MiDAS tutorial

Introduction

Welcome to MiDAS. This tutorial is supposed to help you get started with your analyses of immunogenetic associations. We will work with a simulated data set of 500 patients and 500 controls with a binary disease diagnosis.

We also have high resolution HLA alleles (4 - 6 digit), and presence/absence calls for KIR genes.

Data import and sanity check

First, let’s import the phenotype data and HLA calls using MiDAS import functions. MiDAS will check for correct nomenclature of HLA. We can also define 4-digit resolution for HLA alleles as import format, which means that alleles with higher resolution will be reduced.

dat_pheno <-
  read.table(
  file = system.file("extdata", "MiDAS_tut_pheno.txt", package = "midasHLA"),
  header = TRUE
  )
dat_HLA <-
  readHlaCalls(
  file = system.file("extdata", "MiDAS_tut_HLA.txt", package = "midasHLA"),
  resolution = 4
  )

Let’s take a look at the imported HLA data tables:

HLA data as imported by MiDAS
ID	A_1	A_2	B_1	B_2	C_1	C_2	DPA1_1	DPA1_2	DPB1_1	DPB1_2	DQA1_1	DQA1_2	DQB1_1	DQB1_2	DRA_1	DRA_2	DRB1_1	DRB1_2
C001	A*25:01	A*26:01	B*07:02	B*18:01	C*12:03	C*07:02	DPA1*01:03	DPA1*01:03	DPB1*02:01	DPB1*04:01	DQA1*05:05	DQA1*01:02	DQB1*06:02	DQB1*03:01	DRA*01:02	DRA*01:02	DRB1*15:01	DRB1*12:01
C002	A*02:01	A*02:324	B*50:01	B*18:01	C*06:02	C*12:03	DPA1*01:03	DPA1*01:03	DPB1*04:02	DPB1*04:02	DQA1*02:01	DQA1*05:05	DQB1*02:02	DQB1*03:01	DRA*01:01	DRA*01:01	DRB1*07:01	DRB1*11:04
C003	A*24:02	A*24:04	B*46:01	B*40:06	C*01:03	C*15:02	DPA1*02:02	DPA1*02:01	DPB1*05:01	DPB1*14:01	DQA1*01:04	DQA1*01:03	DQB1*06:01	DQB1*05:02	DRA*01:02	DRA*01:01	DRB1*14:07	DRB1*08:03
C004	A*01:01	A*24:02	B*08:01	B*15:01	C*07:01	C*03:03	DPA1*01:03	DPA1*01:03	DPB1*04:01	DPB1*03:01	DQA1*01:03	DQA1*01:02	DQB1*06:04	DQB1*06:03	DRA*01:02	DRA*01:01	DRB1*13:01	DRB1*13:02
C005	A*01:01	A*25:01	B*18:01	B*08:01	C*12:03	C*07:01	DPA1*01:03	DPA1*01:03	DPB1*04:01	DPB1*23:01	DQA1*05:01	DQA1*01:02	DQB1*02:01	DQB1*06:02	DRA*01:02	DRA*01:02	DRB1*03:01	DRB1*15:01
C006	A*03:01	A*01:01	B*07:02	B*08:01	C*07:01	C*07:02	DPA1*01:03	DPA1*01:03	DPB1*57:01	DPB1*271:01	DQA1*01:02	DQA1*01:01	DQB1*06:04	DQB1*05:01	DRA*01:01	DRA*01:02	DRB1*13:02	DRB1*01:02
C007	A*01:01	A*02:01	B*15:01	B*08:01	C*07:01	C*03:03	DPA1*02:01	DPA1*01:03	DPB1*04:02	DPB1*13:01	DQA1*05:01	DQA1*03:01	DQB1*02:01	DQB1*03:02	DRA*01:01	DRA*01:02	DRB1*03:01	DRB1*04:01
C008	A*11:01	A*02:01	B*35:01	B*27:05	C*03:04	C*04:01	DPA1*01:03	DPA1*01:03	DPB1*04:01	DPB1*04:01	DQA1*03:03	DQA1*01:02	DQB1*06:04	DQB1*03:01	DRA*01:02	DRA*01:01	DRB1*13:02	DRB1*04:01
C009	A*23:01	A*01:01	B*13:02	B*18:01	C*07:01	C*07:02	DPA1*02:01	DPA1*01:03	DPB1*01:01	DPB1*04:02	DQA1*05:05	DQA1*03:01	DQB1*03:01	DQB1*03:02	DRA*01:01	DRA*01:01	DRB1*11:04	DRB1*04:03
C010	A*31:01	A*02:06	B*15:01	B*56:01	C*04:01	C*03:03	DPA1*02:02	DPA1*02:02	DPB1*05:01	DPB1*05:01	DQA1*03:02	DQA1*03:02	DQB1*03:03	DQB1*03:96	DRA*01:01	DRA*01:01	DRB1*09:01	DRB1*09:01
C011	A*02:01	A*01:01	B*07:02	B*13:02	C*06:02	C*07:02	DPA1*02:01	DPA1*02:01	DPB1*17:01	DPB1*10:01	DQA1*02:01	DQA1*01:01	DQB1*02:02	DQB1*05:01	DRA*01:01	DRA*01:01	DRB1*07:01	DRB1*01:01
C012	A*02:01	A*02:01	B*15:01	B*27:02	C*02:02	C*03:03	DPA1*01:03	DPA1*01:03	DPB1*04:01	DPB1*04:01	DQA1*01:02	DQA1*03:01	DQB1*03:02	DQB1*05:02	DRA*01:01	DRA*01:01	DRB1*04:04	DRB1*16:01
C013	A*02:05	A*01:01	B*49:01	B*57:01	C*07:01	C*07:01	DPA1*01:03	DPA1*01:03	DPB1*04:01	DPB1*04:01	DQA1*02:01	DQA1*03:03	DQB1*03:02	DQB1*03:03	DRA*01:01	DRA*01:01	DRB1*04:05	DRB1*07:01
C014	A*30:02	A*01:01	B*37:01	B*27:05	C*06:02	C*02:02	DPA1*02:01	DPA1*01:03	DPB1*02:01	DPB1*10:01	DQA1*03:01	DQA1*03:03	DQB1*04:02	DQB1*03:02	DRA*01:01	DRA*01:01	DRB1*04:03	DRB1*04:04
C015	A*02:642	A*03:01	B*07:02	B*07:02	C*07:02	C*07:02	DPA1*01:03	DPA1*01:03	DPB1*04:01	DPB1*16:01	DQA1*01:02	DQA1*01:02	DQB1*06:02	DQB1*06:02	DRA*01:02	DRA*01:02	DRB1*15:01	DRB1*15:01
C016	A*01:01	A*68:01	B*42:01	B*08:156	C*07:02	C*07:01	DPA1*01:03	DPA1*01:03	DPB1*04:01	DPB1*02:01	DQA1*05:01	DQA1*01:02	DQB1*02:01	DQB1*06:02	DRA*01:02	DRA*01:02	DRB1*03:01	DRB1*15:01
C017	A*03:01	A*11:01	B*08:01	B*18:01	C*05:01	C*07:01	DPA1*02:01	DPA1*01:03	DPB1*04:01	DPB1*01:01	DQA1*05:01	DQA1*05:01	DQB1*02:01	DQB1*02:01	DRA*01:01	DRA*01:02	DRB1*03:01	DRB1*03:01
C018	A*11:01	A*02:01	B*15:01	B*37:01	C*06:02	C*04:01	DPA1*01:03	DPA1*02:02	DPB1*04:01	DPB1*05:01	DQA1*01:03	DQA1*01:05	DQB1*06:01	DQB1*05:01	DRA*01:01	DRA*01:02	DRB1*10:01	DRB1*08:03
C019	A*02:10	A*31:01	B*54:01	B*40:06	C*08:01	C*01:02	DPA1*02:02	DPA1*01:03	DPB1*04:02	DPB1*05:01	DQA1*03:02	DQA1*03:01	DQB1*04:01	DQB1*03:96	DRA*01:01	DRA*01:01	DRB1*09:01	DRB1*04:05
C020	A*01:01	A*29:02	B*44:03	B*08:01	C*07:01	C*16:01	DPA1*02:01	DPA1*02:02	DPB1*01:01	DPB1*10:01	DQA1*02:01	DQA1*05:01	DQB1*02:01	DQB1*02:02	DRA*01:02	DRA*01:01	DRB1*07:01	DRB1*03:01

