Earl Hubbell

Accessing genetic information with high-density DNA arrays

Rapid access to genetic information is central to the revolution taking place in molecular genetics. The simultaneous analysis of the entire human mitochondrial genome is described here. DNA arrays containing up to 135,000 probes complementary to the 16.6-kilobase human mitochondrial genome were generated by light-directed chemical synthesis. A two-color labeling scheme was developed that allows simultaneous comparison of a polymorphic target to a reference DNA or RNA. Complete hybridization patterns were revealed in a matter of minutes. Sequence polymorphisms were detected with single-base resolution and unprecedented efficiency. The methods described are generic and can be used to address a variety of questions in molecular genetics including gene expression, genetic linkage, and genetic variability.

Integrated detection and population-genetic analysis of SNPs and copy number variation

Dissecting the genetic basis of disease risk requires measuring all forms of genetic variation, including SNPs and copy number variants (CNVs), and is enabled by accurate maps of their locations, frequencies and population-genetic properties. We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs) that segregate at an allele frequency >1%. More than 80% of the sequence in previously reported CNV regions fell outside our estimated CNV boundaries, indicating that large (>100 kb) CNVs affect much less of the genome than initially reported. Approximately 80% of observed copy number differences between pairs of individuals were due to common CNPs with an allele frequency >5%, and more than 99% derived from inheritance rather than new mutation. Most common, diallelic CNPs were in strong linkage disequilibrium with SNPs, and most low-frequency CNVs segregated on specific SNP haplotypes.

Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs.

Accurate and complete measurement of single nucleotide (SNP) and copy number (CNV) variants, both common and rare, will be required to understand the role of genetic variation in disease. We present Birdsuite, a four-stage analytical framework instantiated in software for deriving integrated and mutually consistent copy number and SNP genotypes. The method sequentially assigns copy number across regions of common copy number polymorphisms (CNPs), calls genotypes of SNPs, identifies rare CNVs via a hidden Markov model (HMM), and generates an integrated sequence and copy number genotype at every locus (for example, including genotypes such as A-null, AAB and BBB in addition to AA, AB and BB calls). Such genotypes more accurately depict the underlying sequence of each individual, reducing the rate of apparent mendelian inconsistencies. The Birdsuite software is applied here to data from the Affymetrix SNP 6.0 array. Additionally, we describe a method, implemented in PLINK, to utilize these combined SNP and CNV genotypes for association testing with a phenotype.

Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays

We present a genotyping method for simultaneously scoring 116,204 SNPs using oligonucleotide arrays. At call rates >99%, reproducibility is >99.97% and accuracy, as measured by inheritance in trios and concordance with the HapMap Project, is >99.7%. Average intermarker distance is 23.6 kb, and 92% of the genome is within 100 kb of a SNP marker. Average heterozygosity is 0.30, with 105,511 SNPs having minor allele frequencies >5%.

Genome-wide mapping with biallelic markers in Arabidopsis thaliana

Single-nucleotide polymorphisms, as well as small insertions and deletions (here referred to collectively as simple nucleotide polymorphisms, or SNPs), comprise the largest set of sequence variants in most organisms. Positional cloning based on SNPs may accelerate the identification of human disease traits and a range of biologically informative mutations. The recent application of high-density oligonucleotide arrays to allele identification has made it feasible to genotype thousands of biallelic SNPs in a single experiment. It has yet to be established, however, whether SNP detection using oligonucleotide arrays can be used to accelerate the mapping of traits in diploid genomes. The cruciferous weed Arabidopsis thaliana is an attractive model system for the construction and use of biallelic SNP maps. Although important biological processes ranging from fertilization and cell fate determination to disease resistance have been modelled in A. thaliana, identifying mutations in this organism has been impeded by the lack of a high-density genetic map consisting of easily genotyped DNA markers. We report here the construction of a biallelic genetic map in A. thaliana with a resolution of 3.5 cM and its use in mapping Eds16, a gene involved in the defence response to the fungal pathogen Erysiphe orontii. Mapping of this trait involved the high-throughput generation of meiotic maps of F2 individuals using high-density oligonucleotide probe array-based genotyping. We developed a software package called InterMap and used it to automatically delimit Eds16 to a 7-cM interval on chromosome 1. These results are the first demonstration of biallelic mapping in diploid genomes and establish means for generalizing SNP-based maps to virtually any genetic organism.

