BIOMARKERS FOR HBV TREATMENT RESPONSE

Abstract

The present invention relates to methods that are useful for predicting the response of hepatitis B virus (HBV) infected patients to pharmacological treatment.

Claims

1. A method of identifying a patient who may benefit from treatment with an anti-HBV therapy comprising an interferon, the method comprising: determining the presence of a single nucleotide polymorphism in gene SON on chromosome 21 in a sample obtained from the patient, wherein the presence of at least one G allele at rs13047599 indicates that the patient may benefit from the treatment with the anti-HBV treatment.

2. A method of predicting responsiveness of a patient suffering from an HBV infection to treatment with an anti-HBV treatment comprising an interferon, the method comprising: determining the presence of a single nucleotide polymorphism in gene SON on chromosome 21 in a sample obtained from the patient, wherein the presence of at least one G allele at rs13047599 indicates that the patient is more likely to be responsive to treatment with the anti-HBV treatment.

3. A method for determining the likelihood that a patient with an HBV infection will exhibit benefit from an anti-HBV treatment comprising an interferon, the method comprising: determining the presence of a single nucleotide polymorphism in gene SON on chromosome 21 in a sample obtained from the patient, wherein the presence of at least one G allele at rs13047599 indicates that the patient has increased likelihood of benefit from the anti-HBV treatment.

4. A method for optimizing the therapeutic efficacy of an anti-HBV treatment comprising an interferon, the method comprising: determining the presence of a single nucleotide polymorphism in gene SON on chromosome 21 in a sample obtained from the patient, wherein the presence of at least one G allele at rs13047599 indicates that the patient has increased likelihood of benefit from the anti-HBV treatment.

5. A method for treating an HBV infection in a patient, the method comprising: (i) determining the presence of at least one G allele at rs13047599 in gene SON on chromosome 21 in a sample obtained from the patient and (ii) administering an effective amount of an anti-HBV treatment comprising an interferon to said patient, whereby the HBV infection is treated.

6. A method for predicting HBeAg seroconversion and HBV DNA <2000 IU/ml at >=24-week follow-up of treatment (responders vs. non-responders) of an HBe-positive patient infected with HBV to interferon treatment comprising: providing a sample from said human subject, detecting the presence of a single nucleotide polymorphism in gene SON on chromosome 21 and determining that said patient has a high response rate to interferon treatment measured as HBeAg seroconversion and HBV DNA <2000 IU/ml at >=24-week follow-up of treatment (responders vs. non-responders) if at least one G allele at rs13047599 is present.

7. The method of any of claims 1 to 6, wherein the interferon is selected from the group of peginterferon alfa-2a, peginterferon alfa-2b, interferon alfa-2a and interferon alfa-2b.

8. The method of claim 7, wherein the interferon is a peginterferon alfa-2a conjugate having the formula: ##STR00002## wherein R and R are methyl, X is NH, and n and n are individually or both either 420 or 520.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0022] FIG. 1: Bar chart of the number of markers by chromosome in the GWAS Marker Set. An additional 1095 markers, were not plotted due to unknown genomic location.

[0023] FIG. 2: Scree plot for ancestry analysis. It is clear that the majority of information (highest eigenvalues) are obtained from the first two principal components of ancestry, with little gain in information after the fifth principal component.

[0024] FIG. 3: The first three principal components of ancestry for HapMap individuals only. Population codes are as listed in Table 2. FIG. 3 shows the results of PCA for the HapMap reference data only. Five clusters are visible, corresponding to five major bio-geographic ancestral origins. Reading clockwise from top left, they are: African origin (blue/orange/pink/maroon), South Asian Origin (grey), Southeast Asian (yellow/blue/green), Mexican (dark green) and Northern and Western European (blue/red).

[0025] FIG. 4: The first three principal components of ancestry for HapMap individuals; coloured according to population group (Table 2). Overlaid are patients who self-report as Oriental (black crosses) or another racial group (grey crosses). first three principal components of ancestry for HapMap individuals only. Population codes are as listed in Table 2.

[0026] FIG. 5: Manhattan Plots for Endpoint 1

[0027] FIG. 6: Manhattan Plots for Endpoint 2

[0028] FIG. 7: Manhattan Plots for Endpoint 4

[0029] FIG. 8: Bar plot of rate of response (Endpoint 1) for rs13047599 under the dominant model.

[0030] FIG. 9: Bar plot of rate of response (Endpoint 4) for rs12000 under the dominant model

[0031] FIG. 10: Bar plot of rate of response (Endpoint 4) for rs1913228 under the dominant model

[0032] FIG. 11: Bar plot of rate of response (Endpoint 4) for rs12636581 under the dominant model

DETAILED DESCRIPTION OF THE INVENTION

Definitions

[0033] To facilitate the understanding of this invention, a number of terms are defined below. Terms defined herein have meanings as commonly understood by a person of ordinary skill in the areas relevant to the present invention. Terms such as a, an and the are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration. The terminology herein is used to describe specific embodiments of the invention, but their usage does not delimit the invention, except as outlined in the claims.

[0034] The terms sample or biological sample refers to a sample of tissue or fluid isolated from an individual, including, but not limited to, for example, tissue biopsy, plasma, serum, whole blood, spinal fluid, lymph fluid, the external sections of the skin, respiratory, intestinal and genitourinary tracts, tears, saliva, milk, blood cells, tumors, organs. Also included are samples of in vitro cell culture constituents (including, but not limited to, conditioned medium resulting from the growth of cells in culture medium, putatively virally infected cells, recombinant cells, and cell components).

