SNP microarray analysis for genome-wide detection of crossover regions

Clinical applications of whole-genome association studies: future applications at the bedside

Until now, performing whole-genome association studies has been an unattainable, but highly desirable, goal for geneticists. With the recent advent of high-throughput genotyping platforms, this goal is now a reality for geneticists today and for clinicians in the not-so-distant future. This review will cover a broad range of topics to provide an overview of this emerging branch of genetics, and will provide references to more specific sources. Specifically, this review will cover the technologies available today and in the near future, the specific types of whole-genome association studies, the benefits and limitations of these studies, the applications to complex disease-gene interactions, diagnostic devices, therapeutics, and finally, we will describe the 5-year perspective and key issues.

Whole genome identity-by-descent determination

High-throughput single nucleotide polymorphism genotyping assays conveniently produce genotype data for genome-wide genetic linkage and association studies. For pedigree datasets, the unphased genotype data is used to infer the haplotypes for individuals, according to Mendelian inheritance rules. Linkage studies can then locate putative chromosomal regions based on the haplotype allele sharing among the pedigree members and their disease status. Most existing haplotyping programs require rather strict pedigree structures and return a single inferred solution for downstream analysis. In this research, we relax the pedigree structure to contain ungenotyped founders and present a cubic time whole genome haplotyping algorithm to minimize the number of zero-recombination haplotype blocks. With or without explicitly enumerating all the haplotyping solutions, the algorithm determines all distinct haplotype allele identity-by-descent (IBD) sharings among the pedigree members, in linear time in the total number of haplotyping solutions. Our algorithm is implemented as a computer program iBDD. Extensive simulation experiments using 2 sets of 16 pedigree structures from previous studies showed that, in general, there are trillions of haplotyping solutions, but only up to a few thousand distinct haplotype allele IBD sharings. iBDD is able to return all these sharings for downstream genome-wide linkage and association studies.

INFERRING HAPLOTYPES FROM GENOTYPES ON A PEDIGREE WITH MUTATIONS, GENOTYPING ERRORS AND MISSING ALLELES

Inferring the haplotypes of the members of a pedigree from their genotypes has been extensively studied. However, most studies do not consider genotyping errors and de novo mutations. In this paper, we study how to infer haplotypes from genotype data that may contain genotyping errors, de novo mutations, and missing alleles. We assume that there are no recombinants in the genotype data, which is usually true for tightly linked markers. We introduce a combinatorial optimization problem, called haplotype configuration with mutations and errors (HCME), which calls for haplotype configurations consistent with the given genotypes that incur no recombinants and require the minimum number of mutations and errors. HCME is NP-hard. To solve the problem, we propose a heuristic algorithm, the core of which is an integer linear program (ILP) using the system of linear equations over Galois field GF(2). Our algorithm can detect and locate genotyping errors that cannot be detected by simply checking the Mendelian law of inheritance. The algorithm also offers error correction in genotypes/haplotypes rather than just detecting inconsistencies and deleting the involved loci. Our experimental results show that the algorithm can infer haplotypes with a very high accuracy and recover 65%–94% of genotyping errors depending on the pedigree topology.

Rhizomelic chrondrodysplasia punctata type 2 resulting from paternal isodisomy of chromosome 1

Rhizomelic chondrodysplasia punctata (RCDP) is an autosomal-recessive disorder resulting from mutations in one of three peroxisomal genes essential for ether lipid biosynthesis, PEX7 (RCDP1), GNPAT (RCDP2), and AGPS (RCDP3). Affected patients have characteristic features including shortening of the proximal long bones, epiphyseal stippling, bilateral cataracts, growth and developmental delays. Whereas the majority of patients have RCDP type 1, around 5% have RCDP type 2 or 3. We identified a patient with RCDP type 2 and an apparent homozygous deletion, c.1428delC, after full sequencing of his GNPAT genes. The father was heterozygous for this mutation, while sequencing of the maternal GNPAT genes revealed only wild-type sequence. Southern analyses performed on parental gDNA did not show evidence of a maternal gene deletion. Amplification and fragment analysis of dinucleotide repeat markers spanning chromosome 1 in the patient and both parents revealed paternal uniparental inheritance. We discuss the potential mechanisms causing uniparental disomy (UPD) in this patient and review the literature on chromosome 1 UPD. The absence of non-RCDP clinical features in this patient was consistent with previous literature supporting the absence of imprinted genes on chromosome 1. This first description of RCDP caused by UPD dramatically changes the parental recurrence risk, highlighting the value of obtaining parental genotypes when the proband has a putative homozygous mutation by sequence analysis.

Locations and patterns of meiotic recombination in two-generation pedigrees

Background

Meiotic crossovers are the major mechanism by which haplotypes are shuffled to generate genetic diversity. Previously available methods for the genome-wide, high-resolution identification of meiotic crossover sites are limited by the laborious nature of the assay (as in sperm typing).

Methods

Several methods have been introduced to identify crossovers using high density single nucleotide polymorphism (SNP) array technologies, although programs are not widely available to implement such analyses.