Probe selection for high-density oligonucleotide arrays

High-density oligonucleotide microarrays enable simultaneous monitoring of expression levels of tens of thousands of transcripts. For accurate detection and quantitation of transcripts in the presence of cellular mRNA, it is essential to design microarrays whose oligonucleotide probes produce hybridization intensities that accurately reflect the concentration of original mRNA. We present a model-based approach that predicts optimal probes by using sequence and empirical information. We constructed a thermodynamic model for hybridization behavior and determined the influence of empirical factors on the effective fitting parameters. We designed Affymetrix GeneChip probe arrays that contained all 25-mer probes for hundreds of human and yeast transcripts and collected data over a 4,000-fold concentration range. Multiple linear regression models were built to predict hybridization intensities of each probe at given target concentrations, and each intensity profile is summarized by a probe response metric. We selected probe sets to represent each transcript that were optimized with respect to responsiveness, independence (degree to which probe sequences are nonoverlapping), and uniqueness (lack of similarity to sequences in the expressed genomic background). We show that this approach is capable of selecting probes with high sensitivity and specificity for high-density oligonucleotide arrays.

Next generation genome-wide association tool: Design and coverage of a high-throughput European-optimized SNP array

The success of genome-wide association studies has paralleled the development of efficient genotyping technologies. We describe the development of a next-generation microarray based on the new highly-efficient Affymetrix Axiom genotyping technology that we are using to genotype individuals of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health (RPGEH). The array contains 674,517 SNPs, and provides excellent genome-wide as well as gene-based and candidate-SNP coverage. Coverage was calculated using an approach based on imputation and cross validation. Preliminary results for the first 80,301 saliva-derived DNA samples from the RPGEH demonstrate very high quality genotypes, with sample success rates above 94% and over 98% of successful samples having SNP call rates exceeding 98%. At steady state, we have produced 462 million genotypes per week for each Axiom system. The new array provides a valuable addition to the repertoire of tools for large scale genome-wide association studies.

Combinatorial Algorithms for Design of DNA Arrays

Optimal design of DNA arrays requires the development of algorithms with two-fold goals: reducing the effects caused by unintended illumination (border length minimization problem) and reducing the complexity of masks (mask decomposition problem). We describe algorithms that reduce the number of rectangles in mask decomposition by 20-30% as compared to a standard array design under the assumption that the arrangement of oligonucleotides on the array is fixed. This algorithm produces provably optimal solution for all studied real instances of array design. We also address the difficult problem of finding an arrangement which minimizes the border length and come up with a new idea of threading that significantly reduces the border length as compared to standard designs.

High-throughput microarray-based genotyping

A high throughput genotyping platform that scores over 10,000 single-nucleotide polymorphisms (SNPs) per individual on a single GeneChip/spl reg/ high-density oligonucleotide microarray has been developed to conduct studies to elucidate the genetic basis for complex diseases. Currently, the genotyping of individual SNPs relies on the summary statistics (based on the observed intensities of probes on the microarray) for the entirety of the sample set. A classification scheme of those statistics across hundreds of individual DNA samples is obtained to make individual genotyping calls. In contrast to the current approach, the method in this work makes individual genotyping call by finding the minimum residual, i.e. highest likelihood, among four possible states corresponding to three genotypes and no-call. Initially, the residual is calculated based on a given set of probe affinities for that individual sample. Auspiciously, the multitude of samples was utilized to iterate to refine the probe affinities, sample concentration, and background intensities. These refined parameters entail an improved call rate while maintaining high accuracy.

DNA copy number analysis in gastrointestinal stromal tumors using gene expression microarrays.

We report a method, Expression-Microarray Copy Number Analysis (ECNA) for the detection of copy number changes using Affymetrix Human Genome U133 Plus 2.0 arrays, starting with as little as 5 ng input genomic DNA. An analytical approach was developed using DNA isolated from cell lines containing various X-chromosome numbers, and validated with DNA from cell lines with defined deletions and amplifications in other chromosomal locations. We applied this method to examine the copy number changes in DNA from 5 frozen gastrointestinal stromal tumors (GIST). We detected known copy number aberrations consistent with previously published results using conventional or BAC-array CGH, as well as novel changes in GIST tumors. These changes were concordant with results from Affymetrix 100K human SNP mapping arrays. Gene expression data for these GIST samples had previously been generated on U133A arrays, allowing us to explore correlations between chromosomal copy number and RNA expression levels. One of the novel aberrations identified in the GIST samples, a previously unreported gain on 1q21.1 containing the PEX11B gene, was confirmed in this study by FISH and was also shown to have significant differences in expression pattern when compared to a control sample. In summary, we have demonstrated the use of gene expression microarrays for the detection of genomic copy number aberrations in tumor samples. This method may be used to study copy number changes in other species for which RNA expression arrays are available, e.g. other mammals, plants, etc., and for which SNPs have not yet been mapped.