[0035] The terms interferon and interferon-alpha are used herein interchangeably and refer to the family of highly homologous species-specific proteins that inhibit viral replication and cellular proliferation and modulate immune response. Typical suitable interferons include, but are not limited to, recombinant interferon alpha-2b such as Intron A interferon available from Schering Corporation, Kenilworth, N.J., recombinant interferon alpha-2a such as Roferon-A interferon available from Hoffmann-La Roche, Nutley, N.J., recombinant interferon alpha-2C such as Berofor alpha 2 interferon available from Boehringer Ingelheim Pharmaceutical, Inc., Ridgefield, Conn., interferon alpha-n1, a purified blend of natural alpha interferons such as Sumiferon available from Sumitomo, Japan or as Wellferon interferon alpha-n1 (INS) available from the Glaxo-Wellcome Ltd., London, Great Britain, or a consensus alpha interferon such as those described in U.S. Pat. Nos. 4,897,471 and 4,695,623 (especially Examples 7, 8 or 9 thereof) and the specific product available from Amgen, Inc., Newbury Park, Calif., or interferon alpha-n3 a mixture of natural alpha interferons made by Interferon Sciences and available from the Purdue Frederick Co., Norwalk, Conn., under the Alferon Tradename. The use of interferon alpha-2a or alpha-2b is preferred. Interferons can include pegylated interferons as defined below.

[0036] The terms pegylated interferon, pegylated interferon alpha and peginterferon are used herein interchangeably and means polyethylene glycol modified conjugates of interferon alpha, preferably interferon alfa-2a and alfa-2b. Typical suitable pegylated interferon alpha include, but are not limited to, Pegasys and Peg-Intron.

[0037] As used herein, the terms allele and allelic variant refer to alternative forms of a gene including introns, exons, intron/exon junctions and 3 and/or 5 untranslated regions that are associated with a gene or portions thereof. Generally, alleles occupy the same locus or position on homologous chromosomes. When a subject has two identical alleles of a gene, the subject is said to be homozygous for the gene or allele. When a subject has two different alleles of a gene, the subject is said to be heterozygous for the gene. Alleles of a specific gene can differ from each other in a single nucleotide, or several nucleotides, and can include substitutions, deletions, and insertions of nucleotides.

[0038] As used herein, the term polymorphism refers to the coexistence of more than one form of a nucleic acid, including exons and introns, or portion (e.g., allelic variant) thereof. A portion of a gene of which there are at least two different forms, i.e., two different nucleotide sequences, is referred to as a polymorphic region of a gene. A polymorphic region can be a single nucleotide, i.e. single nucleotide polymorphism or SNP, the identity of which differs in different alleles. A polymorphic region can also be several nucleotides long.

[0039] Numerous methods for the detection of polymorphisms are known and may be used in conjunction with the present invention. Generally, these include the identification of one or more mutations in the underlying nucleic acid sequence either directly (e.g., in situ hybridization) or indirectly (identifying changes to a secondary molecule, e.g., protein sequence or protein binding).

[0040] One well-known method for detecting polymorphisms is allele specific hybridization using probes overlapping the mutation or polymorphic site and having about 5, 10, 20, 25, or 30 nucleotides around the mutation or polymorphic region. For use in a kit, e.g., several probes capable of hybridizing specifically to allelic variants, such as single nucleotide polymorphisms, are provided for the user or even attached to a solid phase support, e.g., a bead or chip.

[0041] The single nucleotide polymorphism, rs13047599 refers to a SNP identified by its accession number in the database of SNPs (dbSNP, www.ncbi.nlm.nih.gov/SNP/) and is located on human chromosome 21 in the SON DNA binding protein gene.

Abbreviations

[0042]

TABLE-US-00001 AIC Akaike Information Criterion ALT Alanine aminotransferase Anti-HBs Antibody to hepatitis B surface antigen DNA Deoxyribonucleic acid GWAS Genome-wide Association Study HAV Hepatitis A Virus HBe Hepatitis B e Antigen HBeAg Hepatitis B e Antigen HBV Hepatitis B Virus HCV Hepatitis C Virus HIV Human Immunodeficiency Virus HLA Human Leucocyte Antigen HWE Hardy-Weinberg Equilibrium IU/ml International units per milliliter PCA Principal Components Analysis PEGASYS Pegylated Interferon alpha 2a 40 KD; Peg-IFN Peg-IFN Pegylated Interferon alpha 2a 40 KD; PEGASYS QC Quality Checks qHBsAg Quantitative Hepatitis B Surface Antigen S-loss Surface Antigen Loss SNP Single Nucleotide Polymorphism SPC Summary of Product Characteristics TLR Toll-like Receptor Tx Treatment Vs. Versus

Examples

Objectives and Endpoints

[0043] The objective was to determine genetic variants associated with response to treatment with PEGASYS-containing regimen in patients with Chronic Hepatitis B.

[0044] The following endpoints, by patient group, were considered.

HBe-Positive Patients:

[0045] 1. HBeAg seroconversion at >=24-week follow-up (responders vs. non-responders) [0046] 2. HBeAg seroconversion plus HBV DNA<2000 IU/ml at >=24-week follow-up (responders vs. non-responders) [0047] 3. Loss of HBsAg or seroconversion at >=24-week follow-up (responders vs. non-responders)

[0048] The above list of endpoints shall hereafter be referred to as Endpoints 1-3 respectively.

