sequencing of maternal plasma to detect Down syndrome: An international clinical validation study

1.	Deep impact: deciphering mucosal microbiomes using next-generation sequencing approaches.
	[No authors listed]
	Mucosal Immunol. 2011 Nov;4(6):586-7. doi: 10.1038/mi.2011.46. No abstract available.
	PMID: 22005879 [PubMed - in process]
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2.	DNA sequencing of maternal plasma to detect Down syndrome: An international clinical validation study.
	Palomaki GE, Kloza EM, Lambert-Messerlian GM, Haddow JE, Neveux LM, Ehrich M, van den Boom D, Bombard AT, Deciu C, Grody WW, Nelson SF, Canick JA.
	Genet Med. 2011 Oct 14. [Epub ahead of print]
	PMID: 22005709 [PubMed - as supplied by publisher] Abstract PURPOSE: Prenatal screening for Down syndrome has improved, but the number of resulting invasive diagnostic procedures remains problematic. Measurement of circulating cell-free DNA in maternal plasma might offer improvement. METHODS: A blinded, nested case-control study was designed within a cohort of 4664 pregnancies at high risk for Down syndrome. Fetal karyotyping was compared with an internally validated, laboratory-developed test based on next-generation sequencing in 212 Down syndrome and 1484 matched euploid pregnancies. None had been previously tested. Primary testing occurred at a CLIA-certified commercial laboratory, with cross validation by a CLIA-certified university laboratory. RESULTS: Down syndrome detection rate was 98.6% (209/212), the false-positive rate was 0.20% (3/1471), and the testing failed in 13 pregnancies (0.8%); all were euploid. Before unblinding, the primary testing laboratory also reported multiple alternative interpretations. Adjusting chromosome 21 counts for guanine cytosine base content had the largest impact on improving performance. CONCLUSION: When applied to high-risk pregnancies, measuring maternal plasma DNA detects nearly all cases of Down syndrome at a very low false-positive rate. This method can substantially reduce the need for invasive diagnostic procedures and attendant procedure-related fetal losses. Although implementation issues need to be addressed, the evidence supports introducing this testing on a clinical basis.
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3.	Assessing the Impact of Non-Differential Genotyping Errors on Rare Variant Tests of Association.
	Powers S, Gopalakrishnan S, Tintle N.
	Hum Hered. 2011 Oct 15;72(3):152-159. [Epub ahead of print]
	PMID: 22004945 [PubMed - as supplied by publisher] Abstract Background/Aims: We aim to quantify the effect of non-differential genotyping errors on the power of rare variant tests and identify those situations when genotyping errors are most harmful. Methods: We simulated genotype and phenotype data for a range of sample sizes, minor allele frequencies, disease relative risks and numbers of rare variants. Genotype errors were then simulated using five different error models covering a wide range of error rates. Results: Even at very low error rates, misclassifying a common homozygote as a heterozygote translates into a substantial loss of power, a result that is exacerbated even further as the minor allele frequency decreases. While the power loss from heterozygote to common homozygote errors tends to be smaller for a given error rate, in practice heterozygote to homozygote errors are more frequent and, thus, will have measurable impact on power. Conclusion: Error rates from genotype-calling technology for next-generation sequencing data suggest that substantial power loss may be seen when applying current rare variant tests of association to called genotypes.
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4.	Transcriptome map of mouse isochores.
	Arhondakis S, Frousios K, Iliopoulos CS, Pissis SP, Tischler G, Kossida S.
	BMC Genomics. 2011 Oct 17;12(1):511. [Epub ahead of print]
	PMID: 22004510 [PubMed - as supplied by publisher] Abstract ABSTRACT: BACKGROUND: The availability of fully sequenced genomes and the implementation of transcriptome technologies have increased the studies investigating the expression profiles for a variety of tissues, conditions, and species. In this study, using RNA-seq data for three distinct tissues (brain, liver, and muscle), we investigate how base composition affects mammalian gene expression, an issue of prime practical and evolutionary interest. RESULTS: We present the transcriptome map of the mouse isochores (DNA segments with a fairly homogeneous base composition) for the three different tissues and the effects of isochores' base composition on their expression activity. Our analyses also cover the relations between the genes' expression activity and their localization in the isochore families. CONCLUSIONS: This study is the first where next-generation sequencing data are used to associate the effects of both genomic and genic compositional properties to their corresponding expression activity. Our findings confirm previous results, and further support the existence of a relationship between isochores and gene expression. This relationship corroborates that isochores are primarily a product of evolutionary adaptation rather than a simple by-product of neutral evolutionary processes.
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