METHOD AND SYSTEM FOR RAPID GENETIC ANALYSIS

• US 20190325988A1
• Filed: 04/18/2019
• Published: 10/24/2019
• Est. Priority Date: 04/18/2018
• Status: Active Application

• Current Assignee: Rady Children's Hospital Research Center (RADY Children's Hospital-San Diego)
• Original Assignee: Rady Children's Hospital Research Center (RADY Children's Hospital-San Diego)
• Inventors: Kingsmore, Stephen, Veeraraghavan, Narayanan, Clark, Michelle Marie
• Application Number:
  US16/388,614
• Publication Number:
  US 20190325988A1
• Time in Patent Office: Days
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2600/172 Haplotypes
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present disclosure provides a method for genetic analysis disease diagnoses as well as a system for implementing such analysis.

Claims

1. A method comprising:

a) determining a phenome of a subject from an electronic medical record (EMR), wherein the phenome comprises a plurality of clinical phenotypes extracted from the EMR;

b) translating the clinical phenotypes into standardized vocabulary;

c) generating a first list of potential differential diagnoses of the subject;

d) performing genetic sequencing of a DNA sample from the subject;

e) determining genetic variants of the DNA;

f) analyzing the results of (c) and (e) to generate a second list of potential differential diagnoses of the subject, the second list being rank ordered; and

g) generating a report comprising results of the analysis of (f).

2. The method of claim 1, further comprising generating the EMR for the subject prior to (a).

3. The method of claim 1, wherein (b) utilizes natural language processing to perform the translation.

4. The method of claim 1, wherein (a)-(c) and (d)-(e) are performed in parallel.

5. The method of claim 1, wherein genetic sequencing comprises rapid whole genome sequencing (rWGS), ultra-rapid whole genome sequencing, or rapid whole exome sequencing (rWES).

6. The method of claim 5, wherein the DNA sample is from a biological sample.

7. The method of claim 6, wherein the sample is serum, saliva, buccal smear/swab, plasma, feces, cerebrospinal fluid or urine.

8. The method of claim 6, wherein the sample is blood.

9. The method of claim 6, wherein the biological sample is a dried blood spot.

10. The method of claim 5, wherein genetic sequence comprises rWGS and rWES.

11. The method of claim 1, wherein the ranked list is performed via query of a database populated with known clinical phenotypes expressed in the same vocabulary as the standardized vocabulary of (b).

12. The method of claim 1, wherein determining genetic variants of (f) further comprises annotation and classification of the genetic variants.

13. The method of claim 12, wherein the genetic variants are utilized to generate a probabilistic diagnosis.

14. The method of claim 12, wherein the genetic variants are annotated and classified as being of uncertain significance (VUS), pathogenic (P) or likely pathogenic (LP).

15. The method of claim 12, wherein only genetic variants with an allele frequency of <

5%, 2.5%, 1%, 0.1% or less in a population of healthy individuals is retained.

16. The method of claim 15, wherein determining genetic variants of (e) further comprises annotation of the genetic variants to identify and rank all diplotypes as being of uncertain significance (VUS), pathogenic (P) or likely pathogenic (LP) on the basis of pathogenicity.

17. The method of claim 16, wherein the second list of potential differential diagnoses is generated by comparing the annotated VUS, LP and P diplotypes on a regional genomic basis with corresponding genomic regions associated with the first list of potential differential diagnoses of (c).

18. The method of claim 17, wherein the genetic variants are ranked based on a combination of rank of goodness of fit of clinical phenotypes, rank of pathogenicity of diplotypes, and/or allele frequencies of the genetic variants in a population of health individuals.

19. The method of claim 1, further comprising performing genetic sequencing of a DNA sample from a biological parent of the subject.

20. The method of claim 19, wherein genetic sequencing is performed for both biological parents and only results in which trio diplotypes fit a known inheritance pattern of a specific genetic disease are obtained.

21. The method of claim 19, wherein genetic sequencing is performed for both biological parents, wherein parental health status (healthy or affected) is used to obtain only results in which parental diplotypes fit a known inheritance pattern of a specific genetic disease.

22. The method of claim 19, wherein genetic variants present in the subject'"'"'s genome and not in the parental genome are utilized to determine a diagnosis for the subject.

23. The method of claim 1, wherein genetic sequence comprises sequencing of a whole genome, whole exome, or gene panel.

24. The method of claim 1, wherein the subject is less than 5 years old.

25. The method of claim 24, wherein the subject is an infant, fetus or neonate.

26. The method of claim 1, wherein the potential differential diagnoses comprise genetic diseases.

27. The method of claim 1, wherein the method is automated.

28. The method of claim 1, further comprising generating a therapy regime for the subject based on (g).

29. The method of claim 1, further comprising providing a therapy to the subject.

30. The method of claim 1, wherein (a) further comprises analyzing supplemental clinical information to determine the phenome.

31. The method of claim 1, wherein (a) is performed for a plurality of subjects thereby generating a plurality of EMRs, a plurality of phenomes, and a plurality of clinical phenotypes.

32. The method of claim 2, wherein (a) is performed for a plurality of subjects thereby generating a plurality of EMRs, a plurality of phenomes, and a plurality of clinical phenotypes.

33. The method of claim 31, further comprising storing on a non-transitory memory the plurality of EMRs, the plurality of phenomes, and the plurality of clinical phenotypes to generate a searchable database.

34. The method of claim 32, further comprising storing on a non-transitory memory the plurality of EMRs, the plurality of phenomes, and the plurality of clinical phenotypes to generate a searchable database.

35. The method of claim 33, further comprising utilizing the database to screen for genetic data, a genotype, or a disease or disorder in a second subject or to update a diagnosis of the subject.

36. The method of claim 34, further comprising utilizing the database to screen for genetic data, a genotype, or a disease or disorder in a second subject or to update a diagnosis of the subject.

37. A system comprising:

a controller including at least one processor and non-transitory memory, wherein the controller is configured to perform (a)-(c) and (e)-(g) of claim 1.

38. A method comprising:

a) generating a plurality of electronic medical records (EMRs) for a plurality of subjects;

b) determining a plurality of phenomes of the plurality of subjects from the EMRs using natural language processing, wherein the phenomes each comprise a plurality of clinical phenotypes extracted from each of the EMRs; and

c) storing on a non-transitory memory the plurality of EMRs, the plurality of phenomes, and the plurality of clinical phenotypes to generate a searchable database;

d) utilizing the database to screen for a disease or disorder in a new subject or to update a diagnosis of one of the plurality of subjects.

39. A system comprising:

a controller including at least one processor and non-transitory memory, wherein the controller is configured to perform (a)-(c) of claim 38.