HBe-Negative Patients:

[0049] 4. HBV DNA<2000 IU/ml at >=24-week follow-up (responders vs. non-responders)

[0050] The latter shall be referred to as Endpoint 4.

[0051] Endpoints 3 and 4 were analyzed also in the combined set of HBe-positive and HBe-negative patients.

[0052] For all endpoints and all markers, the null hypothesis of no association, between the genotype and the endpoint, was tested against the two-sided alternative that association exists.

Study Design

[0053] A cumulative meta-analysis, of data from company-sponsored clinical trials, and data from patients in General Practice care, is in progress. The combined data will, at the final analysis, comprise up to 1500 patients who have been treated with Pegasys for at least 24 weeks, with or without a nucleotide/nucleoside analogue, and with 24 weeks of follow-up data available. The following trials/patient sources were considered for inclusion: [0054] RGT (ML22266) [0055] S-Collate (MV22009) [0056] SoN (MV22430) [0057] Switch (ML22265) [0058] Combo [0059] New Switch (ML27928) [0060] NEED [0061] Italian cohort of PEG.Be.Liver [0062] Professor Teerha (Thailand): clinical practice patients and some legacy Ph3 patients [0063] Professor Hongfei Zhang (Beijing, China): clinical practice patients and some legacy Ph3 patients [0064] Professor Yao Xie (Beijing, China): clinical practice patients [0065] Professor Xin Yue Chen (Beijing, China): clinical practice patients

[0066] Adult patients with chronic hepatitis B (male or female patients 18 years of age) must meet the following criteria for study entry: [0067] Previously enrolled in a Roche study and treated for chronic hepatitis B for at least 24 weeks with Peg-IFN nucleoside analogue (lamivudine or entacavir) or Peg-IFN nucleotide analogue (adefovir) with 24-week post-treatment follow-up or; [0068] Treated in general practice for chronic hepatitis B with Peg-IFN according to standard of care and in line with the current summary of product characteristics (SPC)/local labeling who have no contra-indication to Peg-IFN therapy as per the local label and have been treated with Peg-IFN for at least 24 weeks and have 24-week post-treatment response available at the time of blood collection. [0069] Patients are not infected with HAV, HCV, or HIV [0070] Patients should have the following medical record available (either from historical/ongoing study databases or from medical practice notes): [0071] Demographics (e.g. age, gender, ethnic origin) [0072] Pre-therapy HBeAg status, known or unknown HBV genotype [0073] Quantitative HBV DNA by PCR Test in IU/ml over time (e.g. baseline, on-treatment: 12- and 24-week, post-treatment: 24-week) [0074] Quantitative HBsAg test (if not available, qualitative HBsAg test) and anti-HBs over time (e.g. baseline, on-treatment: 12- and 24-week, post-treatment: 24-week) [0075] Serum ALT over time (e.g. baseline, on-treatment: 12- and 24-week, post-treatment: 24-week)

[0076] It is noted that all patients will have received active regimen.

Analysis Populations

[0077] The majority of patients will be from China. For the purposes of statistical analysis, four analysis populations were defined as follows. [0078] PGx-FAS is all patients with at least one genotype [0079] PGx-GT is the subset of PGx-FAS whose genetic data passes quality checks [0080] PGx-CN is the subset of PGx-GT who share a common genetic background in the sense that they cluster with CHB and CHD reference subjects from HapMap version3 (see below) [0081] PGx-non-CN is the remainder of PGx-GT who do not fall within PGx-CN

[0082] Additional suffices are appended as HBePos or HBeNeg for the HBe-Positive and HBe-Negative subsets respectively, and as interim1, . . . interim3, and final, according to the stage of the analysis.

Genetic Markers

[0083] The GWAS marker panel was the Illumina OmniExpress Exome microarray (www.illumina.com), consisting of greater than 750,000 SNP markers and greater than 250,000 exonic markers. The group of markers which passed quality checks are referred to as the GWAS Marker Set.

General Considerations for Data Analysis

[0084] The GWAS is hypothesis-free. Markers with unadjusted p<510.sup.8 were considered to be genome-wide significant. In the interests of statistical power, no adjustment was made for multiple endpoints or multiple rounds of analysis.

Results for First Interim Analysis

[0085] The following paragraphs describe the results arising from the first interim analysis. Similar analyses will be conducted for up to three further batches of accumulating data, with patient sets labeled appropriately with suffices: interim2, interim3, and final.

Description of the Data Clinical data was received in the form of a comma-delimited flat file entitled demoext.csv. The file had 20 columns and 218 rows including a header line, with one row per patient. Genetic data was received in the form of four Illumina-formatted files entitled, 24-luoshi FinalReport.txt, 25 FinalReport.txt, 32 FinalReport.txt and 56 FinalReport.txt. The combination of these files contained genotypes for 137 patients, and 951,117 SNPs. Table 1 below shows the baseline and demographic characteristics for the 137 patients with at least one genotype. All patients were studied under protocol MV22430. The subject set is PGx-FAS-interim1.