Results

Here we present a two-generation "reverse pedigree analysis" method (analyzing the genotypes of two children relative to each parent) and a web-accessible tool to determine and visualize inheritance differences among siblings and crossover locations on each parental gamete. This approach is complementary to existing methods and uses informative markers which provide high resolution for locating meiotic crossover sites. We introduce a segmentation algorithm to identify crossover sites, and used a synthetic data set to determine that the segmentation algorithm specificity was 92% and sensitivity was 89%. The use of reverse pedigrees allows the inference of crossover locations on the X chromosome in a maternal gamete through analysis of two sons and their father. We further analyzed genotypes from eight multiplex autism families, observing a 1.462 maternal to paternal recombination ratio and no significant differences between affected and unaffected children. Meiotic recombination results from pediSNP can also be used to identify haplotypes that are shared by probands within a pedigree, as we demonstrated with a multiplex autism family.

Conclusion

Using "reverse pedigrees" and defining unique sets of genotype markers within pedigree data, we introduce a method that identifies inherited allelic differences and meiotic crossovers. We implemented the method in the pediSNP software program, and we applied it to several data sets. This approach uses data from two generations to identify crossover sites, facilitating studies of recombination in disease. pediSNP is available online at .

Visualization of shared genomic regions and meiotic recombination in high-density SNP data

Background

A fundamental goal of single nucleotide polymorphism (SNP) genotyping is to determine the sharing of alleles between individuals across genomic loci. Such analyses have diverse applications in defining the relatedness of individuals (including unexpected relationships in nominally unrelated individuals, or consanguinity within pedigrees), analyzing meiotic crossovers, and identifying a broad range of chromosomal anomalies such as hemizygous deletions and uniparental disomy, and analyzing population structure.

Principal Findings

We present SNPduo, a command-line and web accessible tool for analyzing and visualizing the relatedness of any two individuals using identity by state. Using identity by state does not require prior knowledge of allele frequencies or pedigree information, and is more computationally tractable and is less affected by population stratification than calculating identity by descent probabilities. The web implementation visualizes shared genomic regions, and generates UCSC viewable tracks. The command-line version requires pedigree information for compatibility with existing software and determining specified relationships even though pedigrees are not required for IBS calculation, generates no visual output, is written in portable C++, and is well-suited to analyzing large datasets. We demonstrate how the SNPduo web tool identifies meiotic crossover positions in siblings, and confirm our findings by visualizing meiotic recombination in synthetic three-generation pedigrees. We applied SNPduo to 210 nominally unrelated Phase I / II HapMap samples and, consistent with previous findings, identified six undeclared pairs of related individuals. We further analyzed identity by state in 2,883 individuals from multiplex families with autism and identified a series of anomalies including related parents, an individual with mosaic loss of chromosome 18, an individual with maternal heterodisomy of chromosome 16, and unexplained replicate samples.

Conclusions

SNPduo provides the ability to explore and visualize SNP data to characterize the relatedness between individuals. It is compatible with, but distinct from, other established analysis software such as PLINK, and performs favorably in benchmarking studies for the analyses of genetic relatedness.

Most parsimonious haplotype allele sharing determination

Background

The "common disease – common variant" hypothesis and genome-wide association studies have achieved numerous successes in the last three years, particularly in genetic mapping in human diseases. Nevertheless, the power of the association study methods are still low, in particular on quantitative traits, and the description of the full allelic spectrum is deemed still far from reach. Given increasing density of single nucleotide polymorphisms available and suggested by the block-like structure of the human genome, a popular and prosperous strategy is to use haplotypes to try to capture the correlation structure of SNPs in regions of little recombination. The key to the success of this strategy is thus the ability to unambiguously determine the haplotype allele sharing status among the members. The association studies based on haplotype sharing status would have significantly reduced degrees of freedom and be able to capture the combined effects of tightly linked causal variants.

Results

For pedigree genotype datasets of medium density of SNPs, we present two methods for haplotype allele sharing status determination among the pedigree members. Extensive simulation study showed that both methods performed nearly perfectly on breakpoint discovery, mutation haplotype allele discovery, and shared chromosomal region discovery.

Conclusion

For pedigree genotype datasets, the haplotype allele sharing status among the members can be deterministically, efficiently, and accurately determined, even for very small pedigrees. Given their excellent performance, the presented haplotype allele sharing status determination programs can be useful in many downstream applications including haplotype based association studies.

Mammalian meiotic recombination hot spots

Our understanding of the details of mammalian meiotic recombination has recently advanced significantly. Sperm typing technologies, linkage studies, and computational inferences from population genetic data have together provided information in unprecedented detail about the location and activity of the sites of crossing-over in mice and humans. The results show that the vast majority of meiotic recombination events are localized to narrow DNA regions (hot spots) that constitute only a small fraction of the genome. The data also suggest that the molecular basis of hot spot activity is unlikely to be strictly determined by specific DNA sequence motifs in cis. Further molecular studies are needed to understand how hot spots originate, function and evolve.

Identification of linked regions using high-density SNP genotype data in linkage analysis

MOTIVATION

With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary.

RESULTS

In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the chi(2) model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data.

AVAILABILITY

http://paed.hku.hk/uploadarea/yangwl/html/index.html

Global variation in copy number in the human genome

Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.