Simultaneous discovery and testing of deletions for disease association in SNP genotyping studies

Identification of null alleles and deletions from SNP genotypes for an intercross between domestic and wild chickens

We analyzed genotypes from ~10K single-nucleotide polymorphisms (SNPs) in two families of an F₂ intercross between Red Junglefowl and White Leghorn chickens. Possible null alleles were found by patterns of incompatible and missing genotypes. We estimated that 2.6% of SNPs had null alleles compared with 2.3% with genotyping errors and that 40% of SNPs in which a parent and offspring were genotyped as different homozygotes had null alleles. Putative deletions were identified by null alleles at adjacent markers. We found two candidate deletions that were supported by fluorescence intensity data from a 60K SNP chip. One of the candidate deletions was from the Red Junglefowl, and one was present in both the Red Junglefowl and White Leghorn. Both candidate deletions spanned protein-coding regions and were close to a previously detected quantitative trait locus affecting body weight in this population. This study demonstrates that the ~50K SNP genotyping arrays now available for several agricultural species can be used to identify null alleles and deletions in data from large families. We suggest that our approach could be a useful complement to linkage analysis in experimental crosses.

Whole-genome detection of disease-associated deletions or excess homozygosity in a case-control study of rheumatoid arthritis

Unlike genome-wide association studies, few comprehensive studies of copy number variation's contribution to complex human disease susceptibility have been performed. Copy number variations are abundant in humans and represent one of the least well-studied classes of genetic variants; in addition, known rheumatoid arthritis susceptibility loci explain only a portion of familial clustering. Therefore, we performed a genome-wide study of association between deletion or excess homozygosity and rheumatoid arthritis using high-density 550 K SNP genotype data from a genome-wide association study. We used a genome-wide statistical method that we recently developed to test each contiguous SNP locus between 868 cases and 1194 controls to detect excess homozygosity or deletion variants that influence susceptibility. Our method is designed to detect statistically significant evidence of deletions or homozygosity at individual SNPs for SNP-by-SNP analyses and to combine the information among neighboring SNPs for cluster analyses. In addition to successfully detecting the known deletion variants on major histocompatibility complex, we identified 4.3 and 28 kb clusters on chromosomes 10p and 13q, respectively, which were significant at a Bonferroni-type-corrected 0.05 nominal significant level. Independently, we performed analyses using PennCNV, an algorithm for identifying and cataloging copy numbers for individuals based on a hidden Markov model, and identified cases and controls that had chromosomal segments with copy number <2. Using Fisher's exact test for comparing the numbers of cases and controls with copy number <2 per SNP, we identified 26 significant SNPs (protective; more controls than cases) aggregating on chromosome 14 with P-values <10(-8).

Genome-wide identification of copy number variations in Chinese Holstein

Recent studies of mammalian genomes have uncovered the vast extent of copy number variations (CNVs) that contribute to phenotypic diversity. Compared to SNP, a CNV can cover a wider chromosome region, which may potentially incur substantial sequence changes and induce more significant effects on phenotypes. CNV has been becoming an alternative promising genetic marker in the field of genetic analyses. Here we firstly report an account of CNV regions in the cattle genome in Chinese Holstein population. The Illumina Bovine SNP50K Beadchips were used for screening 2047 Holstein individuals. Three different programes (PennCNV, cnvPartition and GADA) were implemented to detect potential CNVs. After a strict CNV calling pipeline, a total of 99 CNV regions were identified in cattle genome. These CNV regions cover 23.24 Mb in total with an average size of 151.69 Kb. 52 out of these CNV regions have frequencies of above 1%. 51 out of these CNV regions completely or partially overlap with 138 cattle genes, which are significantly enriched for specific biological functions, such as signaling pathway, sensory perception response and cellular processes. The results provide valuable information for constructing a more comprehensive CNV map in the cattle genome and offer an important resource for investigation of genome structure and genomic variation underlying traits of interest in cattle.