TABLE-US-00002 TABLE 1 Baseline and Demographic Characteristics for PGx-FAS-interim1 Variable Category Statistics Result Count (n) 137 Sex Male n (%) 88 (64%) Female n (%) 49 (36%) Age (yr) Mean (SE) 32.25 (0.848) Race Oriental n (%) 119 (87%) Caucasian n (%) 7 (5%) Other* n (%) 11 (8%) Height (cm) Mean (SE) 168.26 (0.766) Weight (kg) Mean (SE) 67.74 (1.43) BMI (kg/m{circumflex over ()}2) Mean (SE) 23.78 (0.416) Baseline ALT (U/L) Median (IQR) 123 (119) *Self reported races were as follows: Pacific Islander; Maori; Indian; Burmese; Black

Quality Checks by Patient

[0086] The following criteria were assessed, on the basis of unfiltered GWAS data, in all 137 patients of any self-reported race (PGx-FAS-interim1). [0087] <5% missing genotype data [0088] <30% heterozygosity genome-wide [0089] <30% genotype-concordance with another sample [0090] Reported sex consistent with X-chromosome data

[0091] All patients had <5% missing genotypes, genome-wide heterozygosity <30% and X-chromosome data consistent with self-reported sex. One pair of first-degree relatives however was detected. The patient identifiers (ANONID) were 8734 (female Caucasian, aged 55 yr) and 8760 (male Caucasian, aged 25 yr). Of the two, patient 8734 had slightly more missing data (0.2% vs. 0.1%) and so will be excluded from further analysis.

[0092] The remaining 136 patients were incorporated into the PGx-GT-interim1 Set.

Quality Checks by Marker

[0093] Markers derived for GWAS and candidate gene study were assessed for missing data. A total of 1712 markers (<0.2% of the total) had greater than 5% missing data and were excluded from further analysis. The GWAS Marker Set therefore consists of 949,405 markers. FIG. 1 shows the number of markers by chromosome in the GWAS Marker Set. As expected across the autosomes, the number of markers varies approximately in line with chromosomal size.

Multivariate Analysis of Ancestry

[0094] Principal Components Analysis (PCA) is a technique for reducing the dimensionality of a data set. It linearly transforms a set of variables into a smaller set of uncorrelated variables representing most of the information in the original set (Dunteman, 1989). In the current study, the marker variables were transformed into principal components which were compared to self-reported ethnic groupings. The objective is, in preparation for association testing, to determine clusters of individuals who share a homogeneous genetic background.

[0095] A suitable set of GWAS markers for ancestry analysis was obtained, using PGx-GT-interim1, as follows. Markers were excluded if they had frequency less than 5% or if they corresponded to regions with known high Linkage disequilibrium (LD) or inversion (Chr5, 44-51.5 Mb; Chr6, 24-36 Mb; Chr8, 8-12 Mb; Chr11, 42-58 Mb; Chr17, 40-43 Mb). In order to facilitate merging, markers encoding complementary base-changes were also removed. The remaining markers were thinned such that all SNPs within a window of size 1000 had r.sup.2<0.25.

[0096] HapMap version 3 data was downloaded for the resultant marker set (The International HapMap Consortium, 2003; 2005; 2007). Table 2 shows the composition of the HapMap subjects, who were used as reference sets against which data from PGx-GT-interim1 was compared. HapMap data were merged with PGx-GT-interim1 data, taking care to resolve any strand differences between the two sources. Any marker not available for HapMap subjects was excluded from the merged file.

[0097] PCA was applied using 134,575 markers, selected as described above, and genotyped across 136 study individuals and 988 HapMap reference individuals.

TABLE-US-00003 TABLE 2 Details of the HapMap version 3 reference subjects Code Description Count MKK Maasai in Kinyawa, Kenya 143 LWK Luhya in Webuye, Kenya 90 YRI Yoruba in Ibadan, Nigeria 113 ASW African ancestry in Southwest USA 49 CEU Utah residents with Northern and Western European 112 ancestry from the CEPH collection TSI Tuscans in Italy 88 MEX Mexican ancestry in Los Angeles, California 50 GIH Gujarati Indians in Houston, Texas 88 JPT Japanese in Tokyo, Japan 86 CHD Chinese in Metropolitan Denver, Colorado 85 CHB Han Chinese in Bejing, China 84 TOTAL 988

[0098] FIG. 4 shows the same data with study participants overlaid. Patients self-reporting as Oriental are given by black crosses; patients self-reporting as another racial group are given by grey crosses. Two observations are of note. Firstly, it can be clearly seen that while those patients self-reporting as Oriental cluster with, or close to, the Chinese and Japanese HapMap reference individuals, they form a much wider cluster. As such, the study participants represent a genetically more diverse group of individuals than the reference set. The study participants are likely to have been drawn from different countries in South-East Asia. Secondly, within the cluster of black crosses, some grey crosses are observedthese represent individuals who did not self-report as Oriental, but whose genetic background is indistinguishable from that of members of the Oriental group.

[0099] For the purposes of genetic analysis, PGx-CN-interim1 was made up of the 128 patients falling within the boundaries of the self-reported Oriental cluster. Eight patients, whose plotted ancestry clearly departed from that cluster, made up PGx-non-CN-interim1: they self-reported as Caucasian (n=6), Maori (n=1) and Indian (n=1).7.

Assessment of Covariates

[0100] In order to determine the covariates for the forthcoming genome-wide association analysis, a series of variables were tested for association with each endpoint, using backwards stepwise regression. In accordance with the planned analysis, the subject set for Endpoints 1-3 was PGx-GT-HBePos-Interim1 (n=134); the subject set for Endpoint 4 was all members of PGx-GT-Interim1 (n=136). Backwards steps were taken on the basis of the Akaike Information Criterion (AIC).