Next, we want to check our HLA allele frequencies, and compare them to known frequencies from major populations. Here, we only include alleles with an allele frequency of 5% or higher in our study cohort. By default, MiDAS will output comparisons including the following populations, based on published data from allelefrequencies.net:

MiDAS comes with some pre-defined reference populations, but it is possible to customize these comparisons (see documentation).

freq_HLA <- getHlaFrequencies(hla_calls = dat_HLA, compare = TRUE) %>%
  filter(Freq > 0.01)

HLA frequencies, compared to published references
allele	Counts	Freq	USA NMDP African American pop 2	USA NMDP Chinese	USA NMDP European Caucasian	USA NMDP Hispanic South or Central American	USA NMDP Japanese	USA NMDP North American Amerindian	USA NMDP South Asian Indian
A*01:01	236	0.1180	0.0467	0.0145	0.1646	0.0726	0.0100	0.1202	0.1545
A*02:01	486	0.2430	0.1235	0.0946	0.2755	0.2095	0.1480	0.2776	0.0492
A*02:06	22	0.0110	0.0007	0.0349	0.0018	0.0198	0.0748	0.0275	0.0175
A*03:01	199	0.0995	0.0839	0.0140	0.1399	0.0738	0.0090	0.1044	0.0636
A*11:01	114	0.0570	0.0142	0.2752	0.0609	0.0456	0.0874	0.0488	0.1396
A*23:01	47	0.0235	0.1099	0.0021	0.0197	0.0368	0.0011	0.0181	0.0066

Let’s assume our cohort is of predominantly European ancestry. We can plot the following comparison to see if allele frequencies in our data are distributed as expected, for example by visualizing common HLA-A allele frequencies in comparison with European, Chinese, and African American reference populations:

freq_HLA_long <- tidyr::gather(
  data = freq_HLA,
  key = "population",
  value = "freq",
  "Freq",
  "USA NMDP European Caucasian",
  "USA NMDP Chinese",
  "USA NMDP African American pop 2",
  factor_key = TRUE
) %>% 
  filter(grepl("^A", allele))

plot_HLAfreq <-
  ggplot2::ggplot(data = freq_HLA_long, ggplot2::aes(x = allele, y = freq, fill = population)) +
  ggplot2::geom_bar(
    stat = "identity",
    position = ggplot2::position_dodge(0.7),
    width = 0.7,
    colour = "black"
  ) +
  ggplot2::coord_flip() +
  ggplot2::scale_y_continuous(labels = formattable::percent)

plot_HLAfreq

HLA association analysis

Are classical HLA alleles associated with disease status?

The following function prepares our data for analysis, combining HLA and phenotypic data into one object. Here, we are interested in analyzing our data on the level of classical HLA alleles.

HLA <- prepareMiDAS(
  hla_calls = dat_HLA,
  colData = dat_pheno,
  experiment = "hla_alleles"
)

We can now test our HLA data for deviations from Hardy-Weinberg-Equilibrium (HWE) to filter out alleles that strongly deviate from HWE expectations (imputation or genotyping errors, …). Here, let’s remove alleles with a HWE p-value below 0.05 divided by the number of alleles tested / present in our data (N=447). We can create a filtered MiDAS object right away (as.MiDAS = TRUE), as done in this example, or output actual HWE test results.

HLA <- HWETest(
  object = HLA,
  experiment = "hla_alleles",
  HWE_cutoff = 0.05 / 447,
  as.MiDAS = TRUE
)

Now, we define our statistical model and run the analysis. Since we want to test for association with disease status, we use a logistic regression approach. The term is necessary as placeholder for the tested HLA alleles and needs to be included. It will become handy when for example defining more complex interaction models.

In the runMiDAS function, we then refer to this model, choose our analysis type and define a inheritance model. Here we use dominant model, meaning that individuals will be defined as non-carriers (0) vs. carriers (1) for a given allele. Alternatively, it is also possible to choose recessive (0 = non-carrier or heterozygous carrier, 1 = homozygous carrier), overdominant (assuming heterozygous (dis)advantage: 0 = non-carrier or homozygous carrier, 1 = heterozygous carrier), or additive (N of alleles) inheritance models. Moreover, we define allele inclusion criteria, such that we only consider alleles frequencies above 2% and below 98%. We also apply the Bonferroni method to not only get nominal P values, but also such adjusted for multiple testing. For alternative multiple testing correction methods, as well as the option to choose a custom number of tests, please refer to the documentation. exponentiate = TRUE means that the effect estimate will already be shown as odds ratio, since we use a logistic regression model.

HLA_model <- glm(disease ~ term, data = HLA, family = binomial())
HLA_results <- runMiDAS(
  object = HLA_model, 
  experiment = "hla_alleles", 
  inheritance_model = "dominant",
  lower_frequency_cutoff = 0.02, 
  upper_frequency_cutoff = 0.98, 
  correction = "bonferroni", 
  exponentiate = TRUE
)

kableResults(HLA_results)

