Methods and processes for non invasive assessment of a genetic variation

Abstract

Provided in part herein are methods and processes that can be used for non-invasive assessment of a genetic variation which can lead to diagnosis of a particular medical condition or conditions. Such methods and processes can, for example, identify dissimilarities or similarities for one or more features between a subject data set and a reference data set, generate a multidimensional matrix, reduce the matrix into a representation and classify the representation into one or more groups. Methods and processes described herein are applicable to data in biotechnology and other fields.

1 - 63 . (canceled) 
     
     
         64 . A method for non-invasive prenatal assessment of a fetal genetic variation comprising:
 (a) obtaining genomic nucleic acid sequence information of a sample from a subject and obtaining genomic nucleic acid sequence information of a biological specimen from one or more reference persons;   (b) identifying one or more dissimilarities for a feature between a subject data set and a reference data set by a statistical analysis wherein the subject data set comprises genomic nucleic acid sequence information of the sample from the subject and the reference data set comprises genomic nucleic acid sequence information of the biological specimen from the one or more reference persons, wherein the statistical analysis is a paired t-test;   (c) generating, using a microprocessor, a multidimensional matrix from the dissimilarities;   (d) reducing the multidimensional matrix into a reduced data set representation of the matrix;   (e) classifying into one or more groups the reduced data set representation by one or more linear modeling analysis algorithms thereby providing a classification; and   (f) determining the presence or absence of a fetal genetic variation for the sample based on the classification, wherein a statistical sensitivity and a statistical specificity is determined from the classification.   
     
     
         65 . The method of  claim 64 , wherein the fetal genetic variation is fetal gender. 
     
     
         66 . The method of  claim 64 , wherein the one or more reference persons comprises one or more female or male samples. 
     
     
         67 . The method of  claim 64 , wherein the fetal genetic variation is a fetal aneuploidy. 
     
     
         68 . The method of  claim 64 , comprising obtaining genomic nucleic acid reads and mapping the reads to a portion of the reference genome. 
     
     
         69 . The method of  claim 64 , comprising isolating genomic nucleic acid from the sample from the subject and from the biological specimen from the one or more reference persons. 
     
     
         70 . The method of  claim 64 , wherein the sample from the subject and some or all of the biological specimens from the one or more reference persons are from different persons. 
     
     
         71 . The method of  claim 64 , wherein some or all of the biological specimens from the one or more reference persons are from the same person. 
     
     
         72 . The method of  claim 69 , wherein the genomic nucleic acid is from blood, serum or blood plasma from the subject. 
     
     
         73 . The method of  claim 68 , wherein the nucleic acid sequence reads are from a multiplex sequence analysis. 
     
     
         74 . The method of  claim 64 , comprising reiterating the identificating of the one or more dissimilarities in a pairwise analysis between each pair in the subject data set and the reference data set. 
     
     
         75 . The method of  claim 64 , wherein identifying one or more dissimilarities in (b) comprises employing one or more of a decision tree, counternull, multiple comparisons, omnibus test, Behrens-Fisher problem, bootstrapping, Fisher's method for combining independent tests of significance, null hypothesis, type I error, type II error, exact test, one-sample Z test, two-sample Z test, paired Z-test, one-sample t-test, paired t-test, two-sample pooled t-test having equal variances, two-sample unpooled t-test having unequal variances, one-proportion z-test, two-proportion z-test pooled, two-proportion z-test unpooled, one-sample chi-square test, two-sample F test for equality of variances, confidence interval, credible interval, significance, meta analysis, simple linear regression, robust linear regression, and combination thereof. 
     
     
         76 . The method of claim  63 , wherein reducing the multidimensional matrix in (d) comprises employing one or more of metric and non-metric multi-dimensional scaling, Sammon's non-linear mapping, principle component analysis and combinations thereof. 
     
     
         77 . The method of  claim 64 , wherein the classifying in (e) comprises employing one or more of analysis of variance, Anscombe's quartet, cross-sectional regression, curve fitting, empirical Bayes methods, M-estimator, nonlinear regression, linear regression, multivariate adaptive regression splines, lack-of-fit sum of squares, truncated regression model, censored regression model, simple linear regression, segmented linear regression, decision tree, k-nearest neighbor, supporter vector machine, neural network, linear discriminant analysis, quadratic discriminant analysis, and combinations thereof. 
     
     
         78 . The method of  claim 77 , comprising determining a statistical sensitivity and a statistical specificity from the classification. 
     
     
         79 . The method of  claim 64 , wherein the statistical sensitivity and statistical specificity are independently between about 85% and about 100%. 
     
     
         80 . The method of  claim 64 , wherein the identifying one or more dissimilarities in (b) comprises determining a linear relationship. 
     
     
         81 . The method of  claim 80 , wherein one of the one or more dissimilarities in (b) is distance of a feature from the linear relationship. 
     
     
         82 . The method of  claim 64 , wherein the dissimilarities are Z-scores. 
     
     
         83 . The method of  claim 82 , wherein the multidimensional matrix in (c) comprises pairwise dissimilarities between samples of the Z-scores. 
     
     
         84 . The method of  claim 64 , wherein the subject data set and the reference data set comprise sequence tag information. 
     
     
         85 . The method of  claim 64 , comprising quantifying a signal or a tag using a technique selected from the group consisting of flow cytometry, quantitative polymerase chain reaction (qPCR), gel electrophoresis, gene-chip analysis, microarray, mass spectrometry, cytofluorimetric analysis, fluorescence microscopy, confocal laser scanning microscopy, laser scanning cytometry, affinity chromatography, manual batch mode separation, electric field suspension, sequencing, and combination thereof.