Detecting disease variants in case-parent trio studies using the bioconductor software package trio.

Case-parent trio studies are commonly employed in genetics to detect variants underlying common complex disease risk. Both commercial and freely available software suites for genetic data analysis usually contain methods for case-parent trio designs. A user might, however, experience limitations with these packages, which can include missing functionality to extend the software if a desired analysis has not been implemented, and the inability to programmatically capture all the software versions used for low-level processing and high-level inference of genomic data, a critical consideration in particular for high-throughput experiments. Here, we present a software vignette (i.e., a manual with step by step instructions and examples to demonstrate software functionality) for reproducible genome-wide analyses of case-parent trio data using the open source Bioconductor package trio. The workflow for the practitioner uses data from previous genetic trio studies to illustrate functions for marginal association tests, assessment of parent-of-origin effects, power and sample size calculations, and functions to detect gene-gene and gene-environment interactions associated with disease.

Inferring rare disease risk variants based on exact probabilities of sharing by multiple affected relatives.

MOTIVATION: Family-based designs are regaining popularity for genomic sequencing studies because they provide a way to test cosegregation with disease of variants that are too rare in the population to be tested individually in a conventional case-control study. RESULTS: Where only a few affected subjects per family are sequenced, the probability that any variant would be shared by all affected relatives-given it occurred in any one family member-provides evidence against the null hypothesis of a complete absence of linkage and association. A P-value can be obtained as the sum of the probabilities of sharing events as (or more) extreme in one or more families. We generalize an existing closed-form expression for exact sharing probabilities to more than two relatives per family. When pedigree founders are related, we show that an approximation of sharing probabilities based on empirical estimates of kinship among founders obtained from genome-wide marker data is accurate for low levels of kinship. We also propose a more generally applicable approach based on Monte Carlo simulations. We applied this method to a study of 55 multiplex families with apparent non-syndromic forms of oral clefts from four distinct populations, with whole exome sequences available for two or three affected members per family. The rare single nucleotide variant rs149253049 in ADAMTS9 shared by affected relatives in three Indian families achieved significance after correcting for multiple comparisons ([Formula: see text]). AVAILABILITY AND IMPLEMENTATION: Source code and binaries of the R package RVsharing are freely available for download at http://cran.r-project.org/web/packages/RVsharing/index.html. CONTACT: alexandre.bureau@msp.ulaval.ca or ingo@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A genome-wide study of inherited deletions identified two regions associated with nonsyndromic isolated oral clefts.

BACKGROUND: DNA copy number variants play an important part in the development of common birth defects such as oral clefts. Individual patients with multiple birth defects (including oral clefts) have been shown to carry small and large chromosomal deletions. METHODS: We investigated the role of polymorphic copy number deletions by comparing transmission rates of deletions from parents to offspring in case-parent trios of European ancestry ascertained through a cleft proband with trios ascertained through a normal offspring. DNA copy numbers in trios were called using the joint hidden Markov model in the freely available PennCNV software. All statistical analyses were performed using Bioconductor tools in the open source environment R. RESULTS: We identified a 67 kb region in the gene MGAM on chromosome 7q34, and a 206 kb region overlapping genes ADAM3A and ADAM5 on chromosome 8p11, where deletions are more frequently transmitted to cleft offspring than control offspring. CONCLUSIONS: These genes or nearby regulatory elements may be involved in the etiology of oral clefts.

A genome-wide study of de novo deletions identifies a candidate locus for non-syndromic isolated cleft lip/palate risk.

BACKGROUND: Copy number variants (CNVs) may play an important part in the development of common birth defects such as oral clefts, and individual patients with multiple birth defects (including clefts) have been shown to carry small and large chromosomal deletions. In this paper we investigate de novo deletions defined as DNA segments missing in an oral cleft proband but present in both unaffected parents. We compare de novo deletion frequencies in children of European ancestry with an isolated, non-syndromic oral cleft to frequencies in children of European ancestry from randomly sampled trios. RESULTS: We identified a genome-wide significant 62 kilo base (kb) non-coding region on chromosome 7p14.1 where de novo deletions occur more frequently among oral cleft cases than controls. We also observed wider de novo deletions among cleft lip and palate (CLP) cases than seen among cleft palate (CP) and cleft lip (CL) cases. CONCLUSIONS: This study presents a region where de novo deletions appear to be involved in the etiology of oral clefts, although the underlying biological mechanisms are still unknown. Larger de novo deletions are more likely to interfere with normal craniofacial development and may result in more severe clefts. Study protocol and sample DNA source can severely affect estimates of de novo deletion frequencies. Follow-up studies are needed to further validate these findings and to potentially identify additional structural variants underlying oral clefts.

Analytic power and sample size calculation for the genotypic transmission/disequilibrium test in case-parent trio studies.