MiDAS analysis results
allele	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic	Ntotal	Ntotal [%]	N(disease=0)	N(disease=0) [%]	N(disease=1)	N(disease=1) [%]
DQB1*06:02	2.600e-06	2.290e-04	2.1719	0.1651	1.5764	3.0138	4.698e+00	198	9.90%	69	6.90%	129	12.90%
DRB1*15:01	7.300e-06	6.386e-04	2.0649	0.1617	1.5081	2.8450	4.484e+00	206	10.30%	74	7.40%	132	13.20%
B*57:01	3.080e-05	2.682e-03	2.9931	0.2631	1.8157	5.1189	4.167e+00	79	3.95%	21	2.10%	58	5.80%
C*07:02	1.859e-04	1.618e-02	1.8227	0.1606	1.3334	2.5044	3.737e+00	204	10.20%	78	7.80%	126	12.60%
B*18:01	6.012e-04	5.230e-02	0.5014	0.2012	0.3357	0.7400	-3.431e+00	122	6.10%	79	7.90%	43	4.30%
DRA*01:02	2.701e-03	2.350e-01	1.4703	0.1285	1.1435	1.8926	3.000e+00	573	28.65%	263	26.30%	310	31.00%
DQB1*02:02	4.569e-03	3.975e-01	0.6257	0.1654	0.4513	0.8635	-2.836e+00	185	9.25%	110	11.00%	75	7.50%
B*51:01	4.863e-03	4.231e-01	1.7271	0.1941	1.1849	2.5396	2.816e+00	128	6.40%	49	4.90%	79	7.90%
DRA*01:01	1.073e-02	9.331e-01	0.6210	0.1867	0.4289	0.8930	-2.552e+00	862	43.10%	445	44.50%	417	41.70%
B*44:02	2.068e-02	1.000e+00	1.4963	0.1742	1.0655	2.1111	2.314e+00	161	8.05%	67	6.70%	94	9.40%
C*08:02	2.076e-02	1.000e+00	1.8030	0.2549	1.1023	3.0076	2.312e+00	71	3.55%	26	2.60%	45	4.50%
DQA1*04:01	2.235e-02	1.000e+00	1.9179	0.2851	1.1089	3.4120	2.284e+00	57	2.85%	20	2.00%	37	3.70%
DQB1*05:02	3.106e-02	1.000e+00	0.5645	0.2652	0.3313	0.9417	-2.156e+00	65	3.25%	41	4.10%	24	2.40%
DRB1*11:01	3.188e-02	1.000e+00	0.6651	0.1900	0.4565	0.9630	-2.146e+00	131	6.55%	77	7.70%	54	5.40%
DRB1*07:01	3.518e-02	1.000e+00	0.7266	0.1517	0.5390	0.9772	-2.106e+00	228	11.40%	128	12.80%	100	10.00%
A*31:01	6.065e-02	1.000e+00	1.6857	0.2784	0.9846	2.9492	1.876e+00	58	2.90%	22	2.20%	36	3.60%
B*08:01	8.299e-02	1.000e+00	0.7130	0.1951	0.4847	1.0431	-1.734e+00	122	6.10%	70	7.00%	52	5.20%
DQA1*01:02	9.218e-02	1.000e+00	1.2555	0.1351	0.9637	1.6371	1.684e+00	327	16.35%	151	15.10%	176	17.60%
B*07:02	1.037e-01	1.000e+00	1.3456	0.1825	0.9424	1.9292	1.627e+00	142	7.10%	62	6.20%	80	8.00%
B*35:01	1.043e-01	1.000e+00	0.6811	0.2365	0.4254	1.0787	-1.624e+00	80	4.00%	47	4.70%	33	3.30%
DQB1*04:02	1.069e-01	1.000e+00	1.5610	0.2762	0.9141	2.7147	1.612e+00	58	2.90%	23	2.30%	35	3.50%
DRB1*03:01	1.102e-01	1.000e+00	0.7737	0.1606	0.5639	1.0592	-1.597e+00	194	9.70%	107	10.70%	87	8.70%
DQA1*02:01	1.177e-01	1.000e+00	0.7913	0.1496	0.5895	1.0604	-1.565e+00	235	11.75%	128	12.80%	107	10.70%
C*03:03	1.291e-01	1.000e+00	0.6974	0.2374	0.4349	1.1070	-1.518e+00	79	3.95%	46	4.60%	33	3.30%
C*07:01	1.309e-01	1.000e+00	0.7952	0.1517	0.5901	1.0700	-1.511e+00	226	11.30%	123	12.30%	103	10.30%
DQB1*02:01	1.314e-01	1.000e+00	0.7860	0.1596	0.5740	1.0739	-1.509e+00	197	9.85%	108	10.80%	89	8.90%
DPB1*104:01	1.341e-01	1.000e+00	0.6295	0.3089	0.3380	1.1441	-1.498e+00	46	2.30%	28	2.80%	18	1.80%
DQA1*01:01	1.613e-01	1.000e+00	1.2610	0.1656	0.9122	1.7473	1.401e+00	179	8.95%	81	8.10%	98	9.80%
DQB1*03:01	1.699e-01	1.000e+00	0.8355	0.1309	0.6461	1.0797	-1.373e+00	373	18.65%	197	19.70%	176	17.60%
A*02:01	1.796e-01	1.000e+00	1.1874	0.1280	0.9241	1.5265	1.342e+00	427	21.35%	203	20.30%	224	22.40%
DQA1*05:05	2.041e-01	1.000e+00	0.8356	0.1414	0.6330	1.1022	-1.270e+00	278	13.90%	148	14.80%	130	13.00%
A*11:01	2.223e-01	1.000e+00	0.7788	0.2049	0.5194	1.1620	-1.220e+00	108	5.40%	60	6.00%	48	4.80%
DPA1*01:03	2.340e-01	1.000e+00	1.3315	0.2406	0.8330	2.1466	1.190e+00	924	46.20%	457	45.70%	467	46.70%
DQB1*03:03	2.340e-01	1.000e+00	1.3315	0.2406	0.8330	2.1466	1.190e+00	76	3.80%	33	3.30%	43	4.30%
DQA1*05:01	2.369e-01	1.000e+00	0.8294	0.1582	0.6076	1.1303	-1.183e+00	201	10.05%	108	10.80%	93	9.30%
A*25:01	2.563e-01	1.000e+00	0.7211	0.2881	0.4056	1.2630	-1.135e+00	52	2.60%	30	3.00%	22	2.20%
B*27:05	2.728e-01	1.000e+00	0.7374	0.2778	0.4239	1.2666	-1.097e+00	56	2.80%	32	3.20%	24	2.40%
DPB1*04:02	2.921e-01	1.000e+00	0.8426	0.1625	0.6120	1.1582	-1.054e+00	187	9.35%	100	10.00%	87	8.70%
A*03:01	2.960e-01	1.000e+00	1.1835	0.1612	0.8632	1.6250	1.045e+00	191	9.55%	89	8.90%	102	10.20%
DPB1*17:01	2.973e-01	1.000e+00	0.7299	0.3020	0.3991	1.3139	-1.042e+00	47	2.35%	27	2.70%	20	2.00%
C*03:04	2.978e-01	1.000e+00	1.2747	0.2331	0.8086	2.0229	1.041e+00	81	4.05%	36	3.60%	45	4.50%
DQA1*03:01	2.986e-01	1.000e+00	1.1981	0.1738	0.8526	1.6870	1.040e+00	158	7.90%	73	7.30%	85	8.50%