[0101] The covariates in the full model were as follows: Age, Sex, Baseline HBV DNA, Baseline ALT, HBV genotype, and Concomitant use of nucleotide/nucleoside analogues. Baseline HBV and Baseline ALT were both log-transformed in order to improve symmetry. Due to the fact that almost all patients shared a homogeneous genetic background, principal components of ancestry were not included in the backwards stepwise regression. Tables 3-5 show the covariates selected for the various endpoints.

[0102] It can be seen that baseline HBV DNA and Baseline ALT were selected in all five instances; Concomitant nucleotide/nucleoside analogues were selected in three of the five; HBV Genotype was selected for Endpoint 4, although the coding of that variable (with three low-frequency categories) meant that the individual effect sizes were not well-estimated.

TABLE-US-00004 TABLE 3 Covariates selected by backwards stepwise regression for Endpoint 1 Variable Odds Ratio (95% CI) p-value (Intercept) 0.23 (0.00-12.94) 0.4729 Log(HBV DNA) 0.55 (0.37-0.83) 0.0037 Log(ALT) 3.03 (1.53-6.02) 0.0015 Concomitant nucleotide/nucleoside 0.35 (0.14-0.86) 0.0220 analogues

TABLE-US-00005 TABLE 4 Covariates selected by backwards stepwise regression for Endpoint 2 Variable Odds Ratio (95% CI) p-value (Intercept) 0.04 (0.00-3.91) 0.1673 Log(HBV DNA) 0.49 (0.30-0.79) 0.0035 Log(ALT) 4.27 (1.85-9.86) 0.0007 Concomitant nucleotide/nucleoside 0.32 (0.09-1.12) 0.0736 analogues

TABLE-US-00006 TABLE 5 Covariates selected by backwards stepwise regression for Endpoint 4 Variable Odds Ratio (95% CI) p-value (Intercept) .sup.0.17 (0.00-10.28) 0.3980 Sex (Male) 0.39 (0.15, 1.04) 0.0593 Log(HBV DNA) 0.44 (0.28, 0.68) 0.0002 Log(ALT) 3.75 (1.74, 8.05) 0.0007 Genotype A NA 0.2292 Genotype B 4.18 (1.49, 11.73) 0.0066 Genotype D 0.70 (0.07, 7.31) 0.7618 Genotype Mixed NA 0.0698

Single-Point Association Analysis

Methods

[0103] Due to the modest sample size (n=136) of the first interim analysis, markers were excluded from single-point association analysis if they had frequency less than 5%. The remaining 571832 markers were coded in two ways as follows. Firstly they were coded according to an additive model, given by the count of the number of minor alleles. Secondly they were coded according to a dominant model of inheritance, based upon carriage of the minor allele.

[0104] Association analysis was conducted for two patient sets and five endpoints, under two modes of inheritance.

[0105] The following model was fitted using multivariate logistic regression:

Endpoint=Intercept+[Covariates]+Marker

[0106] Covariates were applied as selected above (Section 7.5). Despite the original intent, no correction for principal components of ancestry was applied in the analyses of PGx-GT, due to problems of over-fitting in this mainly homogeneous set. Adjustments for principal components of ancestry will be attempted in future interim analyses. An adjustment for study was also not applied because all of the patients in the current interim analysis are drawn from the same protocol.

[0107] The significance of each marker was determined using a t-test. The genomic control lambda was calculated for each GWAS analysis and QQ-plots were examined, but no clear evidence of test-statistic inflation was found (Devlin and Roeder 1999).

[0108] All markers were tested, using a chi-square test, for departure from Hardy-Weinberg Equilibrium (HWE). The calculation was performed for patients in the PGx-CN-interim1 Set and the results were used to assist in the interpretation of association analysis output.

[0109] Bar plots were produced for markers of interest, to show the rate of response by genotype.

Results

[0110] FIGS. 5-7 are the Manhattan Plots, by Endpoint and Tables 6-17 list association results with p<10.sup.4.

TABLE-US-00007 TABLE 6 Association Results with p < 10.sup.4 for Endpoint 1, PGx-CN-HBePos, additive model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 7 rs16878220 14557271 1.0000 0.2930 5.23 4.20E05 INTRONIC DGKB 7 rs10257158 14558492 1.0000 0.2930 5.90 1.92E05 INTRONIC DGKB 7 rs16878221 14583406 0.6738 0.3008 6.46 1.62E05 INTRONIC DGKB 9 rs3750551 71051949 0.5431 0.3164 5.14 7.10E05 INTRONIC TJP2 17 rs17689366 10118314 1.0000 0.1211 10.01 3.30E05 INTERGENIC NA 17 rs17762691 10119173 1.0000 0.1250 9.21 4.52E05 INTERGENIC NA 23 rs1037218 NA 0.7654 0.4624 6.90 5.33E05 NA NA 23 rs1026101 NA 1.0000 0.4451 6.24 8.72E05 NA NA 23 rs5915908 NA 0.7654 0.4682 6.82 6.29E05 NA NA

