Family-based association tests for genomewide association scans

With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.     

SNP discovery using advanced algorithms and neural networks

Forage is an application which uses two neural networks for detecting single nucleotide polymorphisms (SNPs). Potential SNP candidates are identified in multiple alignments. Each candidate is then represented by a vector of features, which is classified as SNP or monomorphic by the networks. A validated dataset of SNPs was constructed from experimentally verified SNP data and used for network training and method evalutation. AVAILABILITY: The package is available at biobase.biotech.kth.se/forage/     

SNP-PHAGE – High throughput SNP discovery pipeline

Background  

Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable.     

Genotyping single-nucleotide polymorphisms by matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry

In recent years matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI) has emerged as a very powerful method for genotyping single nucleotide polymorphisms. The accuracy, speed of data accumulation, and data structure are the major features of MALDI. Several SNP genotyping methods have been implemented with a high degree of automation and are being applied for large-scale association studies. Most methods for SNP genotyping using MALDI mass spectrometric detection and their potential application for high-throughput are reviewed here.     

Redundancy based detection of sequence polymorphisms in expressed sequence tag data using autoSNP

AutoSNP is a program to detect single nucleotide polymorphisms (SNPs) and insertion/deletion polymorphisms (indels) in expressed sequence tag (EST) data. The program uses d2cluster and cap3 to cluster and align EST sequences, and uses redundancy to differentiate between candidate SNPs and sequence errors. Candidate polymorphisms are identified as occurring in multiple reads within an alignment. For each candidate SNP, two measures of confidence are calculated, the redundancy of the polymorphism at a SNP locus and the co segregation of the candidate SNP with other SNPs in the alignment. AVAILABILITY: The program was written in PERL and is freely available to non-commercial users by request from the authors.     

SNP discovery in nonmodel organisms: strand bias and base‐substitution errors reduce conversion rates

Single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in organisms of conservation, commercial or biological interest. Most SNP discovery projects in nonmodel organisms apply a strategy for identifying putative SNPs based on filtering rules that account for random sequencing errors. Here, we analyse data used to develop 4723 novel SNPs for the commercially important deep‐sea fish, orange roughy (Hoplostethus atlanticus), to assess the impact of not accounting for systematic sequencing errors when filtering identified polymorphisms when discovering SNPs. We used SAMtools to identify polymorphisms in a velvet assembly of genomic DNA sequence data from seven individuals. The resulting set of polymorphisms were filtered to minimize ‘bycatch’—polymorphisms caused by sequencing or assembly error. An Illumina Infinium SNP chip was used to genotype a final set of 7714 polymorphisms across 1734 individuals. Five predictors were examined for their effect on the probability of obtaining an assayable SNP: depth of coverage, number of reads that support a variant, polymorphism type (e.g. A/C), strand‐bias and Illumina SNP probe design score. Our results indicate that filtering out systematic sequencing errors could substantially improve the efficiency of SNP discovery. We show that BLASTX can be used as an efficient tool to identify single‐copy genomic regions in the absence of a reference genome. The results have implications for research aiming to identify assayable SNPs and build SNP genotyping assays for nonmodel organisms.     

Increasing power of genome-wide association studies by collecting additional single-nucleotide polymorphisms

Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation- and distance-based traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics.cs.ucla.edu/followupSNPs.     

Estimating population divergence time and phylogeny from single-nucleotide polymorphisms data with outgroup ascertainment bias

The inference of population divergence times and branching patterns is of fundamental importance in many population genetic analyses. Many methods have been developed for estimating population divergence times, and recently, there has been particular attention towards genome-wide single-nucleotide polymorphisms (SNP) data. However, most SNP data have been affected by an ascertainment bias caused by the SNP selection and discovery protocols. Here, we present a modification of an existing maximum likelihood method that will allow approximately unbiased inferences when ascertainment is based on a set of outgroup populations. We also present a method for estimating trees from the asymmetric dissimilarity measures arising from pairwise divergence time estimation in population genetics. We evaluate the methods by simulations and by applying them to a large SNP data set of seven East Asian populations.     

Genome-wide tagging SNPs with entropy-based Monte Carlo method.

The number of common single nucleotide polymorphisms (SNPs) in the human genome is estimated to be around 3-6 million. It is highly anticipated that the study of SNPs will help provide a means for elucidating the genetic component of complex diseases and variable drug responses. High-throughput technologies such as oligonucleotide arrays have produced enormous amount of SNP data, which creates great challenges in genome-wide disease linkage and association studies. In this paper, we present an adaptation of the cross entropy (CE) method and propose an iterative CE Monte Carlo (CEMC) algorithm for tagging SNP selection. This differs from most of SNP selection algorithms in the literature in that our method is independent of the notion of haplotype block. Thus, the method is applicable to whole genome SNP selection without prior knowledge of block boundaries. We applied this block-free algorithm to three large datasets (two simulated and one real) that are in the order of thousands of SNPs. The successful applications to these large scale datasets demonstrate that CEMC is computationally feasible for whole genome SNP selection. Furthermore, the results show that CEMC is significantly better than random selection, and it also outperformed another block-free selection algorithm for the dataset considered.     

