Global analysis of disease-related DNA sequence variation in 10 healthy individuals: Implications for whole genome-based clinical diagnostics : Genetics in Medicine

Global analysis of disease-related DNA sequence variation in 10 healthy individuals: Implications for whole genome-based clinical diagnostics
Moore, Barry MS1; Hu, Hao BS1; Singleton, Marc MS1; De La Vega, Francisco M. DSc2; Reese, Martin G. PhD3; Yandell, Mark PhD1

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Author Information
From the 1Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah School of Medicine, Salt Lake City, Utah; 2Research and Development, Genetic Systems Division, Life Technologies, Foster City, California; and 3Omicia, Inc., Emeryville, California

Mark Yandell, PhD, Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT. E-mail: myandell@genetics.utah.edu.

Disclosure: Barry Moore and Mark Yandell have worked as paid consultants for Omicia Inc. Francisco M. De La Vega was an employee and held stock options of Life Technologies when this research was conducted. The other authors declare no conflict of interest.

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Submitted for publication November 9, 2010.

Accepted for publication January 6, 2011.

Published online ahead of print February 15, 2011.

Abstract
Background: Understanding how sequence variants within healthy genomes are distributed with respect to ethnicity and disease-implicated genes is an essential first step toward establishing baselines for personalized genomic medicine.

Methods: In this study, we present an analysis of 10 genomes from healthy individuals of various ethnicities, produced using six different sequencing technologies. In total, these genomes contain more than 34 million single-nucleotide variants.

Results: We have analyzed these variants from a clinical perspective, assaying the influence of sequencing technology and ethnicity on prognosis. We have also examined the utility of OMIM and the disease-gene literature for determining the impact of rare, personal variants on an individual's health.

Conclusions: Our analyses demonstrate that clinical prognoses are complicated by sequencing platform-specific errors and ethnicity. We show that disease-causing alleles are globally distributed along ethnic lines, with alleles known to be disease causing in Eurasians being significantly more likely to be homozygous in Africans.

What does it mean to have a “healthy” genome? Neither J. Craig Venter's nor James Watson's genomes contain any alleles likely to cause or strongly predispose them to genetic illness.1,2 They are also not heterozygous for any alleles raising serious reproductive issues. Given these facts, some have expressed skepticism regarding the prognostic value of personal genome sequences.3,4 To date, the standard reply to the skeptic has been that healthy adults have healthy genomes. Although reasonable, this rebuttal presumes that we know what a healthy genome is. No doubt, a clean bill of genomic health will be the most common clinical scenario in genomic medicine. However, just what does a healthy genome look like? What is the impact of sequencing technology on prognostic accuracy? What role will ethnicity play in prognosis? Finally, how useful will existing resources, such as OMIM, be for categorizing personal genome variants as deleterious? The answers to these questions are of immediate importance for the future of genomic medicine.

To answer these questions, we have assembled a standardized dataset of single-nucleotide variants (SNVs), also called single nucleotide polymorphisms, from 10 personal genome sequences.1,2,5–11 These genomes represent both sexes and a variety of ethnic backgrounds:
three Africans, two Asians, and five whites. This second feature of the data set has allowed us to assay the impact of ethnicity on variant locations. Six different sequencing technologies are also represented. One genome was sequenced with Sanger technology,12 one using the Roche 454 platform (Life Sciences, Branford, CT), three with ABI SOLiD (Applied Biosystems/Life Technologies, Carlsbad, CA), three with Illumina Genome Analyzer (GA) (Illumina, San Diego, CA), one with Helicos Biosciences (Cambridge, MA), and one genome was sequenced by Complete Genomics (Mountain View, CA). Moreover, one individual's genome is represented twice, sequenced on both the Illumina GA and ABI SOLiD platforms.

The diversity of ethnic backgrounds and sequencing technologies used to produce this dataset make it ideal for investigations of the impact of sequencing platform on SNV ascertainment and of ethnicity on SNV distributions. To address the clinical relevance of these factors, we have carried out two additional large-scale analyses. First, we analyzed these genomes' 34 million variants with respect to their intersection with OMIM variants, which we have systematically mapped forward to the current genome assembly; this has made it possible to globally assay the distributions of OMIM alleles in different ethnic backgrounds. Additional clinical perspective is obtained through a second, complementary analysis using a disease-gene classification system based on MeSH13 and Harrison's Internal Medicine.14 This classification system is a first step toward comprehensive disease-gene ontology, and it has allowed us to examine individual genomic variation associated with specific areas of the clinical disease-gene literature, such as oncogenesis, neonatal development, and
cardiovascular disease.

Taken together, these analyses illustrate some of the challenges of whole genome variation analysis and provide a first glimpse of trends and distributions indicative of healthy genomes that will also be important for clinical prognosis of personal genome sequences

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Global analysis of disease-related DNA sequence variation in... : Genetics in Medicine