Inheritance model introduces differential bias in CNV calls between parents and offspring

Copy Number Variation (CNV) is increasingly implicated in disease pathogenesis. CNVs are often identified by statistical models applied to data from single nucleotide polymorphism panels. Family information for samples provides additional information for CNV inference. Two modes of PennCNV (the Joint-call and Posterior-call), which are some of the most well-developed family-based CNV calling methods, use a "Joint-model" as a main component. This models all family members' CNV states together with Mendelian inheritance. Methods based on the Joint-model are used to infer CNV calls of cases and controls in a pedigree, which may be compared to each other to test an association. Although benefits from the Joint-model have been shown elsewhere, equality of call rates in parents and offspring has not been evaluated previously. This can affect downstream analyses in studies that compare CNV rates in cases vs. controls in pedigrees. In this paper, we show that the Joint-model can introduce different CNV call rates among family members in the absence of a true difference. We show that the Joint-model may analytically introduce differential CNV calls because of asymmetry of the model. We demonstrate these differential call rates using single-marker simulations. We show that call rates using the two modes of PennCNV also differ between parents and offspring in one multimarker simulated dataset and two real datasets. Our results advise need for caution in use of the Joint-model calls in CNV association studies with family-based datasets.

DominantMapper: rule-based analysis of SNP data for rapid mapping of dominant diseases in related nuclear families

With the advent of cheap rapid methods for whole-genome SNP genotyping and the completion of the Human Genome Project, mapping disease loci has become primarily a bioinformatic rather than a laboratory problem. Here, we describe DominantMapper, a computer program that implements a rule-based analysis algorithm for the detection of dominant disease loci in either a small number of nuclear families or a single large nuclear family. To demonstrate its utility, we present the successful analysis of two pedigrees in which the affected individuals carry either APC or TSPAN12 mutations.

Novel multiplex real-time PCR system using the SNP technology for the simultaneous diagnosis of Chlamydia trachomatis, Ureaplasma parvum and Ureaplasma urealyticum and genetic typing of serovars of C. trachomatis and U. parvum in NGU

To explore the possibilities of a novel multiplex real-time PCR system for rapid diagnosis, genetic typing of serovars and clinical application in NGU, we developed a multiplex real-time PCR system for the simultaneous diagnosis of Chlamydia trachomatis, Ureaplasma parvum and Ureaplasma urealyticum and molecular detection of serovars of C. trachomatis and U. parvum in NGU using the SNP technology and TaqMan-LNA probe. In 57 pathogen-positive clinical specimens, we identified the following C. trachomatis serovars: D (20.05%, 12/57), E (36.84%, 21/57), F (19.30%, 11/57), G (8.77%, 5/57), H (5.26%, 3/57), J (3.51%, 2/57), and K (5.26%, 3/57). In 115 pathogen-positive clinical specimens, we identified the following U. parvum serovars: 1 (0.87%, 2/115), 3 (55.65%, 64/115), 6 (20.87%, 24/115) and 14 (21.74%, 25/115). Our fast pathogen diagnosis and serotyping assay using real-time TaqMan-LNA PCR may improve our ability to study the pathogenesis and epidemiology of NGU.

Bayesian EM algorithm for scoring polymorphic deletions from SNP data and application to a common CNV on 8q24

Copy number variations (CNVs) in the human genome provide exciting candidates for functional polymorphisms. Hence, we now assess association between CNV carrier status and diseases status by evaluating the signal intensity of SNP genotyping assays. Here, we present a novel statistical method designed to perform such inference and apply this method to a known CNV in a bipolar disorder linkage region. Using Bayesian computations we calculate the posterior probability for carrier status of a CNV in each individual of a sample by jointly analyzing genotype information and hybridization intensity. We model the signal intensity as a mixture of normal distributions, allowing for locus-specific and allele-specific distributions. Using an expectation maximization algorithm we estimate the parameters of these distributions and use these estimates for inferring carrier status of each individual and for the boundaries of the CNV. We applied the method to a sample of 3,512 individuals to a previously described common deletion on 8q24, a region consistently showing linkage to bipolar disorder, and unambiguously inferred 172 heterozygous and 1 homozygous deletion carrier. We observed no significant association between bipolar disorder and carrier status. We carefully assessed the validity of the inferred carrier status and observed no indication of errors. Furthermore, the algorithm precisely identifies the boundaries of the CNV. Finally, we assessed the power of this algorithm to detect shorter CNVs by sub-sampling from the SNPs covered by this deletion, demonstrating that our EM algorithm produces precise estimates of carrier status.

