parental genotype density interactions contributed to variation in F₂ seed mass. Norms of reaction for each of the 15 paternal sibships illustrate paternal family density interactions. Three sibships exhibited significant declines in mean seed mass with increasing density; 12 sibships showed no change. Maternal family density interaction effects on seed mass were also detected; among maternal sibships, mean seed mass at low density was negatively correlated with mean seed mass at high density. These results demonstrate: a) planting density has a strong effect on mean individual seed mass produced by adults; b) density influences the magnitude of maternal effects on progeny phenotype; and c) genotype density interactions influence seed mass, potentially contributing to the maintenance of maternal genetic variation in seed mass in natural populations of wild radish.     

Power for genetic association studies with random allele frequencies and genotype distributions

One of the first and most important steps in planning a genetic association study is the accurate estimation of the statistical power under a proposed study design and sample size. In association studies for candidate genes or in fine-mapping applications, allele and genotype frequencies are often assumed to be known when, in fact, they are unknown (i.e., random variables from some distribution). For example, if we consider a diallelic marker with allele frequencies of 0.5 and 0.5 and Hardy-Weinberg proportions, the three genotype frequencies are often assumed to be 0.25, 0.50, and 0.25, and the statistical power is calculated. Unfortunately, ignoring this source of variation can inflate the estimated power of the study. In the present article, we propose averaging the estimates of power over the distribution of the genotype frequencies to calculate the true estimate of power for a fixed allele frequency. For the usual situation, in which allele frequencies in a population are not known, we propose placing a prior distribution on the allele frequency, taking advantage of any available genotype information. This Bayesian approach provides a more accurate estimate of power. We present examples for quantitative and qualitative traits in cohort studies of unrelated individuals and results from an extensive series of examples that show that ignoring the uncertainty in allele frequencies can inflate the estimated power of the study. We also present the results from case-control studies and show that standard methods may also overestimate power. As discussed in this article, the approach of fixing allele frequencies even if they are not known is the common approach to power calculations. We show that ignoring the sources of variation in allele frequencies tends to result in overestimates of power and, consequently, in studies that are underpowered. Software in C is available at http://www.ambrosius.net/Power/.     

The power of genomic control

Although association analysis is a useful tool for uncovering the genetic underpinnings of complex traits, its utility is diminished by population substructure, which can produce spurious association between phenotype and genotype within population-based samples. Because family-based designs are robust against substructure, they have risen to the fore of association analysis. Yet, if population substructure could be ignored, this robustness can come at the price of power. Unfortunately it is rarely evident when population substructure can be ignored. Devlin and Roeder recently have proposed a method, termed "genomic control" (GC), which has the robustness of family-based designs even though it uses population-based data. GC uses the genome itself to determine appropriate corrections for population-based association tests. Using the GC method, we contrast the power of two study designs, family trios (i.e., father, mother, and affected progeny) versus case-control. For analysis of trios, we use the TDT test. When population substructure is absent, we find GC is always more powerful than TDT; furthermore, contrary to previous results, we show that as a disease becomes more prevalent the discrepancy in power becomes more extreme. When population substructure is present, however, the results are more complex: TDT is more powerful when population substructure is substantial, and GC is more powerful otherwise. We also explore general issues of power and implementation of GC within the case-control setting and find that, economically, GC is at least comparable to and often less expensive than family-based methods. Therefore, GC methods should prove a useful complement to family-based methods for the genetic analysis of complex traits.     