DPB1*13:01	3.067e-01	1.000e+00	0.7390	0.2959	0.4093	1.3149	-1.022e+00	49	2.45%	28	2.80%	21	2.10%
DQA1*01:03	3.208e-01	1.000e+00	1.1970	0.1811	0.8398	1.7102	9.928e-01	143	7.15%	66	6.60%	77	7.70%
DRB1*04:01	3.369e-01	1.000e+00	1.2288	0.2145	0.8079	1.8775	9.604e-01	97	4.85%	44	4.40%	53	5.30%
DQB1*05:03	3.760e-01	1.000e+00	0.7680	0.2981	0.4239	1.3736	-8.854e-01	48	2.40%	27	2.70%	21	2.10%
DQA1*01:04	3.938e-01	1.000e+00	0.7831	0.2867	0.4426	1.3704	-8.528e-01	52	2.60%	29	2.90%	23	2.30%
A*32:01	4.177e-01	1.000e+00	0.8022	0.2720	0.4674	1.3647	-8.104e-01	58	2.90%	32	3.20%	26	2.60%
A*23:01	4.558e-01	1.000e+00	0.7993	0.3005	0.4394	1.4375	-7.457e-01	47	2.35%	26	2.60%	21	2.10%
DQB1*06:01	4.558e-01	1.000e+00	1.2512	0.3005	0.6956	2.2759	7.457e-01	47	2.35%	21	2.10%	26	2.60%
B*40:01	4.958e-01	1.000e+00	1.2050	0.2738	0.7053	2.0743	6.812e-01	57	2.85%	26	2.60%	31	3.10%
C*12:03	5.147e-01	1.000e+00	0.8856	0.1865	0.6135	1.2760	-6.516e-01	133	6.65%	70	7.00%	63	6.30%
DRB1*13:01	5.273e-01	1.000e+00	1.1428	0.2112	0.7557	1.7330	6.321e-01	100	5.00%	47	4.70%	53	5.30%
A*29:02	5.379e-01	1.000e+00	0.8264	0.3096	0.4463	1.5145	-6.159e-01	44	2.20%	24	2.40%	20	2.00%
DRB1*01:01	5.622e-01	1.000e+00	0.8939	0.1934	0.6108	1.3059	-5.795e-01	122	6.10%	64	6.40%	58	5.80%
A*68:01	5.692e-01	1.000e+00	0.8499	0.2857	0.4823	1.4870	-5.692e-01	52	2.60%	28	2.80%	24	2.40%
B*44:03	5.709e-01	1.000e+00	0.8792	0.2271	0.5616	1.3720	-5.667e-01	85	4.25%	45	4.50%	40	4.00%
DRB1*13:02	5.750e-01	1.000e+00	1.1343	0.2247	0.7303	1.7673	5.607e-01	87	4.35%	41	4.10%	46	4.60%
DRB1*11:04	5.789e-01	1.000e+00	0.8839	0.2224	0.5699	1.3666	-5.550e-01	89	4.45%	47	4.70%	42	4.20%
DQB1*03:02	6.084e-01	1.000e+00	1.0915	0.1709	0.7808	1.5273	5.123e-01	164	8.20%	79	7.90%	85	8.50%
B*13:02	6.202e-01	1.000e+00	0.8843	0.2483	0.5415	1.4384	-4.955e-01	70	3.50%	37	3.70%	33	3.30%
DPA1*02:01	6.217e-01	1.000e+00	0.9328	0.1410	0.7072	1.2298	-4.935e-01	279	13.95%	143	14.30%	136	13.60%
B*15:01	6.412e-01	1.000e+00	0.8969	0.2334	0.5659	1.4173	-4.661e-01	80	4.00%	42	4.20%	38	3.80%
DRB1*04:04	6.542e-01	1.000e+00	0.8745	0.2994	0.4829	1.5731	-4.480e-01	47	2.35%	25	2.50%	22	2.20%
B*38:01	6.665e-01	1.000e+00	0.8833	0.2880	0.4993	1.5537	-4.310e-01	51	2.55%	27	2.70%	24	2.40%
DQB1*06:04	7.083e-01	1.000e+00	1.0980	0.2498	0.6727	1.7976	3.742e-01	69	3.45%	33	3.30%	36	3.60%
DPB1*03:01	7.087e-01	1.000e+00	0.9325	0.1869	0.6457	1.3454	-3.736e-01	132	6.60%	68	6.80%	64	6.40%
DPB1*02:01	7.233e-01	1.000e+00	0.9511	0.1417	0.7203	1.2555	-3.541e-01	275	13.75%	140	14.00%	135	13.50%
C*12:02	7.526e-01	1.000e+00	0.9053	0.3156	0.4842	1.6832	-3.152e-01	42	2.10%	22	2.20%	20	2.00%
A*24:02	7.586e-01	1.000e+00	1.0484	0.1537	0.7756	1.4176	3.074e-01	216	10.80%	106	10.60%	110	11.00%
C*05:01	7.899e-01	1.000e+00	0.9538	0.1776	0.6728	1.3513	-2.664e-01	149	7.45%	76	7.60%	73	7.30%
DPB1*01:01	8.067e-01	1.000e+00	0.9419	0.2448	0.5814	1.5231	-2.446e-01	72	3.60%	37	3.70%	35	3.50%
C*02:02	8.215e-01	1.000e+00	1.0522	0.2256	0.6756	1.6408	2.256e-01	86	4.30%	42	4.20%	44	4.40%
C*01:02	8.316e-01	1.000e+00	1.0463	0.2128	0.6891	1.5902	2.127e-01	98	4.90%	48	4.80%	50	5.00%
DQB1*06:03	8.385e-01	1.000e+00	1.0424	0.2038	0.6987	1.5563	2.038e-01	108	5.40%	53	5.30%	55	5.50%
DQB1*05:01	8.724e-01	1.000e+00	1.0261	0.1606	0.7489	1.4063	1.606e-01	192	9.60%	95	9.50%	97	9.70%
A*30:02	8.761e-01	1.000e+00	0.9526	0.3119	0.5141	1.7603	-1.559e-01	43	2.15%	22	2.20%	21	2.10%
DRB1*04:05	8.812e-01	1.000e+00	1.0457	0.2989	0.5806	1.8875	1.494e-01	47	2.35%	23	2.30%	24	2.40%
C*15:02	8.812e-01	1.000e+00	0.9563	0.2989	0.5298	1.7224	-1.494e-01	47	2.35%	24	2.40%	23	2.30%
C*16:01	8.835e-01	1.000e+00	0.9580	0.2930	0.5370	1.7054	-1.465e-01	49	2.45%	25	2.50%	24	2.40%
DQA1*03:03	9.248e-01	1.000e+00	0.9824	0.1887	0.6781	1.4227	-9.434e-02	129	6.45%	65	6.50%	64	6.40%
C*06:02	9.348e-01	1.000e+00	0.9867	0.1636	0.7158	1.3601	-8.178e-02	183	9.15%	92	9.20%	91	9.10%
C*04:01	9.396e-01	1.000e+00	1.0115	0.1515	0.7516	1.3615	7.573e-02	225	11.25%	112	11.20%	113	11.30%
A*26:01	1.000e+00	1.000e+00	1.0000	0.2232	0.6447	1.5510	0.000e+00	88	4.40%	44	4.40%	44	4.40%
A*01:01	1.000e+00	1.000e+00	1.0000	0.1522	0.7419	1.3479	0.000e+00	222	11.10%	111	11.10%	111	11.10%
B*49:01	1.000e+00	1.000e+00	1.0000	0.3153	0.5367	1.8633	0.000e+00	42	2.10%	21	2.10%	21	2.10%
B*52:01	1.000e+00	1.000e+00	1.0000	0.3153	0.5367	1.8633	0.000e+00	42	2.10%	21	2.10%	21	2.10%