Detection of disease-associated deletions in case-control studies using SNP genotypes with application to rheumatoid arthritis

Genomic deletions have long been known to play a causative role in microdeletion syndromes. Recent whole-genome genetic studies have shown that deletions can increase the risk for several psychiatric disorders, suggesting that genomic deletions play an important role in the genetic basis of complex traits. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. Likelihood-based statistical methods for identifying disease-associated deletions have recently been developed for familial studies of parent-offspring trios. The purpose of this study is to develop statistical approaches for detecting genomic deletions associated with complex disease in case-control studies. Our methods are designed to be used with dense single nucleotide polymorphism (SNP) genotypes to detect deletions in large-scale or whole-genome genetic studies. As more and more SNP genotype data for genome-wide association studies become available, development of sophisticated statistical approaches will be needed that use these data. Our proposed statistical methods are designed to be used in SNP-by-SNP analyses and in cluster analyses based on combined evidence from multiple SNPs. We found that these methods are useful for detecting disease-associated deletions and are robust in the presence of linkage disequilibrium using simulated SNP data sets. Furthermore, we applied the proposed statistical methods to SNP genotype data of chromosome 6p for 868 rheumatoid arthritis patients and 1,197 controls from the North American Rheumatoid Arthritis Consortium. We detected disease-associated deletions within the region of human leukocyte antigen in which genomic deletions were previously discovered in rheumatoid arthritis patients.

A flexible rank-based framework for detecting copy number aberrations from array data

MOTIVATION

DNA copy number aberration--both inherited and sporadic--is a significant contributor to a variety of human diseases. Copy number characterization is therefore an area of intense research. Probe hybridization-based arrays are important tools used to measure copy number in a high-throughput manner.

RESULTS

In this article, we present a simple but powerful nonparametric rank-based approach to detect deletions and gains from raw array copy number measurements. We use three different rank-based statistics to detect three separate molecular phenomena-somatic lesions, germline deletions and germline gains. The approach is robust and rigorously grounded in statistical theory, thereby enabling the meaningful assignment of statistical significance to each putative aberration. We demonstrate the flexibility of our approach by applying it to data from three different array platforms. We show that our method compares favorably with established approaches by applying it to published well-characterized samples. Power simulations demonstrate exquisite sensitivity for array data of reasonable quality.

CONCLUSIONS

Our flexible rank-based framework is suitable for multiple platforms including single nucleotide polymorphism arrays and array comparative genomic hybridization, and can reliably detect gains or losses of genomic DNA, whether inherited, de novo, or somatic.

AVAILABILITY

An R package RankCopy containing the methods described here, and is freely available from the author's web site (http://mendel.gene.cwru.edu/laframboiselab/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic association analysis of copy-number variation (CNV) in human disease pathogenesis

Structural genetic variation, including copy-number variation (CNV), constitutes a substantial fraction of total genetic variability and the importance of structural genetic variants in modulating human disease is increasingly being recognized. Early successes in identifying disease-associated CNVs via a candidate gene approach mandate that future disease association studies need to include structural genetic variation. Such analyses should not rely on previously developed methodologies that were designed to evaluate single nucleotide polymorphisms (SNPs). Instead, development of novel technical, statistical, and epidemiologic methods will be necessary to optimally capture this newly-appreciated form of genetic variation in a meaningful manner.