The power to detect linkage disequilibrium with quantitative traits in selected samples

Results from power studies for linkage detection have led to many ongoing and planned collections of phenotypically extreme nuclear families. Given the great expense of collecting these families and the imminent availability of a dense diallelic marker map, the families are likely to be used in allelic-association as well as linkage studies. However, optimal selection strategies for linkage may not be equally powerful for association. We examine the power to detect linkage disequilibrium for quantitative traits after phenotypic selection. The results encompass six selection strategies that are in widespread use, including single selection (two designs), affected sib pairs, concordant and discordant pairs, and the extreme-concordant and -discordant design. Selection of sibships on the basis of one extreme proband with high or low trait scores provides as much power as discordant sib pairs but requires the screening and phenotyping of substantially fewer initial families from which to select. Analysis of the role of allele frequencies within each selection design indicates that common trait alleles generally offer the most power, but similarities between the marker- and trait-allele frequencies are much more important than the trait-locus frequency alone. Some of the most widespread selection designs, such as single selection, yield power gains only when both the marker and quantitative trait loci (QTL) are relatively rare in the population. In contrast, discordant pairs and the extreme-proband design provide power for the broadest range of QTL-marker-allele frequency differences. Overall, proband selection from either tail provides the best balance of power, robustness, and simplicity of ascertainment for family-based association analysis.     

Genotype relative risks: methods for design and analysis of candidate-gene association studies.

Design and analysis methods are presented for studying the association of a candidate gene with a disease by using parental data in place of nonrelated controls. This alternative design eliminates spurious differences in allele frequencies between cases and nonrelated controls resulting from different ethnic origins and population stratification for these two groups. We present analysis methods which are based on two genetic relative risks: (1) the relative risk of disease for homozygotes with two copies of the candidate gene versus homozygotes without the candidate gene and (2) the relative risk for heterozygotes with one copy of the candidate gene versus homozygotes without the candidate gene. In addition to estimating the magnitude of these relative risks, likelihood methods allow specific hypotheses to be tested, namely, a test for overall association of the candidate gene with disease, as well as specific genetic hypotheses, such as dominant or recessive inheritance. Two likelihood methods are presented: (1) a likelihood method appropriate when Hardy-Weinberg equilibrium holds and (2) a likelihood method in which we condition on parental genotype data when Hardy-Weinberg equilibrium does not hold. The results for the relative efficiency of these two methods suggest that the conditional approach may at times be preferable, even when equilibrium holds. Sample-size and power calculations are presented for a multitiered design. The purpose of tier 1 is to detect the presence of an abnormal sequence for a postulated candidate gene among a small group of cases. The purpose of tier 2 is to test for association of the abnormal variant with disease, such as by the likelihood methods presented. The purpose of tier 3 is to confirm positive results from tier 2. Results indicate that required sample sizes are smaller when expression of disease is recessive, rather than dominant, and that, for recessive disease and large relative risks, necessary sample sizes may be feasible, even if only a small percentage of the disease can be attributed to the candidate gene.     

Simultaneously correcting for population stratification and for genotyping error in case-control association studies

In population-based case-control association studies, the regular chi (2) test is often used to investigate association between a candidate locus and disease. However, it is well known that this test may be biased in the presence of population stratification and/or genotyping error. Unlike some other biases, this bias will not go away with increasing sample size. On the contrary, the false-positive rate will be much larger when the sample size is increased. The usual family-based designs are robust against population stratification, but they are sensitive to genotype error. In this article, we propose a novel method of simultaneously correcting for the bias arising from population stratification and/or for the genotyping error in case-control studies. The appropriate corrections depend on sample odds ratios of the standard 2x3 tables of genotype by case and control from null loci. Therefore, the test is simple to apply. The corrected test is robust against misspecification of the genetic model. If the null hypothesis of no association is rejected, the corrections can be further used to estimate the effect of the genetic factor. We considered a simulation study to investigate the performance of the new method, using parameter values similar to those found in real-data examples. The results show that the corrected test approximately maintains the expected type I error rate under various simulation conditions. It also improves the power of the association test in the presence of population stratification and/or genotyping error. The discrepancy in power between the tests with correction and those without correction tends to be more extreme as the magnitude of the bias becomes larger. Therefore, the bias-correction method proposed in this article should be useful for the genetic analysis of complex traits.