Faculty Bibliography

Determining linkage phase from population samples with statistical methods is accurate only within regions of high linkage disequilibrium (LD). Yet, affected individuals in a genetic mapping study, including those involving cases and controls, may share sequences identical-by-descent stretching on the order of 10s to 100s of kilobases, quite possibly over regions of low LD in the population. At the same time, inferring phase from nuclear families may be hampered by missing family members, missing genotypes, and the noninformativity of certain genotype patterns. In this study, we reformulate our previous haplotype reconstruction algorithm, and its associated computer program, to phase parents with information derived from population samples as well as from their offspring. In applications of our algorithm to 100-kb stretches, simulated in accordance to a Wright-Fisher model with typical levels of LD in humans, we find that phase reconstruction for 160 trios with 10% missing data is highly accurate (>90%) over the entire length. Furthermore, our algorithm can estimate allelic status for missing data at high accuracy (>95%). Finally, the input capacity of the program is vast, easily handling thousands of segregating sites in > or = 1000 chromosomes.

Somatic mitochondrial mutations are common in human cancers, and can be used as a tool for early detection of cancer. We have developed a mitochondrial Custom Reseq microarray as an array-based sequencing platform for rapid and high-throughput analysis of mitochondrial DNA. The MitoChip contains oligonucleotide probes synthesized using standard photolithography and solid-phase synthesis, and is able to sequence >29 kb of double-stranded DNA in a single assay. Both strands of the entire human mitochondrial coding sequence (15,451 bp) are arrayed on the MitoChip; both strands of an additional 12,935 bp (84% of coding DNA) are arrayed in duplicate. We used 300 ng of genomic DNA to amplify the mitochondrial coding sequence in three overlapping long PCR fragments. We then sequenced >2 million base pairs of mitochondrial DNA, and successfully assigned base calls at 96.0% of nucleotide positions. Replicate experiments demonstrated >99.99% reproducibility. In matched fluid samples (urine and pancreatic juice, respectively) obtained from five patients with bladder cancer and four with pancreatic cancer, the MitoChip detected at least one cancer-associated mitochondrial mutation in six (66%) of nine samples. The MitoChip is a high-throughput sequencing tool for the reliable identification of mitochondrial DNA mutations from primary tumors in clinical samples.

Sudden cardiac death (SCD) remains a public health problem of major magnitude. Contrary to earlier expectations, and despite decreased overall cardiac mortality, SCD rates appear to be rising in concert with escalating global prevalence of coronary disease and heart failure, the two major conditions predisposing to SCD. With the exception of the implantable defibrillator, there are few effective approaches to SCD prevention and even fewer clues concerning patient phenotypes predisposed to life-threatening arrhythmias. Clinical variables such as ejection fraction predict mortality but are not sensitive enough to identify many high SCD risk patients. The predictive power of autonomic dysregulation and markers such as lipid levels, hypertension, diabetes, and smoking is quite low in subclinical heart disease, the population in which the majority of SCDs occur. This review addresses advances in genomic science applicable to the SCD public health problem in both rare and common forms of heart disease. These include novel bioinformatic approaches to both identify candidate genes/pathways and identify previously unknown functional genetic elements, as well as methods to comprehensively screen these elements. We also discuss the possibility of applying high-density genome-wide SNP analyses to examine genetic contributions to arrhythmia susceptibility in community-based, case-control studies of common forms of SCD. The development of novel strategies to identify contributors to susceptibility in common cardiac phenotypes is most likely to lead to new and relevant therapeutic targets for SCD.