Three HLA alleles show significant association with the disease after multiple testing adjustment. Due to the complex linkage disequilibrium structure in the MHC, it is likely that associations are not statistically independent. The two alleles HLA-DRB1*15:01 and HLA-DQB1*06:02 are a common class II haplotype. We can therefore test if there are associations that are statistically independent of our top-associated allele, by setting the conditional flag to TRUE. MiDAS will now perform stepwise conditional testing until the top associated allele does not reach a defined significance threshold (here th = 0.05, based on adjusted p value).

HLA_results_cond <- runMiDAS(
  object = HLA_model, 
  experiment = "hla_alleles", 
  inheritance_model = "dominant", 
  conditional = TRUE,
  lower_frequency_cutoff = 0.02, 
  upper_frequency_cutoff = 0.98, 
  correction = "bonferroni", 
  exponentiate = TRUE
)

kableResults(HLA_results_cond, scroll_box_height = "200px")

MiDAS analysis results
allele	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic	covariates	Ntotal	Ntotal [%]	N(disease=0)	N(disease=0) [%]	N(disease=1)	N(disease=1) [%]
DQB1*06:02	2.60e-06	2.290e-04	2.172	0.1651	1.576	3.014	4.698		198	9.90%	69	6.90%	129	12.90%
B*57:01	2.64e-05	2.269e-03	3.049	0.2653	1.841	5.235	4.203	DQB1*06:02	79	3.95%	21	2.10%	58	5.80%

The results for conditional testing are displayed in a way that for each step the top associated allele is shown, along with a list of alleles conditioned on.

As we can see, HLA-DRB1*15:01 was not independently associated with the disease when correcting for our top-associated allele HLA-DQB1*06:02. However, HLA-B*57:01 can be considered an independent association signal.

HLA association fine-mapping on amino acid level

Next, we want to find out what are the strongest associated amino acid positions, corresponding to our allele-level associations. This can help fine-mapping the associated variants to e.g. the peptide binding region or other functionally distinct parts of the protein. We thus prepare a MiDAS object with experiment type “hla_aa”, which includes the inference of amino acid variation from allele calls.

HLA_AA <- prepareMiDAS(
  hla_calls = dat_HLA,
  colData = dat_pheno,
  experiment = "hla_aa"
)

Amino acid data will be stored in a MiDAS object, but we can extract it to a data frame and select a couple of variables to display how this looks like:

dat_HLA_AA <- HLA_AA[["hla_aa"]] %>% 
  assay() %>% 
  t() %>% 
  as.data.frame() %>% 
  select(starts_with("B_97_")) %>% 
  head()

HLA amino acid data as inferred by MiDAS
	B_97_S	B_97_R	B_97_T
C001	1	1	0
C002	0	2	0
C003	0	1	1
C004	1	1	0
C005	1	1	0
C006	2	0	0

Now, we run the association test based on amino acid variation. To first identify the most relevant associated amino acid positions, we run a likelihood ratio (omnibus) test, which groups all residues at each amino acid position.

HLA_AA_model <- glm(disease ~ term, data = HLA_AA, family = binomial())
HLA_AA_omnibus_results <- runMiDAS(
  HLA_AA_model,
  experiment = "hla_aa",
  inheritance_model = "dominant",
  conditional = FALSE,
  omnibus = TRUE,
  lower_frequency_cutoff = 0.02,
  upper_frequency_cutoff = 0.98,
  correction = "bonferroni"
)

kableResults(HLA_AA_omnibus_results)

