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      Genetic diagnosis by whole exome capture and massively parallel DNA sequencing

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          Abstract

          Protein coding genes constitute only approximately 1% of the human genome but harbor 85% of the mutations with large effects on disease-related traits. Therefore, efficient strategies for selectively sequencing complete coding regions (i.e., "whole exome") have the potential to contribute to the understanding of rare and common human diseases. Here we report a method for whole-exome sequencing coupling Roche/NimbleGen whole exome arrays to the Illumina DNA sequencing platform. We demonstrate the ability to capture approximately 95% of the targeted coding sequences with high sensitivity and specificity for detection of homozygous and heterozygous variants. We illustrate the utility of this approach by making an unanticipated genetic diagnosis of congenital chloride diarrhea in a patient referred with a suspected diagnosis of Bartter syndrome, a renal salt-wasting disease. The molecular diagnosis was based on the finding of a homozygous missense D652N mutation at a position in SLC26A3 (the known congenital chloride diarrhea locus) that is virtually completely conserved in orthologues and paralogues from invertebrates to humans, and clinical follow-up confirmed the diagnosis. To our knowledge, whole-exome (or genome) sequencing has not previously been used to make a genetic diagnosis. Five additional patients suspected to have Bartter syndrome but who did not have mutations in known genes for this disease had homozygous deleterious mutations in SLC26A3. These results demonstrate the clinical utility of whole-exome sequencing and have implications for disease gene discovery and clinical diagnosis.

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          Most cited references12

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          Is Open Access

          The complete genome of an individual by massively parallel DNA sequencing.

          The association of genetic variation with disease and drug response, and improvements in nucleic acid technologies, have given great optimism for the impact of 'genomic medicine'. However, the formidable size of the diploid human genome, approximately 6 gigabases, has prevented the routine application of sequencing methods to deciphering complete individual human genomes. To realize the full potential of genomics for human health, this limitation must be overcome. Here we report the DNA sequence of a diploid genome of a single individual, James D. Watson, sequenced to 7.4-fold redundancy in two months using massively parallel sequencing in picolitre-size reaction vessels. This sequence was completed in two months at approximately one-hundredth of the cost of traditional capillary electrophoresis methods. Comparison of the sequence to the reference genome led to the identification of 3.3 million single nucleotide polymorphisms, of which 10,654 cause amino-acid substitution within the coding sequence. In addition, we accurately identified small-scale (2-40,000 base pair (bp)) insertion and deletion polymorphism as well as copy number variation resulting in the large-scale gain and loss of chromosomal segments ranging from 26,000 to 1.5 million base pairs. Overall, these results agree well with recent results of sequencing of a single individual by traditional methods. However, in addition to being faster and significantly less expensive, this sequencing technology avoids the arbitrary loss of genomic sequences inherent in random shotgun sequencing by bacterial cloning because it amplifies DNA in a cell-free system. As a result, we further demonstrate the acquisition of novel human sequence, including novel genes not previously identified by traditional genomic sequencing. This is the first genome sequenced by next-generation technologies. Therefore it is a pilot for the future challenges of 'personalized genome sequencing'.
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            Genome-wide association studies: theoretical and practical concerns.

            To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.
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              Multiple rare alleles contribute to low plasma levels of HDL cholesterol.

              Heritable variation in complex traits is generally considered to be conferred by common DNA sequence polymorphisms. We tested whether rare DNA sequence variants collectively contribute to variation in plasma levels of high density lipoprotein cholesterol (HDL-C). We sequenced three candidate genes (ABCA1, APOA1, and LCAT) that cause Mendelian forms of low HDL-C levels in individuals from a population-based study. Nonsynonymous sequence variants were significantly more common (16% versus 2%) in individuals with low HDL-C ( 95th percentile). Similar findings were obtained in an independent population, and biochemical studies indicated that most sequence variants in the low HDL-C group were functionally important. Thus, rare alleles with major phenotypic effects contribute significantly to low plasma HDL-C levels in the general population.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                PNAS
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                November 10 2009
                November 10 2009
                November 10 2009
                October 27 2009
                : 106
                : 45
                : 19096-19101
                Article
                10.1073/pnas.0910672106
                2768590
                19861545
                92d3870d-634d-4c71-8a51-96e32dbba053
                © 2009
                History

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