Detection, imputation, and association analysis of small deletions and null alleles on oligonucleotide arrays

Copy-number variation (CNV) is a major contributor to human genetic variation. Recently, CNV associations with human disease have been reported. Many genome-wide association (GWA) studies in complex diseases have been performed with sets of biallelic single-nucleotide polymorphisms (SNPs), but the available CNV methods are still limited. We present a new method (TriTyper) that can infer genotypes in case-control data sets for deletion CNVs, or SNPs with an extra, untyped allele at a high-resolution single SNP level. By accounting for linkage disequilibrium (LD), as well as intensity data, calling accuracy is improved. Analysis of 3102 unrelated individuals with European descent, genotyped with Illumina Infinium BeadChips, resulted in the identification of 1880 SNPs with a common untyped allele, and these SNPs are in strong LD with neighboring biallelic SNPs. Simulations indicate our method has superior power to detect associations compared to biallelic SNPs that are in LD with these SNPs, yet without increasing type I errors, as shown in a GWA analysis in celiac disease. Genotypes for 1204 triallelic SNPs could be fully imputed, with only biallelic-genotype calls, permitting association analysis of these SNPs in many published data sets. We estimate that 682 of the 1655 unique loci reflect deletions; this is on average 99 deletions per individual, four times greater than those detected by other methods. Whereas the identified loci are strongly enriched for known deletions, 61% have not been reported before. Genes overlapping with these loci more often have paralogs (p = 0.006) and biologically interact with fewer genes than expected (p = 0.004).

The effect of pedigree structure on detection of deletions and other null alleles

Deletions and other null alleles for genetic markers can be detected as a special case of non-Mendelian inheritance, ie when a parent and a child appear to be homozygous for different alleles. The probability to detect a deletion for a fixed overall number of investigated individuals was calculated for biallelic and multiallelic markers with varying allele frequencies. To determine the effect of increasing the number of parents and grandparents, the probability for this event was derived for a parent and one child, a trio, a trio with one grandparent and a trio with two grandparents. The results for biallelic markers show that for a fixed total number of individuals, a sample of trios with two grandparents is always more efficient than the other family types, despite a lower total number of founder chromosomes in the sample. For multiallelic markers the outcome varies. The effect of adding additional children to a nuclear family was also investigated. For nuclear families, the optimal number of children is two or three, depending on the allele frequencies. It is shown that adding children is more efficient than adding grandparents.

Structural variation of chromosomes in autism spectrum disorder

Structural variation (copy number variation [CNV] including deletion and duplication, translocation, inversion) of chromosomes has been identified in some individuals with autism spectrum disorder (ASD), but the full etiologic role is unknown. We performed genome-wide assessment for structural abnormalities in 427 unrelated ASD cases via single-nucleotide polymorphism microarrays and karyotyping. With microarrays, we discovered 277 unbalanced CNVs in 44% of ASD families not present in 500 controls (and re-examined in another 1152 controls). Karyotyping detected additional balanced changes. Although most variants were inherited, we found a total of 27 cases with de novo alterations, and in three (11%) of these individuals, two or more new variants were observed. De novo CNVs were found in approximately 7% and approximately 2% of idiopathic families having one child, or two or more ASD siblings, respectively. We also detected 13 loci with recurrent/overlapping CNV in unrelated cases, and at these sites, deletions and duplications affecting the same gene(s) in different individuals and sometimes in asymptomatic carriers were also found. Notwithstanding complexities, our results further implicate the SHANK3-NLGN4-NRXN1 postsynaptic density genes and also identify novel loci at DPP6-DPP10-PCDH9 (synapse complex), ANKRD11, DPYD, PTCHD1, 15q24, among others, for a role in ASD susceptibility. Our most compelling result discovered CNV at 16p11.2 (p = 0.002) (with characteristics of a genomic disorder) at approximately 1% frequency. Some of the ASD regions were also common to mental retardation loci. Structural variants were found in sufficiently high frequency influencing ASD to suggest that cytogenetic and microarray analyses be considered in routine clinical workup.