TABLE-US-00008 TABLE 7 Association Results with p < 10.sup.4 for Endpoint 1), PGx-GT-HBePos, additive model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 5 rs814586 10584998 0.578 0.3828 5.14 5.25E05 INTERGENIC NA 7 rs16878220 14557271 1.0000 0.2930 5.00 3.61E05 INTRONIC DGKB 7 rs10257158 14558492 1.0000 0.2930 5.59 1.61E05 INTRONIC DGKB 7 rs16878221 14583406 0.6738 0.3008 5.67 2.01E05 INTRONIC DGKB 11 rs2553825 35055962 0.7952 0.2109 4.87 9.56E05 INTERGENIC NA 17 rs17689366 10118314 1.0000 0.1211 7.24 7.34E05 INTERGENIC NA 17 rs17762691 10119173 1.0000 0.125 6.83 9.67E05 INTERGENIC NA 17 exm2272553 NA 0.2864 0.4727 4.75 6.60E05 NA NA 19 rs168109 46408111 0.5926 0.4531 3.97 8.10E05 DOWN- NA STREAM

TABLE-US-00009 TABLE 8 Association Results with p < 10.sup.4 for Endpoint 1, PGx-CN-HBePos, dominant model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 3 rs11923404 166614084 1.0000 0.1614 9.83 4.25E05 INTERGENIC NA 6 rs4711668 41354451 0.4789 0.4883 0.11 6.61E05 INTRONIC TREM1 6 rs6899577 78017088 0.3282 0.2344 7.17 6.99E05 INTERGENIC NA 6 rs6899965 78160992 1.0000 0.2227 6.96 8.50E05 INTERGENIC NA 7 rs16878221 14583406 0.6738 0.3008 9.47 4.96E05 INTRONIC DGKB 11 rs2553825 35055962 0.7952 0.2109 7.18 7.49E05 INTERGENIC NA 17 rs17689366 10118314 1.0000 0.1211 10.78 2.11E05 INTERGENIC NA 17 rs17762691 10119173 1.0000 0.125 9.87 3.30E05 INTERGENIC NA 19 rs9630865 35471071 1.0000 0.444 0.12 8.36E05 INTERGENIC NA 21 rs3761347 33786154 0.1143 0.2695 8.55 3.48E05 UPSTREAM NA 21 rs7283354 33798940 0.1232 0.2812 7.26 8.94E05 INTRONIC GART 21 rs7279549 33838483 0.3892 0.2812 8.19 5.03E05 INTRONIC SON 21 rs13047599 33848130 0.5161 0.2852 8.16 5.44E05 NON-SYNON SON 21 rs12626839 33850966 0.3771 0.2773 9.62 2.16E05 INTRONIC SON 21 rs11088256 33865413 0.3892 0.2812 8.19 5.03E05 INTRONIC SON 21 rs2834239 33876026 0.3771 0.2773 9.59 2.25E05 INTRONIC DONSON 21 rs7283856 33881646 0.5161 0.2852 8.13 5.66E05 INTRONIC DONSON 21 rs10460711 33885863 0.2714 0.2734 9.65 1.99E05 INTRONIC CRYZL1 21 rs2070391 33949012 0.3771 0.2773 9.93 2.04E05 INTRONIC ITSN1 21 rs2073368 34088673 0.3960 0.293 11.73 1.34E05 INTRONIC ITSN1 21 exm2254590 NA 0.6576 0.2698 8.30 5.01E05 NA NA 21 exm1567802 NA 0.3771 0.2773 9.62 2.16E05 NA NA 21 exm1567815 NA 0.5161 0.2852 8.16 5.44E05 NA NA

TABLE-US-00010 TABLE 9 Association Results with p < 10.sup.4 for Endpoint 1, PGx-GT-HBePos, dominant model HWE Chr SNP BP (p) MAF Beta p-value Variant Gene 1 exm 252809 NA 0.726 0.4766 0.13 8.88E05 NA NA 6 rs4711668 41354451 0.4789 0.4883 0.11 3.19E05 INTRONIC TREM1 7 rs16878221 14583406 0.6738 0.3008 7.54 7.22E05 INTRONIC DGKB 11 rs2553825 35055962 0.7952 0.2109 7.22 5.91E05 INTERGENIC NA 11 rs2583151 99148202 0.2742 0.0508 18.06 7.05E05 INTERGENIC NA 17 rs17689366 10118314 1.0000 0.1211 7.65 6.53E05 INTERGENIC NA 17 rs17762691 10119173 1.0000 0.1250 7.17 9.55E05 INTERGENIC NA 17 rs1007482 69587964 1.0000 0.1562 7.44 7.56E05 INTERGENIC NA 19 rs9630865 35471071 1.0000 0.4440 0.11 6.72E05 INTERGENIC NA 21 rs3761347 33786154 0.1143 0.2695 7.71 3.50E05 UPSTREAM NA 21 rs7283354 33798940 0.1232 0.2812 6.63 9.03E05 INTRONIC GART 21 rs7279549 33838483 0.3892 0.2812 7.38 5.17E05 INTRONIC SON 21 rs13047599 33848130 0.5161 0.2852 7.35 5.61E05 NON-SYNON SON 21 rs12626839 33850966 0.3771 0.2773 8.55 2.21E05 INTRONIC SON 21 rs11088256 33865413 0.3892 0.2812 7.38 5.17E05 INTRONIC SON 21 rs2834239 33876026 0.3771 0.2773 8.51 2.32E05 INTRONIC DONSON 21 rs7283856 33881646 0.5161 0.2852 7.32 5.86E05 INTRONIC DONSON 21 rs10460711 33885863 0.2714 0.2734 8.58 2.03E05 INTRONIC CRYZL1 21 rs2070391 33949012 0.3771 0.2773 8.78 2.10E05 INTRONIC ITSN1 21 rs2073368 34088673 0.3960 0.2930 10.29 1.31E05 INTRONIC ITSN1 21 exm 254590 NA 0.6576 0.2698 7.42 5.25E05 NA NA 21 exm 567802 NA 0.3771 0.2773 8.55 2.21E05 NA NA 21 exm 567815 NA 0.5161 0.2852 7.35 5.61E05 NA NA

