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The value of regenotyping older linkage data sets with denser marker panels.

ABSTRACT: Linkage analysis can help determine regions of interest in whole-genome sequence studies. However, many linkage studies rely on older microsatellite (MSAT) panels. We set out to determine whether results would change if we regenotyped families using a dense map of SNPs.We selected 47 Hispanic-American families from the NIMH Repository and Genomics Resource (NRGR) schizophrenia data repository. We regenotyped all individuals with DNA available from the NRGR on the Affymetrix Lat Array. After optimizing SNP selection for inclusion on the linkage map, we compared information content (IC) and linkage results using MSAT, SNP and MSAT+SNP maps.As expected, SNP provided a higher average IC (0.78, SD 0.03) than MSAT (0.51, SD 0.10) in a direct 'apples-to-apples' comparison using only individuals genotyped on both platforms; while MSAT+SNP provided only a slightly higher IC (0.82, SD 0.03). However, when utilizing all available individuals, including those who had genotypes available on only one platform, the IC was substantially increased using MSAT+SNP (0.76, SD 0.05) compared to SNP (0.61, SD 0.02). Linkage results changed appreciably between MSAT and MSAT+SNP in terms of magnitude, rank ordering and localization of peaks.Regenotyping older family data can substantially alter the conclusions of linkage analyses.

SUBMITTER: Vieland VJ

PROVIDER: S-EPMC4151881 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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The value of regenotyping older linkage data sets with denser marker panels.

Vieland Veronica J VJ Walters Kimberly A KA Azaro Marco M Brzustowicz Linda M LM Lehner Thomas T

Human heredity 20140621 1

<h4>Objectives</h4>Linkage analysis can help determine regions of interest in whole-genome sequence studies. However, many linkage studies rely on older microsatellite (MSAT) panels. We set out to determine whether results would change if we regenotyped families using a dense map of SNPs.<h4>Methods</h4>We selected 47 Hispanic-American families from the NIMH Repository and Genomics Resource (NRGR) schizophrenia data repository. We regenotyped all individuals with DNA available from the NRGR on t ...[more]

PMID: 24969307

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Project description:BackgroundDue to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively.ResultsIn addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16.ConclusionThis study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.

Dataset Information

The value of regenotyping older linkage data sets with denser marker panels.

Publications

The value of regenotyping older linkage data sets with denser marker panels.

Similar Datasets

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