MiDAS analysis results
aa_pos	residues	df	statistic	p.value	p.adjusted
B_97	S, R, T, N, V, W	6	3.532e+01	3.700e-06	1.573e-03
DQB1_9	F, Y, *	3	2.569e+01	1.110e-05	4.682e-03
DQB1_185	*, T	2	1.881e+01	8.240e-05	3.476e-02
DQB1_142	*, V	2	1.788e+01	1.313e-04	5.542e-02
DQB1_177	*, H	2	1.788e+01	1.313e-04	5.542e-02
DQB1_167	*, R	2	1.729e+01	1.759e-04	7.422e-02
DQB1_116	*, V	2	1.695e+01	2.081e-04	8.782e-02
DQB1_125	*, A	2	1.695e+01	2.081e-04	8.782e-02
B_81	L, A	2	1.689e+01	2.154e-04	9.092e-02
DQB1_130	*, R	2	1.663e+01	2.451e-04	1.034e-01
DQB1_126	*, Q	2	1.636e+01	2.807e-04	1.185e-01
B_80	N, T, I	3	1.866e+01	3.209e-04	1.354e-01
B_82	R, L	2	1.582e+01	3.674e-04	1.550e-01
B_83	G, R	2	1.582e+01	3.674e-04	1.550e-01
DQB1_140	*, A, T	3	1.836e+01	3.711e-04	1.566e-01
DQB1_182	*, S, N	3	1.836e+01	3.711e-04	1.566e-01
DQB1_135	*, G, D	3	1.815e+01	4.087e-04	1.725e-01
B_62	R, G	2	1.511e+01	5.232e-04	2.208e-01
DRB1_71	A, R, E, K	4	1.984e+01	5.362e-04	2.263e-01
B_275	*, E	2	1.476e+01	6.231e-04	2.630e-01
B_295	*, .	2	1.476e+01	6.231e-04	2.630e-01
B_296	*, A	2	1.476e+01	6.231e-04	2.630e-01
B_297	*, V	2	1.476e+01	6.231e-04	2.630e-01
B_299	*, V	2	1.476e+01	6.231e-04	2.630e-01
B_300	*, I	2	1.476e+01	6.231e-04	2.630e-01
B_301	*, G	2	1.476e+01	6.231e-04	2.630e-01
B_77	S, N, D	3	1.717e+01	6.530e-04	2.756e-01
B_65	Q, R	2	1.385e+01	9.828e-04	4.147e-01
B_70	Q, N, K, S	4	1.849e+01	9.909e-04	4.181e-01
B_239	*, R	2	1.362e+01	1.105e-03	4.664e-01
B_253	*, E	2	1.362e+01	1.105e-03	4.664e-01
B_267	*, P	2	1.362e+01	1.105e-03	4.664e-01
B_268	*, K	2	1.362e+01	1.105e-03	4.664e-01
B_270	*, L	2	1.362e+01	1.105e-03	4.664e-01
B_66	I, N	2	1.330e+01	1.294e-03	5.460e-01
B_194	*, I	2	1.312e+01	1.415e-03	5.970e-01
B_103	V, L	2	1.296e+01	1.537e-03	6.484e-01
B_30	D, G	2	1.283e+01	1.637e-03	6.907e-01
B_-16	*, V	2	1.255e+01	1.886e-03	7.960e-01
B_339	*, A	2	1.242e+01	2.010e-03	8.481e-01
B_199	*, A	2	1.215e+01	2.298e-03	9.696e-01
B_211	*, A	2	1.207e+01	2.395e-03	1.000e+00
DQB1_55	R, L, P	3	1.420e+01	2.647e-03	1.000e+00
C_99	Y, C, F, S	4	1.624e+01	2.714e-03	1.000e+00
DRA_217	L, V	2	1.173e+01	2.830e-03	1.000e+00
B_9	Y, H, D	3	1.370e+01	3.340e-03	1.000e+00
B_69	A, T	2	1.122e+01	3.661e-03	1.000e+00
DQB1_38	A, V	2	1.081e+01	4.489e-03	1.000e+00
DQB1_77	T, R, *	3	1.287e+01	4.925e-03	1.000e+00
B_67	Y, S, F, C, M	5	1.666e+01	5.181e-03	1.000e+00
B_71	A, T	2	1.044e+01	5.405e-03	1.000e+00
DQB1_87	F, L, *, Y	4	1.424e+01	6.568e-03	1.000e+00
DQB1_30	Y, S, H	3	1.148e+01	9.382e-03	1.000e+00
B_-21	*, T	2	9.202e+00	1.004e-02	1.000e+00
B_-23	*, R	2	9.202e+00	1.004e-02	1.000e+00
DQB1_28	T, S	2	9.008e+00	1.106e-02	1.000e+00
DQB1_46	V, E	2	9.008e+00	1.106e-02	1.000e+00
DQB1_47	Y, F	2	9.008e+00	1.106e-02	1.000e+00
DQB1_52	P, L	2	9.008e+00	1.106e-02	1.000e+00
B_-10	*, G	2	8.724e+00	1.275e-02	1.000e+00
B_-9	*, A	2	8.473e+00	1.446e-02	1.000e+00
DQB1_74	E, A, S, *	4	1.222e+01	1.579e-02	1.000e+00
B_163	E, L, T	3	1.031e+01	1.610e-02	1.000e+00
B_282	*	1	5.561e+00	1.836e-02	1.000e+00
B_306	*	1	5.561e+00	1.836e-02	1.000e+00
B_326	*	1	5.561e+00	1.836e-02	1.000e+00
DQA1_107	I, *, T	3	9.899e+00	1.945e-02	1.000e+00
DQA1_156	L, *, F	3	9.899e+00	1.945e-02	1.000e+00
DQA1_161	E, *, D	3	9.899e+00	1.945e-02	1.000e+00
DQA1_163	S, *, I	3	9.899e+00	1.945e-02	1.000e+00
DQB1_71	T, K, D, A, *	5	1.324e+01	2.121e-02	1.000e+00
DQB1_57	D, A, V, S	4	1.140e+01	2.237e-02	1.000e+00
DQB1_37	Y, I, D	3	9.592e+00	2.237e-02	1.000e+00
DRB1_25	R, Q	2	7.501e+00	2.350e-02	1.000e+00
DRB1_14	E, K	2	7.447e+00	2.415e-02	1.000e+00
DQA1_175	K, *, Q, E	4	1.122e+01	2.421e-02	1.000e+00
DRB1_13	R, Y, S, F, H, G	6	1.442e+01	2.532e-02	1.000e+00
DQA1_75	S, I	2	7.203e+00	2.729e-02	1.000e+00
DQB1_75	L, V, *	3	9.131e+00	2.760e-02	1.000e+00
DPB1_85	G, E	2	7.165e+00	2.781e-02	1.000e+00
DPB1_86	P, A	2	7.165e+00	2.781e-02	1.000e+00
DPB1_87	M, V	2	7.165e+00	2.781e-02	1.000e+00
DQA1_-16	M, *, L	3	8.964e+00	2.977e-02	1.000e+00
DRB1_11	P, G, S, V, L	5	1.229e+01	3.107e-02	1.000e+00
B_-8	*, V	2	6.935e+00	3.120e-02	1.000e+00
DQA1_69	L, A, T	3	8.813e+00	3.189e-02	1.000e+00
DPB1_56	E, A	2	6.804e+00	3.331e-02	1.000e+00
B_-11	*, W	2	6.510e+00	3.858e-02	1.000e+00
DRB1_73	A, G	2	6.484e+00	3.909e-02	1.000e+00
DQA1_56	., G, R	3	8.283e+00	4.051e-02	1.000e+00
DQA1_76	L, M, V	3	8.283e+00	4.051e-02	1.000e+00
B_63	N, E	2	6.264e+00	4.363e-02	1.000e+00
B_143	T, S	2	6.242e+00	4.412e-02	1.000e+00
B_147	W, L	2	6.242e+00	4.412e-02	1.000e+00
DQB1_53	Q, L	2	6.167e+00	4.581e-02	1.000e+00
DRB1_70	Q, D, R	3	7.952e+00	4.701e-02	1.000e+00
B_178	K, T	2	6.097e+00	4.744e-02	1.000e+00
DQA1_129	H, *, Q	3	7.675e+00	5.323e-02	1.000e+00
DRB1_78	Y, V	2	5.806e+00	5.486e-02	1.000e+00
B_45	E, K, M, T	4	9.174e+00	5.689e-02	1.000e+00
B_152	E, V	2	5.717e+00	5.736e-02	1.000e+00
A_156	W, L, Q, R	4	9.095e+00	5.876e-02	1.000e+00
DPB1_84	G, D	2	5.518e+00	6.335e-02	1.000e+00