TABLE-US-00011 TABLE 10 Association Results with p < 10.sup.4 for Endpoint 2, PGx-CN-HBePos, additive model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 6 rs2881194 167187909 0.5610 0.3477 8.06 7.74E05 INTRONIC RPS6KA2 6 exm2270542 NA 0.5610 0.3477 8.06 7.74E05 NA NA 6 rs4710123 NA 0.4910 0.2578 8.85 7.29E05 NA NA 14 rs7145788 100827034 1.0000 0.0703 33.93 6.89E05 INTERGENIC NA 14 rs2401012 100830039 0.3580 0.1055 37.31 2.66E05 INTERGENIC NA 14 rs2151762 100865108 1.0000 0.0703 33.93 6.89E05 INTERGENIC NA

TABLE-US-00012 TABLE 11 Association Results with p < 10.sup.4 for Endpoint 2, PGx-GT-HBePos, additive model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 8 rs4460346 141182898 1.0000 0.2070 8.84 4.36E05 INTRONIC ENSG00000167632 8 rs4297022 141182923 0.7816 0.1992 9.45 4.49E05 INTRONIC ENSG00000167632 9 rs17793551 111187280 0.3662 0.1133 10.85 8.61E05 INTRONIC PTPN3 11 rs4936746 122172000 0.5975 0.0859 14.18 6.26E05 INTRONIC ENSG00000154127 11 rs12576789 122180319 0.5975 0.0859 14.18 6.26E05 INTRONIC ENSG00000154127

TABLE-US-00013 TABLE 12 Association Results with p < 10.sup.4 for Endpoint 2, PGx-CN-HBePos, dominant model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 4 rs13115100 160972974 0.4772 0.1484 12.78 8.23E05 INTERGENIC NA 11 rs1320042 7400603 0.8608 0.4883 0.04 9.86E05 INTRONIC SYT9 12 rs2638398 19906509 0.2807 0.4375 0.03 8.73E05 INTERGENIC NA 12 rs7959247 19909489 0.2807 0.4375 0.03 8.73E05 INTERGENIC NA 14 rs7145788 100827034 1.0000 0.0703 33.93 6.89E05 INTERGENIC NA 14 rs2401012 100830039 0.3580 0.1055 37.31 2.66E05 INTERGENIC NA 14 rs2151762 100865108 1.0000 0.0703 33.93 6.89E05 INTERGENIC NA

TABLE-US-00014 TABLE 13 Association Results with p < 10.sup.4 for Endpoint 2, PGx-GT-HBePos, dominant model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 7 rs10282247 25045068 0.1924 0.0820 19.52 5.00E05 INTERGENIC NA 8 rs4460346 141182898 1.0000 0.2070 19.28 8.03E05 INTRONIC ENSG00000167632 8 rs4297022 141182923 0.7816 0.1992 23.66 4.42E05 INTRONIC ENSG00000167632 10 rs11239257 44649855 1.0000 0.1094 13.57 9.81E05 INTERGENIC NA 11 rs1320042 7400603 0.8608 0.4883 0.04 5.59E05 INTRONIC SYT9 11 rs4936746 122172000 0.5975 0.0859 14.18 6.26E05 INTRONIC ENSG00000154127 11 rs12576789 122180319 0.5975 0.0859 14.18 6.26E05 INTRONIC ENSG00000154127 14 rs2401012 100830039 0.3580 0.1055 16.48 6.76E05 INTERGENIC NA

TABLE-US-00015 TABLE 14 Association Results with p < 10.sup.4 for Endpoint 4, PGx-CN, additive model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 3 rs1913228 62748219 1.0000 0.1772 9.33 6.18E05 INTRONIC CADPS 3 rs12636581 62748900 1.0000 0.1758 9.36 5.94E05 INTRONIC CADPS 4 rs10016726 14342870 0.3099 0.3164 9.12 5.44E05 INTERGENIC NA 6 exm-rs3830076 NA 1.0000 0.0820 15.77 9.64E05 NA NA 8 rs12675119 27714410 1.0000 0.3398 7.63 6.20E05 INTRONIC ESCO2 8 rs4732756 27720605 0.6827 0.3164 8.49 2.31E05 INTRONIC ESCO2

TABLE-US-00016 TABLE 15 Association Results with p < 10.sup.4 for Endpoint 4, PGx-GT, additive model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 3 rs7633796 62733450 0.4034 0.1953 6.99 7.32E05 INTRONIC CADPS 3 rs1913228 62748219 1.0000 0.1772 8.70 3.50E05 INTRONIC CADPS 3 rs12636581 62748900 1.0000 0.1758 8.74 3.31E05 INTRONIC CADPS 4 rs6856070 10154866 1.0000 0.1484 7.35 8.77E05 INTRONIC ENSG00000109684 4 rs13108803 174915773 0.0121 0.2305 4.91 8.15E05 INTERGENIC NA 5 rs6449558 61121309 0.1914 0.2188 5.97 9.07E05 INTERGENIC NA 11 rs10839791 7461909 0.3640 0.1719 9.45 6.79E05 UPSTREAM NA 11 rs11038167 NA 0.5677 0.3633 6.14 4.38E05 NA NA 12 rs893531 3947012 0.3161 0.1602 9.23 8.76E05 INTERGENIC NA