The spontaneous autosomal recessive mouse mutant for hydrocephaly with hop gait (hyh) exhibits dramatic cystic dilation of the ventricles at birth and invariably develops hopping gait. We show that the gene for soluble N-ethylmaleimide sensitive factor attachment protein alpha, also known as alpha-SNAP, is mutated in hyh mice. alpha-SNAP plays a key role in a wide variety of membrane fusion events in eukaryotic cells, including the regulated exocytosis of neurotransmitters. Homozygous mutant mice harbor a missense mutation M105I in a conserved residue in one of the alpha-helical domains. We demonstrate that the hyh mutant is not a null allele and is expressed; however, the mutant protein is 40% less abundant in hyh mice. The hyh mutant provides a valuable in vivo model to study vesicle/membrane trafficking and provides insight into the potential roles of alpha-SNAP in embryogenesis and brain development.

Certain congenital disorders that are rare in the general population are quite common in individuals with trisomic conditions. For example, complete atrioventricular septal defect occurs in about 20% of individuals with Down syndrome, an approximately 500-fold increase in risk as compared to individuals without Down syndrome. Genetic variation on the chromosome involved in the trisomy may affect susceptibility to these trisomy-specific disorders. That is, increased dosage of a variant may be directly involved in increasing the risk of a disorder, or it may be indirectly involved by causing up- or downregulation of other genes. As in standard disomic gene-mapping, one can search for genes using linkage or association methods. Within association methods, one can consider case-control methods or family-based control methods such as the transmission disequilibrium test (TDT). Most gene-mapping methods need to be substantially redesigned for use with trisomic data. In this paper, we present a "trisomic TDT", a statistical method of testing for nonrandom transmission of alleles from parents to trisomic children. We demonstrate the method on a dataset of parent-child trios in which the child has Down syndrome.

Chromosome 2 has been consistently identified as a genomic region with genetic linkage evidence suggesting that one or more loci contributes to blood pressure and hypertension status. As with all complex disease traits, following-up linkage evidence to identify the underlying susceptibility gene(s) is an arduous yet biologically and clinically important task. Using combined positional candidate gene methods, the Family Blood Pressure Program (FBPP) has concentrated efforts in narrowing a large region of chromosome 2, demonstrating evidence for linkage in several populations, and identifying underlying candidate hypertension susceptibility gene(s). Initial informatics efforts identified the boundaries of the region and the known genes within it. A total of 82 polymorphic sites in 8 genes were genotyped in a large hypothesis-generating sample consisting of 1640 African Americans, 1339 whites, and 1616 Mexican Americans. After resampling-based false discovery adjustment, SLC4A5, a sodium bicarbonate transporter, was identified as a primary candidate gene for hypertension. Polymorphisms in SLC4A5 were subsequently genotyped and analyzed for validation in two other subcomponents of the FBPP, each contributing African Americans (N=461; N=778) and whites (N=550; N=967). Again, single nucleotide polymorphisms within this gene were significantly associated with blood pressure levels and hypertension status. Although not identifying a single causal gene variant that is significantly associated with blood pressure levels and hypertension status across all samples, the results further implicate SLC4A5 as a candidate hypertension susceptibility gene. Moreover, the present study validates previous evidence for one or more genes on chromosome 2 that influence hypertension-related phenotypes in the population-at-large.

SUMMARY: Three recent publications have examined the quality and completeness of public database single nucleotide polymorphism (dbSNP) and have come to dramatically different conclusions regarding dbSNPs false positive rate and the proportion of dbSNPs that are expected to be common. These studies employed different genotyping technologies and different protocols in determining minimum acceptable genotyping quality thresholds. Because heterozygous sites typically have lower quality scores than homozygous sites, a higher minimum quality threshold reduces the number of false positive SNPs, but yields fewer heterozygotes and leads to fewer confirmed SNPs. To account for the different confirmation rates and distributions of minor allele frequencies, we propose that the three confirmation studies have different false positive and false negative rates. We developed a mathematical model to predict SNP confirmation rates and the apparent distribution of minor allele frequencies under user-specified false positive and false negative rates. We applied this model to the three published studies and to our own resequencing effort. We conclude that the dbSNP false positive rate is approximately 15-17% and that the reported confirmation studies have vastly different genotyping error rates and patterns

The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.

Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.