Isolation and characterization of SNP variation at 90 anonymous loci in the banded wren (<Emphasis Type="Italic">Thryothorus pleurostictus)</Emphasis>

Single nucleotide polymorphisms (SNPs) are becoming more commonly used as molecular markers in conservation studies. However, relatively few studies have employed SNPs for species with little or no existing sequence data, partly due to the practical challenge of locating appropriate SNP loci in these species. Here we describe an application of SNP discovery via shotgun cloning that requires no pre-existing sequence data and is readily applied to all taxa. Using this method, we isolated, cloned and screened for SNP variation at 90 anonymous sequence loci (51 kb total) from the banded wren (Thryothorus pleurostictus), a Central American species with minimal pre-existing sequence data and a documented paucity of microsatellite allelic variation. We identified 168 SNPs (a mean of one SNP/305 bp, with SNPs unevenly distributed across loci). Further characterization of variation at 41 of these SNP loci among 256 individuals including 37 parent–offspring families suggests that they provide substantial information for defining the genetic mating system of this species, and that SNPs may be generally useful for this purpose when other markers are problematic.     

Conversion of barley SNPs into PCR-based markers using dCAPS method

Molecular genetic research relies heavily on the ability to detect polymorphisms in DNA. Single nucleotide polymorphisms (SNPs) are the most frequent form of DNA variation in the genome. In combination with a PCR assay, the corresponding SNP can be analyzed as a derived cleaved amplified polymorphic sequence (dCAPS) marker. The dCAPS method exploits the well-known specificity of a restriction endonuclease for its recognition site and can be used to virtually detect any SNP. Here, we describe the use of the dCAPS method for detecting single-nucleotide changes by means of a barley EST, CK569932, PCR-based marker.     

Correcting for ascertainment biases when analyzing SNP data: applications to the estimation of linkage disequilibrium

As large-scale sequencing efforts turn from single genome sequencing to polymorphism discovery, single nucleotide polymorphisms (SNPs) are becoming an increasingly important class of population genetic data. But because of the ascertainment biases introduced by many methods of SNP discovery, most SNP data cannot be analyzed using classical population genetic methods. Statistical methods must instead be developed that can explicitly take into account each method of SNP discovery. Here we review some of the current methods for analyzing SNPs and derive sampling distributions for single SNPs and pairs of SNPs for some common SNP discovery schemes. We also show that the ascertainment scheme has a large effect on the estimation of linkage disequilibrium and recombination, and describe some methods of correcting for ascertainment biases when estimating recombination rates from SNP data.     

Dynamic variable selection in SNP genotype autocalling from APEX microarray data

Background  

Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide – adenine (A), thymine (T), cytosine (C) or guanine (G) – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Our laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). This mini-sequencing method is a powerful combination of a highly parallel microarray with distinctive Sanger-based dideoxy terminator sequencing chemistry. Using this microarray platform, our current genotype calling system (known as SNP Chart) is capable of calling single SNP genotypes by manual inspection of the APEX data, which is time-consuming and exposed to user subjectivity bias.     

Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal

Once genetic linkage has been identified for a complex disease, the next step is often association analysis, in which single-nucleotide polymorphisms (SNPs) within the linkage region are genotyped and tested for association with the disease. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained, in part or in full, by the candidate SNP. We propose a novel approach that quantifies the degree of linkage disequilibrium (LD) between the candidate SNP and the putative disease locus through joint modeling of linkage and association. We describe a simple likelihood of the marker data conditional on the trait data for a sample of affected sib pairs, with disease penetrances and disease-SNP haplotype frequencies as parameters. We estimate model parameters by maximum likelihood and propose two likelihood-ratio tests to characterize the relationship of the candidate SNP and the disease locus. The first test assesses whether the candidate SNP and the disease locus are in linkage equilibrium so that the SNP plays no causal role in the linkage signal. The second test assesses whether the candidate SNP and the disease locus are in complete LD so that the SNP or a marker in complete LD with it may account fully for the linkage signal. Our method also yields a genetic model that includes parameter estimates for disease-SNP haplotype frequencies and the degree of disease-SNP LD. Our method provides a new tool for detecting linkage and association and can be extended to study designs that include unaffected family members.     

Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tag data

We have developed a computer based method to identify candidate single nucleotide polymorphisms (SNPs) and small insertions/deletions from expressed sequence tag data. Using a redundancy-based approach, valid SNPs are distinguished from erroneous sequence by their representation multiple times in an alignment of sequence reads. A second measure of validity was also calculated based on the cosegregation of the SNP pattern between multiple SNP loci in an alignment. The utility of this method was demonstrated by applying it to 102,551 maize (Zea mays) expressed sequence tag sequences. A total of 14,832 candidate polymorphisms were identified with an SNP redundancy score of two or greater. Segregation of these SNPs with haplotype indicates that candidate SNPs with high redundancy and cosegregation confidence scores are likely to represent true SNPs. This was confirmed by validation of 264 candidate SNPs from 27 loci, with a range of redundancy and cosegregation scores, in four inbred maize lines. The SNP transition/transversion ratio and insertion/deletion size frequencies correspond to those observed by direct sequencing methods of SNP discovery and suggest that the majority of predicted SNPs and insertion/deletions identified using this approach represent true genetic variation in maize.     

Odds ratio-based genetic algorithms for generating SNP barcodes of genotypes to predict disease susceptibility

Genome-wide association analysis involving many single nucleotide polymorphisms (SNPs) data is challenging mathematically and computationally. It is time consuming to classify the combination of multilocus genotypes into high- and low-risk groups without false positive and negative errors. Hence, we propose the odds ratio-based genetic algorithms (OR-GA) method that uses the odds ratio as a new quantitative measure of disease risk among many SNP combinations. Genetic algorithms (GA) are applied to generate SNP "barcodes" of genotypes, which propose the maximal difference of occurrence between the case and control groups, to predict disease susceptibility (e.g., osteoporosis). When individuals are grouped into a low and high bone mass density (BMD) range, different SNP barcode patterns may occur several times in each of these two groups. Our results showed that a GA can effectively identify a specific SNP barcode with an optimized fitness value. SNP barcodes with a low fitness value will naturally be discarded from the population. A representative SNP barcode with a variable number of SNPs is processed by odds ratio analysis to determine the maximum difference between the low and high BMD groups in a statistical manner. Therefore, this paper introduces a powerful procedure for analysis of disease-associated SNP barcode in genome-wide genes.     

Investigation of inversion polymorphisms in the human genome using principal components analysis

Despite the significant advances made over the last few years in mapping inversions with the advent of paired-end sequencing approaches, our understanding of the prevalence and spectrum of inversions in the human genome has lagged behind other types of structural variants, mainly due to the lack of a cost-efficient method applicable to large-scale samples. We propose a novel method based on principal components analysis (PCA) to characterize inversion polymorphisms using high-density SNP genotype data. Our method applies to non-recurrent inversions for which recombination between the inverted and non-inverted segments in inversion heterozygotes is suppressed due to the loss of unbalanced gametes. Inside such an inversion region, an effect similar to population substructure is thus created: two distinct "populations" of inversion homozygotes of different orientations and their 1:1 admixture, namely the inversion heterozygotes. This kind of substructure can be readily detected by performing PCA locally in the inversion regions. Using simulations, we demonstrated that the proposed method can be used to detect and genotype inversion polymorphisms using unphased genotype data. We applied our method to the phase III HapMap data and inferred the inversion genotypes of known inversion polymorphisms at 8p23.1 and 17q21.31. These inversion genotypes were validated by comparing with literature results and by checking Mendelian consistency using the family data whenever available. Based on the PCA-approach, we also performed a preliminary genome-wide scan for inversions using the HapMap data, which resulted in 2040 candidate inversions, 169 of which overlapped with previously reported inversions. Our method can be readily applied to the abundant SNP data, and is expected to play an important role in developing human genome maps of inversions and exploring associations between inversions and susceptibility of diseases.     

Genetic Variation in TLR Genes in Ugandan and South African Populations and Comparison with HapMap Data

Genetic epidemiological studies of complex diseases often rely on data from the International HapMap Consortium for identification of single nucleotide polymorphisms (SNPs), particularly those that tag haplotypes. However, little is known about the relevance of the African populations used to collect HapMap data for study populations conducted elsewhere in Africa. Toll-like receptor (TLR) genes play a key role in susceptibility to various infectious diseases, including tuberculosis. We conducted full-exon sequencing in samples obtained from Uganda (n = 48) and South Africa (n = 48), in four genes in the TLR pathway: TLR2, TLR4, TLR6, and TIRAP. We identified one novel TIRAP SNP (with minor allele frequency [MAF] 3.2%) and a novel TLR6 SNP (MAF 8%) in the Ugandan population, and a TLR6 SNP that is unique to the South African population (MAF 14%). These SNPs were also not present in the 1000 Genomes data. Genotype and haplotype frequencies and linkage disequilibrium patterns in Uganda and South Africa were similar to African populations in the HapMap datasets. Multidimensional scaling analysis of polymorphisms in all four genes suggested broad overlap of all of the examined African populations. Based on these data, we propose that there is enough similarity among African populations represented in the HapMap database to justify initial SNP selection for genetic epidemiological studies in Uganda and South Africa. We also discovered three novel polymorphisms that appear to be population-specific and would only be detected by sequencing efforts.