DQA1_47	C, K, R, Q	4	8.866e+00	6.454e-02	1.000e+00
B_167	W, S	2	5.462e+00	6.514e-02	1.000e+00
DPB1_69	E, K, R	3	7.206e+00	6.560e-02	1.000e+00
DRB1_96	*, H	2	5.296e+00	7.080e-02	1.000e+00
DRB1_98	*, K	2	5.296e+00	7.080e-02	1.000e+00
DRB1_104	*, S	2	5.296e+00	7.080e-02	1.000e+00
DRB1_120	*, S	2	5.296e+00	7.080e-02	1.000e+00
DRB1_149	*, H	2	5.296e+00	7.080e-02	1.000e+00
DRB1_180	*, V	2	5.296e+00	7.080e-02	1.000e+00
DRB1_58	A, E	2	5.167e+00	7.550e-02	1.000e+00
DQA1_54	F, L	2	4.945e+00	8.436e-02	1.000e+00
DQB1_56	P, L	2	4.874e+00	8.743e-02	1.000e+00
DRB1_74	A, Q, E, R, L	5	9.459e+00	9.208e-02	1.000e+00
B_116	Y, L, S, F, D	5	9.438e+00	9.281e-02	1.000e+00
DRB1_77	T, N	2	4.705e+00	9.511e-02	1.000e+00
DQB1_66	E, D	2	4.696e+00	9.554e-02	1.000e+00
DQB1_67	V, I	2	4.696e+00	9.554e-02	1.000e+00
DQB1_23	R	1	2.777e+00	9.565e-02	1.000e+00
B_46	E, A	2	4.545e+00	1.030e-01	1.000e+00
DQA1_18	S, F	2	4.503e+00	1.052e-01	1.000e+00
DQA1_45	V, A	2	4.503e+00	1.052e-01	1.000e+00
DQA1_48	L, W	2	4.503e+00	1.052e-01	1.000e+00
DQA1_55	R, G	2	4.503e+00	1.052e-01	1.000e+00
DQA1_61	F, G	2	4.503e+00	1.052e-01	1.000e+00
DQA1_64	T, R	2	4.503e+00	1.052e-01	1.000e+00
DQA1_66	I, M	2	4.503e+00	1.052e-01	1.000e+00
DQA1_80	S, Y	2	4.503e+00	1.052e-01	1.000e+00
A_163	R, T	2	4.467e+00	1.072e-01	1.000e+00
DQA1_11	Y, C	2	4.452e+00	1.080e-01	1.000e+00
A_90	D, A	2	4.340e+00	1.142e-01	1.000e+00
DPA1_31	M, Q	2	4.297e+00	1.167e-01	1.000e+00
DPA1_50	Q, R	2	4.237e+00	1.202e-01	1.000e+00
A_245	*, A	2	4.225e+00	1.210e-01	1.000e+00
B_171	Y, H	2	4.132e+00	1.267e-01	1.000e+00
DQB1_84	E, Q, *	3	5.693e+00	1.276e-01	1.000e+00
DQB1_85	V, L, *	3	5.693e+00	1.276e-01	1.000e+00
DQB1_89	G, T, *	3	5.693e+00	1.276e-01	1.000e+00
DQB1_90	I, T, *	3	5.693e+00	1.276e-01	1.000e+00
DPB1_36	V, A	2	4.077e+00	1.302e-01	1.000e+00
DQB1_70	G, R, E, *	4	7.108e+00	1.303e-01	1.000e+00
DQA1_130	S, *, A	3	5.630e+00	1.311e-01	1.000e+00
DRB1_37	S, F, N, Y, L	5	8.451e+00	1.331e-01	1.000e+00
DQA1_102	L, *	2	4.014e+00	1.344e-01	1.000e+00
DQA1_138	T, *	2	4.014e+00	1.344e-01	1.000e+00
DQA1_139	S, *	2	4.014e+00	1.344e-01	1.000e+00
DRB1_10	Q, Y	2	3.965e+00	1.377e-01	1.000e+00
DQA1_50	V, L, E	3	5.421e+00	1.435e-01	1.000e+00
DQA1_53	Q, R, K	3	5.421e+00	1.435e-01	1.000e+00
DQA1_52	R, H, S	3	5.388e+00	1.455e-01	1.000e+00
A_105	P, S	2	3.819e+00	1.482e-01	1.000e+00
A_193	*, A	2	3.707e+00	1.567e-01	1.000e+00
A_194	*, V	2	3.707e+00	1.567e-01	1.000e+00
A_207	*, S	2	3.707e+00	1.567e-01	1.000e+00
A_253	*, Q	2	3.707e+00	1.567e-01	1.000e+00
DQA1_-13	T, *, A	3	5.123e+00	1.630e-01	1.000e+00
DPA1_83	T, A	2	3.592e+00	1.660e-01	1.000e+00
A_77	S, D, N	3	4.931e+00	1.770e-01	1.000e+00
DRB1_12	K, T	2	3.424e+00	1.805e-01	1.000e+00
C_66	K, N	2	3.422e+00	1.807e-01	1.000e+00
DRB1_60	Y, S, H	3	4.810e+00	1.863e-01	1.000e+00
DQB1_45	G, E	2	3.314e+00	1.907e-01	1.000e+00
DRB1_30	Y, L, C, H	4	6.083e+00	1.930e-01	1.000e+00
DQA1_40	G, E	2	3.267e+00	1.953e-01	1.000e+00
DQA1_51	L, F	2	3.267e+00	1.953e-01	1.000e+00
C_152	E, A	2	3.198e+00	2.021e-01	1.000e+00
C_77	S, N	2	3.197e+00	2.022e-01	1.000e+00
C_80	N, K	2	3.197e+00	2.022e-01	1.000e+00
DPA1_96	*, P	1	1.600e+00	2.059e-01	1.000e+00
B_113	H, Y	2	3.079e+00	2.145e-01	1.000e+00
A_73	T, I	2	3.047e+00	2.180e-01	1.000e+00
DRB1_28	D, E	2	3.036e+00	2.192e-01	1.000e+00
DQA1_160	A, *, D	3	4.381e+00	2.231e-01	1.000e+00
DPB1_55	D, E, A	3	4.338e+00	2.272e-01	1.000e+00
DRB1_33	N, H	2	2.928e+00	2.313e-01	1.000e+00
DQA1_1	E, *	2	2.925e+00	2.317e-01	1.000e+00
DQA1_218	R, *, Q	3	4.267e+00	2.340e-01	1.000e+00
DQA1_41	R, K	2	2.893e+00	2.354e-01	1.000e+00
B_109	L	1	1.387e+00	2.389e-01	1.000e+00
C_-17	*	1	1.387e+00	2.389e-01	1.000e+00
C_-15	*	1	1.387e+00	2.389e-01	1.000e+00
C_-9	*	1	1.387e+00	2.389e-01	1.000e+00
C_1	*	1	1.387e+00	2.389e-01	1.000e+00
C_175	G	1	1.387e+00	2.389e-01	1.000e+00
C_184	*	1	1.387e+00	2.389e-01	1.000e+00
C_219	*	1	1.387e+00	2.389e-01	1.000e+00
C_253	*	1	1.387e+00	2.389e-01	1.000e+00
C_267	*	1	1.387e+00	2.389e-01	1.000e+00
C_275	*	1	1.387e+00	2.389e-01	1.000e+00
C_285	*	1	1.387e+00	2.389e-01	1.000e+00
C_312	*	1	1.387e+00	2.389e-01	1.000e+00
C_345	*	1	1.387e+00	2.389e-01	1.000e+00
A_43	Q	1	1.387e+00	2.389e-01	1.000e+00
DQB1_86	A, E, *, G	4	5.435e+00	2.455e-01	1.000e+00
DQA1_2	D, *, G	3	4.104e+00	2.505e-01	1.000e+00
C_173	E, K	2	2.722e+00	2.564e-01	1.000e+00
A_219	*, R	2	2.718e+00	2.569e-01	1.000e+00
C_103	L, V	2	2.700e+00	2.592e-01	1.000e+00
C_91	G, R	2	2.649e+00	2.659e-01	1.000e+00
DRB1_16	H, Y	2	2.631e+00	2.684e-01	1.000e+00
DPB1_-14	*, T	2	2.558e+00	2.782e-01	1.000e+00
DRB1_57	D, V, A, S	4	5.068e+00	2.804e-01	1.000e+00
DQA1_26	T, S	2	2.541e+00	2.807e-01	1.000e+00
A_282	*, I	2	2.520e+00	2.836e-01	1.000e+00
A_311	*, K	2	2.520e+00	2.836e-01	1.000e+00
C_330	*, A	2	2.517e+00	2.840e-01	1.000e+00
DRB1_32	Y, H	2	2.498e+00	2.868e-01	1.000e+00