Case-parent trio studies considering genotype data from children affected by a disease and their parents are frequently used to detect single nucleotide polymorphisms (SNPs) associated with disease. The most popular statistical tests for this study design are transmission/disequilibrium tests (TDTs). Several types of these tests have been developed, for example, procedures based on alleles or genotypes. Therefore, it is of great interest to examine which of these tests have the highest statistical power to detect SNPs associated with disease. Comparisons of the allelic and the genotypic TDT for individual SNPs have so far been conducted based on simulation studies, since the test statistic of the genotypic TDT was determined numerically. Recently, however, it has been shown that this test statistic can be presented in closed form. In this article, we employ this analytic solution to derive equations for calculating the statistical power and the required sample size for different types of the genotypic TDT. The power of this test is then compared with the one of the corresponding score test assuming the same mode of inheritance as well as the allelic TDT based on a multiplicative mode of inheritance, which is equivalent to the score test assuming an additive mode of inheritance. This is, thus, the first time the power of these tests are compared based on equations, yielding instant results and omitting the need for time-consuming simulation studies. This comparison reveals that these tests have almost the same power, with the score test being slightly more powerful.

Fast detection of de novo copy number variants from SNP arrays for case-parent trios.

BACKGROUND: In studies of case-parent trios, we define copy number variants (CNVs) in the offspring that differ from the parental copy numbers as de novo and of interest for their potential functional role in disease. Among the leading array-based methods for discovery of de novo CNVs in case-parent trios is the joint hidden Markov model (HMM) implemented in the PennCNV software. However, the computational demands of the joint HMM are substantial and the extent to which false positive identifications occur in case-parent trios has not been well described. We evaluate these issues in a study of oral cleft case-parent trios. RESULTS: Our analysis of the oral cleft trios reveals that genomic waves represent a substantial source of false positive identifications in the joint HMM, despite a wave-correction implementation in PennCNV. In addition, the noise of low-level summaries of relative copy number (log R ratios) is strongly associated with batch and correlated with the frequency of de novo CNV calls. Exploiting the trio design, we propose a univariate statistic for relative copy number referred to as the minimum distance that can reduce technical variation from probe effects and genomic waves. We use circular binary segmentation to segment the minimum distance and maximum a posteriori estimation to infer de novo CNVs from the segmented genome. Compared to PennCNV on simulated data, MinimumDistance identifies fewer false positives on average and is comparable to PennCNV with respect to false negatives. Genomic waves contribute to discordance of PennCNV and MinimumDistance for high coverage de novo calls, while highly concordant calls on chromosome 22 were validated by quantitative PCR. Computationally, MinimumDistance provides a nearly 8-fold increase in speed relative to the joint HMM in a study of oral cleft trios. CONCLUSIONS: Our results indicate that batch effects and genomic waves are important considerations for case-parent studies of de novo CNV, and that the minimum distance is an effective statistic for reducing technical variation contributing to false de novo discoveries. Coupled with segmentation and maximum a posteriori estimation, our algorithm compares favorably to the joint HMM with MinimumDistance being much faster.

Sex-dependent association of a common low-density lipoprotein receptor polymorphism with RNA splicing efficiency in the brain and Alzheimer's disease.

Since apoE allele status is the predominant Alzheimer's disease (AD) genetic risk factor, functional single nucleotide polymorphisms (SNPs) in brain apoE receptors represent excellent candidates for association with AD. Recently, we identified a SNP, rs688, as modulating the splicing efficiency of low-density lipoprotein receptor (LDLR) exon 12 in female human liver and in minigene-transfected HepG2 cells. Moreover, the rs688T minor allele was associated with significantly higher LDL and total cholesterol in women within the Framingham Offspring Study cohort. Since LDLR is a major apoE receptor in the brain, we hypothesized that rs688 modulates LDLR splicing in neural tissues and associates with AD. To evaluate this hypothesis, we first transfected LDLR minigenes into SH-SY5Y neuroblastoma cells and found that the rs688T allele reduces exon 12 inclusion in this neural model. We then evaluated the association of rs688 allele with exon 12 splicing efficiency in vivo by quantifying LDLR splicing in human anterior cingulate tissue obtained at autopsy; the rs688T allele is associated with decreased LDLR exon 12 splicing efficiency in aged males, but not females. Lastly, we evaluated whether rs688 associates with AD by genotyping DNA from 1457 men and 2055 women drawn from three case-control series. The rs688T/T genotype was associated with increased AD odds in males [recessive model, odds ratio (OR) of 1.49, 95% confidence interval (CI) of 1.13-1.97, uncorrected P = 0.005], but not in females. In summary, these studies identify a functional apoE receptor SNP that is associated with AD in a sex-dependent fashion.

On multi-marker tests for association in case-control studies.

Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we establish a theoretical benchmark by quantifying the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (2007) based on the maximum absolute test statistic observed in an LD block is a practical and powerful method in a variety of settings. We also explore the effect on the power that prior biological or functional knowledge used to narrow down the locus of the causal marker can have, and conclude that this prior knowledge has to be very strong and specific for the power to approach the maximum achievable level, or even beat the power observed for methods such as the one proposed by Conneely and Boehnke (2007).

Gene expression levels as endophenotypes in genome-wide association studies of Alzheimer disease.

BACKGROUND: Late-onset Alzheimer disease (LOAD) is a common disorder with a substantial genetic component. We postulate that many disease susceptibility variants act by altering gene expression levels. METHODS: We measured messenger RNA (mRNA) expression levels of 12 LOAD candidate genes in the cerebella of 200 subjects with LOAD. Using the genotypes from our LOAD genome-wide association study for the cis-single nucleotide polymorphisms (SNPs) (n = 619) of these 12 LOAD candidate genes, we tested for associations with expression levels as endophenotypes. The strongest expression cis-SNP was tested for AD association in 7 independent case-control series (2,280 AD and 2,396 controls). RESULTS: We identified 3 SNPs that associated significantly with IDE (insulin degrading enzyme) expression levels. A single copy of the minor allele for each significant SNP was associated with approximately twofold higher IDE expression levels. The most significant SNP, rs7910977, is 4.2 kb beyond the 3' end of IDE. The association observed with this SNP was significant even at the genome-wide level (p = 2.7 x 10(-8)). Furthermore, the minor allele of rs7910977 associated significantly (p = 0.0046) with reduced LOAD risk (OR = 0.81 with a 95% CI of 0.70-0.94), as expected biologically from its association with elevated IDE expression. CONCLUSIONS: These results provide strong evidence that IDE is a late-onset Alzheimer disease (LOAD) gene with variants that modify risk of LOAD by influencing IDE expression. They also suggest that the use of expression levels as endophenotypes in genome-wide association studies may provide a powerful approach for the identification of disease susceptibility alleles.

Concordant association of insulin degrading enzyme gene (IDE) variants with IDE mRNA, Abeta, and Alzheimer's disease.

BACKGROUND: The insulin-degrading enzyme gene (IDE) is a strong functional and positional candidate for late onset Alzheimer's disease (LOAD). METHODOLOGY/PRINCIPAL FINDINGS: We examined conserved regions of IDE and its 10 kb flanks in 269 AD cases and 252 controls thereby identifying 17 putative functional polymorphisms. These variants formed eleven haplotypes that were tagged with ten variants. Four of these showed significant association with IDE transcript levels in samples from 194 LOAD cerebella. The strongest, rs6583817, which has not previously been reported, showed unequivocal association (p = 1.5x10(-8), fold-increase = 2.12,); the eleven haplotypes were also significantly associated with transcript levels (global p = 0.003). Using an in vitro dual luciferase reporter assay, we found that rs6583817 increases reporter gene expression in Be(2)-C (p = 0.006) and HepG2 (p = 0.02) cell lines. Furthermore, using data from a recent genome-wide association study of two Croatian isolated populations (n = 1,879), we identified a proxy for rs6583817 that associated significantly with decreased plasma Abeta40 levels (ss = -0.124, p = 0.011) and total measured plasma Abeta levels (b = -0.130, p = 0.009). Finally, rs6583817 was associated with decreased risk of LOAD in 3,891 AD cases and 3,605 controls. (OR = 0.87, p = 0.03), and the eleven IDE haplotypes (global p = 0.02) also showed significant association. CONCLUSIONS: Thus, a previously unreported variant unequivocally associated with increased IDE expression was also associated with reduced plasma Abeta40 and decreased LOAD susceptibility. Genetic association between LOAD and IDE has been difficult to replicate. Our findings suggest that targeted testing of expression SNPs (eSNPs) strongly associated with altered transcript levels in autopsy brain samples may be a powerful way to identify genetic associations with LOAD that would otherwise be difficult to detect.

Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer's disease.

By analyzing late-onset Alzheimer's disease (LOAD) in a genome-wide association study (313,504 SNPs, three series, 844 cases and 1,255 controls) and evaluating the 25 SNPs with the most significant allelic association in four additional series (1,547 cases and 1,209 controls), we identified a SNP (rs5984894) on Xq21.3 in PCDH11X that is strongly associated with LOAD in individuals of European descent from the United States. Analysis of rs5984894 by multivariable logistic regression adjusted for sex gave global P values of 5.7 x 10(-5) in stage 1, 4.8 x 10(-6) in stage 2 and 3.9 x 10(-12) in the combined data. Odds ratios were 1.75 (95% CI = 1.42-2.16) for female homozygotes (P = 2.0 x 10(-7)) and 1.26 (95% CI = 1.05-1.51) for female heterozygotes (P = 0.01) compared to female noncarriers. For male hemizygotes (P = 0.07) compared to male noncarriers, the odds ratio was 1.18 (95% CI = 0.99-1.41).