# US 10,865,446 B2 · DNA methylation biomarkers of post-partum depression risk > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,865,446 B2 - Document ID: US10865446B2 - Status: Granted - Kind: B2 - Application number: 14/439,468 - Filed: 2013-11-04 - Published: 2015-10-22 - Granted: 2020-12-15 - Earliest claimed: 2012-11-02 - Assignees: The Johns Hopkins University; University of Maryland, Baltimore - Applicants: The Johns Hopkins University; University of Maryland - Inventors: Zachary Kaminsky; Jennifer L. Payne; Todd Gould - Web page: https://patentgrep.com/patent/US10865446B2 ## CPC - C12Q 1/6883: for diseases caused by alterations of genetic material - G01N 15/10: Investigating individual particles - G16H 50/30: for calculating health indices; for individual health risk assessment - C12Q 2600/118: Prognosis of disease development - C12Q 2600/154: Methylation markers - C12Q 2600/158: Expression markers - C12Q 2600/16: Primer sets for multiplex assays - G01N 2015/016: {White blood cells} - G01N 2015/1024: {Counting particles by non-optical means} - C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The present invention relates to the field of post-partum depression. More specifically, the present invention relates to the use of biomarkers to diagnose post-partum depression or predict a risk thereof. In a specific embodiment, a method for identifying a likelihood of PPD in a patient comprises the steps of (a) providing a sample from the patient; (b) measuring white blood cell type counts and DNA methylation levels of a panel of biomarkers in the sample collected from the patient, wherein the panel of biomarkers comprises HP1BP3 and TTC9B and the white blood cell type counts comprise monocytes and non-monocytes; and (c) identifying the patient as likely to develop PPD based on the relative DNA methylation levels at the biomarker loci relative to the ratio of monocytes:non-monocytes. ## Claims ### Claim 1 A method for determining an increased risk of developing post-partum depression (PPD) in a human patient comprising the steps of: a) measuring white blood cell type counts and determining a ratio of monocytes:non monocytes in the sample collected from the patient; b) measuring DNA methylation levels of a panel of biomarkers loci in a sample collected from the patient, wherein the panel of biomarker loci comprises HP1BP3 loci and TTC9B loci, wherein the HP1BP3 loci comprising CpG dinucleotides located within chr1:20986708-20986650 on the minus strand of human genome build hg 18 and TTC9B loci comprises CpG dinucleotide located at chr19:45416573 on the plus strand of human genome build hg 18, wherein the DNA methylation levels are measured by amplification using one or more primer comprising SEQ ID NO 8-17; and c) using a linear model that utilizes the DNA methylation level of HP1BP3 and TTC9B and the ratio of monocytes:non-monocytes to determine that the patient is at increased risk of developing PPD. ### Claim 2 (depends on 1) The method of claim 1, wherein the panel of biomarkers further comprises PABPC1L, wherein the PABPC1L biomarker loci comprises CpG dinucleotides located within the region chr20: 42971786-42971857 on the positive strand of human genome build hg18, and wherein the DNA methylation level of PABPC1L is measured by amplification using a primer comprising one of SEQ ID NOS:1-7. ### Claim 3 (depends on 1) The method of claim 1, wherein the panel of biomarkers further comprises OXTR, wherein the OXTR biomarker loci comprises CpG dinucleotides located within the region chr3:8785134-8785171 on the minus strand of human genome build hg18, and wherein the DNA methylation level of OXTR is measured by amplification using a primer comprising one of SEQ ID NOS:18-22. ### Claim 4 A method for identifying a likelihood of post-partum depression (PPD) in a human patient comprising the steps of: a) measuring white blood cell type counts and determining a ratio of monocytes:non monocytes in the sample collected from the patient; b) measuring DNA methylation levels of a panel of biomarkers loci in a sample collected from the patient, wherein the panel of biomarker loci comprises HP1BP3 loci and TTC9B loci, wherein the HP1BP3 loci comprising CpG dinucleotides located within chr1:20986708-20986650 on the minus strand of human genome build hg 18 and TTC9B loci comprises CpG dinucleotide located at chr19:45416573 on the plus strand of human genome build hg 18, wherein the DNA methylation levels are measured by amplification using a primer comprising one of SEQ ID NO 8-17; and c) using a linear model that utilizes the DNA methylation level of HP1BP3 and TTC9B and the ratio of monocytes:non-monocytes to determine that the patient as likely to develop PPD. ### Claim 5 (depends on 4) The method of claim 4, wherein the panel of biomarkers further comprises PABPC1L, wherein the PABPC1L loci biomarker comprises CpG dinucleotides located within the region chr20: 42971786-42971857 on the positive strand of human genome build hg18, and wherein the DNA methylation level of PABPC1L is measured by amplification using a primer comprising one of SEQ ID NOS:1-7. ### Claim 6 (depends on 4) The method of claim 4, wherein the linear model utilizes DNA methylation at HP1BP3 interacting with the ratio of monocytes:non-monocytes and utilizes DNA methylation at TTC9B as an additive covariate. ### Claim 7 (depends on 4) The method of claim 4, wherein the linear model utilizes DNA methylation at HP1BP3 and TTC9B as additive covariates and the ratio of monocytes:non-monocytes as an interacting component. ### Claim 8 (depends on 4) The method of claim 4, wherein the linear model uses a score from the Pittsburgh Sleep Quality Index (PSQI) scale taken at the time of sample draw from the patient as an additive or interactive covariate in the model to improve prediction accuracy. ### Claim 9 (depends on 4) The method of claim 4, wherein the linear model uses a score from the Clinical Global Impression Scale (CGIS) scale taken at the time of sample draw from the patient as an additive or interactive covariate in the model to improve prediction accuracy. ### Claim 10 (depends on 4) The method of claim 4, wherein the linear model uses a score from the Perceived Stress Scale (PSS) scale taken at the time of sample draw from the patient as an additive or interactive covariate in the model to improve prediction accuracy. ### Claim 11 (depends on 1) The method of claim 1, wherein the linear model utilizes DNA methylation at HP1BP3 interacting with the ratio of monocytes:non-monocytes and utilizes DNA methylation at TTC9B as an additive covariate. ### Claim 12 (depends on 1) The method of claim 1, wherein the linear model utilizes DNA methylation at HP1BP3 and TTC9B as additive covariates and the ratio of monocytes:non-monocytes as an interacting component. ### Claim 13 (depends on 1) The method of claim 1, wherein the linear model uses a score from the Pittsburgh Sleep Quality Index (PSQI) scale taken at the time of sample draw from the patient as an additive or interactive covariate in the model to improve prediction accuracy. ### Claim 14 (depends on 1) The method of claim 1, wherein the linear model uses a score from the Clinical Global Impression Scale (CGIS) scale taken at the time of sample draw from the patient as an additive or interactive covariate in the model to improve prediction accuracy. ### Claim 15 (depends on 1) The method of claim 1, wherein the linear model uses a score from the Perceived Stress Scale (PSS) scale taken at the time of sample draw from the patient as an additive or interactive covariate in the model to improve prediction accuracy.