TABLE-US-00017 TABLE 16 Association Results with p < 10.sup.4 for Endpoint 4, PGx-CN, dominant model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 3 rs1913228 62748219 1.0000 0.1772 18.65 2.54E05 INTRONIC CADPS 3 rs12636581 62748900 1.0000 0.1758 18.72 2.45E05 INTRONIC CADPS 6 exm-rs3830076 NA 1.0000 0.082 15.77 9.64E05 NA NA 8 rs1437253 108864282 0.8596 0.4922 0.07 9.40E05 INTERGENIC NA

TABLE-US-00018 TABLE 17 Association Results with p < 10.sup.4 for Endpoint 4, PGx-GT, dominant model Chr SNP BP HWE (p) MAF Beta p-value Variant Gene 3 rs7633796 62733450 0.4034 0.1953 12.02 3.88E05 INTRONIC CADPS 3 rs1913228 62748219 1.0000 0.1772 20.06 9.27E06 INTRONIC CADPS 3 rs1913228 62748219 1.0000 0.1772 20.06 9.27E06 INTRONIC CADPS 3 rs12636581 62748900 1.0000 0.1758 20.16 8.81E06 INTRONIC CADPS 3 rs12636581 62748900 1.0000 0.1758 20.16 8.81E06 INTRONIC CADPS 3 rs1513143 62753782 0.2315 0.2422 12.11 5.59E05 INTRONIC CADPS 4 rs10014387 172643415 1.0000 0.4961 0.10 6.92E05 INTERGENIC NA 5 rs6449558 61121309 0.1914 0.2188 12.50 6.66E05 INTERGENIC NA 6 rs1233710 28323425 0.8444 0.3438 0.072 7.60E05 INTRONIC ZKSCAN4 6 rs12000 28335415 0.5939 0.4297 0.088 8.80E05 NON-SYNON NKAPL 6 rs2228628 NA 1.0000 0.1289 11.87 5.73E05 NA NA 6 exm524729 NA 0.5939 0.4297 0.088 8.80E05 NA NA 6 rs4713506 NA 0.1689 0.1523 11.54 9.28E05 NA NA 6 exm-rs4713506 NA 0.1689 0.1523 11.54 9.28E05 NA NA 6 rs4713505 NA 1.0000 0.1289 11.87 5.73E05 NA NA 6 exm-rs4713505 NA 1.0000 0.1289 11.87 5.73E05 NA NA

Interpretation

[0111] Genome-wide association scanning was applied to 571832 markers with estimated frequency in PGx-CN (n=128) of greater than 5%.

[0112] Four associations surpassed the level of p<10.sup.5: [0113] Intronic markers in CADPS (Endpoint 4; PGx-GT; dominant)

[0114] CADPS is Calcium-Dependent Secretion Activator. This gene encodes a neural/endocrine-specific cytosolic and peripheral membrane protein required for the calcium-regulated exocytosis of secretory vesicles. Diseases associated with CADPS include pineoblastoma and childhood medulloblastoma.

[0115] Two non-synonymous changes had p<10.sup.4: [0116] rs13047599 in SON (SON DNA Binding Protein) [0117] rs12000 in NKAPL (NFKB Activating Protein-Like)

[0118] The first of these was observed for Endpoint 1, in both PGx-CN-HBePos and PGx-GT-HBePos, under the dominant model of inheritance, but not the additive model. The encoded protein binds RNA and promotes pre-mRNA splicing, particularly transcripts with poor splice sites. The protein also recognizes a specific DNA sequence found in the human hepatitis B virus (HBV) and represses HBV core promoter activity. Diseases associated with SON include hepatitis B. Bar plots, showing rate of response by genotype under the dominant model, for rs13047599 and Endpoint 1 is given in FIG. 9 below.

[0119] The second non-synonymous change was observed for Endpoint 4 in PGx-GT It lies in NKAPL, a protein-coding gene associated with schizophrenia. Bar plots, showing rate of response by genotype for rs12000 and Endpoint 4 is given in FIG. 10 below.

[0120] As expected, a great deal of consistency of results was observed within each endpoint; the difference between the CN and GT groups is a matter of only a handful of patients. A total of 33 genes are listed in the tables above. Of these, at least two have been previously implicated in hepatitis B disease risk or progression: [0121] PTPN3 (Protein tyrosine phosphatase; Hsu et al, 2007) [0122] TREM1 (Triggering receptor expressed on myeloid cells 1; Liao et al, 2012)

Software

[0123] Custom-written perl scripts (Wall et al, 1996) were used to reformat the data, select markers for ancestry analysis and produce tables. PLINK version 1.07 (Purcell et al, 2007) was used to perform the genetic QC analyses, to merge study data with HapMap data, and for association analysis. EIGENSOFT 4.0 (Patterson et al, 2006; Price et al, 2006) was used for PCA. R version 2.15.2 (R Core Team, 2012) was used for the production of graphics.

[0124] All of the compositions and/or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and/or methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.