B_94	T, I	2	2.473e+00	2.904e-01	1.000e+00
DQB1_13	G, A, *	3	3.735e+00	2.915e-01	1.000e+00
C_24	A, S	2	2.431e+00	2.966e-01	1.000e+00
C_295	*, A	2	2.316e+00	3.142e-01	1.000e+00
C_311	*, A	2	2.316e+00	3.142e-01	1.000e+00
C_313	*, V	2	2.316e+00	3.142e-01	1.000e+00
C_332	*, S	2	2.316e+00	3.142e-01	1.000e+00
DRB1_47	F, Y	2	2.287e+00	3.187e-01	1.000e+00
A_9	Y, F, S, T	4	4.680e+00	3.218e-01	1.000e+00
B_12	V, M	2	2.186e+00	3.352e-01	1.000e+00
DRB1_67	I, L, F	3	3.360e+00	3.394e-01	1.000e+00
B_11	S, A	2	2.145e+00	3.421e-01	1.000e+00
DQA1_207	V, *, M	3	3.336e+00	3.426e-01	1.000e+00
DPB1_11	G, L	2	2.127e+00	3.452e-01	1.000e+00
C_113	Y, H	2	2.014e+00	3.653e-01	1.000e+00
A_109	L	1	8.077e-01	3.688e-01	1.000e+00
A_231	*, V	2	1.979e+00	3.718e-01	1.000e+00
B_145	R, L	2	1.976e+00	3.723e-01	1.000e+00
DPB1_8	L, V	2	1.958e+00	3.758e-01	1.000e+00
C_309	*, V	2	1.876e+00	3.915e-01	1.000e+00
C_327	*, C	2	1.876e+00	3.915e-01	1.000e+00
C_14	R, W	2	1.834e+00	3.998e-01	1.000e+00
C_49	A, E	2	1.834e+00	3.998e-01	1.000e+00
A_76	E, V, A	3	2.935e+00	4.018e-01	1.000e+00
DPB1_178	*, L	2	1.820e+00	4.026e-01	1.000e+00
B_32	Q, L	2	1.766e+00	4.136e-01	1.000e+00
DQB1_14	M, L, *	3	2.820e+00	4.202e-01	1.000e+00
A_144	Q, K	2	1.719e+00	4.234e-01	1.000e+00
DRB1_9	W, E	2	1.605e+00	4.482e-01	1.000e+00
C_194	*, V	2	1.600e+00	4.493e-01	1.000e+00
C_261	*, V	2	1.600e+00	4.493e-01	1.000e+00
C_273	*, R	2	1.600e+00	4.493e-01	1.000e+00
C_114	D, N	2	1.576e+00	4.547e-01	1.000e+00
C_248	*, V	2	1.560e+00	4.583e-01	1.000e+00
C_211	*, A	2	1.554e+00	4.597e-01	1.000e+00
DPB1_76	M, V, I	3	2.568e+00	4.631e-01	1.000e+00
C_147	W, L	2	1.511e+00	4.697e-01	1.000e+00
C_35	R, Q	2	1.502e+00	4.718e-01	1.000e+00
C_-18	*, R	2	1.478e+00	4.775e-01	1.000e+00
C_-5	*, T	2	1.478e+00	4.775e-01	1.000e+00
C_-1	*, A	2	1.478e+00	4.775e-01	1.000e+00
C_284	*, I	2	1.478e+00	4.775e-01	1.000e+00
C_289	*, A	2	1.478e+00	4.775e-01	1.000e+00
C_291	*, L	2	1.478e+00	4.775e-01	1.000e+00
C_314	*, M	2	1.478e+00	4.775e-01	1.000e+00
C_315	*, C	2	1.478e+00	4.775e-01	1.000e+00
DPB1_33	Q	1	4.988e-01	4.800e-01	1.000e+00
DQA1_208	G, *	2	1.451e+00	4.842e-01	1.000e+00
DRB1_86	V, G	2	1.448e+00	4.847e-01	1.000e+00
DQA1_-7	V, *, M	3	2.441e+00	4.860e-01	1.000e+00
A_297	*, V	2	1.434e+00	4.881e-01	1.000e+00
C_270	*, L	2	1.412e+00	4.937e-01	1.000e+00
A_186	*, K	2	1.397e+00	4.973e-01	1.000e+00
A_-16	*, L	2	1.395e+00	4.979e-01	1.000e+00
A_283	*, P	2	1.395e+00	4.979e-01	1.000e+00
C_21	R, H	2	1.394e+00	4.980e-01	1.000e+00
C_186	*, K	2	1.387e+00	4.997e-01	1.000e+00
C_310	*, V	2	1.387e+00	4.997e-01	1.000e+00
A_184	*, A, P	3	2.353e+00	5.024e-01	1.000e+00
C_73	A, T	2	1.376e+00	5.027e-01	1.000e+00
C_163	T, L, E	3	2.260e+00	5.203e-01	1.000e+00
DPA1_11	A, M	2	1.299e+00	5.224e-01	1.000e+00
A_149	T, A	2	1.254e+00	5.343e-01	1.000e+00
A_56	G, R	2	1.228e+00	5.411e-01	1.000e+00
B_41	A, T	2	1.228e+00	5.412e-01	1.000e+00
A_161	E, D	2	1.204e+00	5.478e-01	1.000e+00
DPB1_9	F, Y, H	3	2.109e+00	5.501e-01	1.000e+00
C_138	T, K	2	1.169e+00	5.573e-01	1.000e+00
A_151	H, R	2	1.161e+00	5.597e-01	1.000e+00
DQA1_199	A, *, T	3	2.025e+00	5.672e-01	1.000e+00
DPB1_194	*, R	2	1.111e+00	5.737e-01	1.000e+00
DPB1_215	*, I	2	1.111e+00	5.737e-01	1.000e+00
B_131	R, S	2	1.054e+00	5.902e-01	1.000e+00
A_107	G, W	2	1.047e+00	5.926e-01	1.000e+00
C_94	T, I	2	1.044e+00	5.935e-01	1.000e+00
A_63	N, E, Q	3	1.882e+00	5.973e-01	1.000e+00
DQA1_-6	M, *	2	1.031e+00	5.973e-01	1.000e+00
DRB1_26	F, Y, L	3	1.844e+00	6.054e-01	1.000e+00
A_74	D, H	2	1.002e+00	6.059e-01	1.000e+00
DQA1_187	A, *, T	3	1.819e+00	6.109e-01	1.000e+00
DQA1_215	F, *, L	3	1.819e+00	6.109e-01	1.000e+00
C_90	A, D	2	9.661e-01	6.169e-01	1.000e+00
C_95	L, I	2	9.617e-01	6.183e-01	1.000e+00
C_11	A, S	2	9.525e-01	6.211e-01	1.000e+00
B_158	A, T	2	9.012e-01	6.372e-01	1.000e+00
B_114	D, N, H	3	1.682e+00	6.410e-01	1.000e+00
A_-18	*, R	2	8.755e-01	6.455e-01	1.000e+00
A_-5	*, T	2	8.755e-01	6.455e-01	1.000e+00
A_284	*, I	2	8.755e-01	6.455e-01	1.000e+00
C_156	W, R, L, Q	4	2.473e+00	6.495e-01	1.000e+00
A_66	N, K	2	8.571e-01	6.515e-01	1.000e+00
A_142	I, T	2	8.559e-01	6.519e-01	1.000e+00
A_145	R, H	2	8.559e-01	6.519e-01	1.000e+00
A_-15	*	1	2.024e-01	6.528e-01	1.000e+00
A_294	*	1	2.024e-01	6.528e-01	1.000e+00
A_79	R, G	2	8.418e-01	6.565e-01	1.000e+00
A_80	I, T	2	8.418e-01	6.565e-01	1.000e+00
A_81	A, L	2	8.418e-01	6.565e-01	1.000e+00
A_82	L, R	2	8.418e-01	6.565e-01	1.000e+00
A_83	R, G	2	8.418e-01	6.565e-01	1.000e+00
DPB1_96	*, K, R	3	1.606e+00	6.581e-01	1.000e+00
DPB1_170	*, I, T	3	1.606e+00	6.581e-01	1.000e+00
A_99	Y, F	2	8.308e-01	6.601e-01	1.000e+00
B_177	D, E	2	8.213e-01	6.632e-01	1.000e+00
B_180	E, Q	2	8.213e-01	6.632e-01	1.000e+00
B_24	S, T, A	3	1.536e+00	6.740e-01	1.000e+00
DRB1_31	F, I	2	7.443e-01	6.893e-01	1.000e+00
B_156	R, L, W, D	4	2.250e+00	6.899e-01	1.000e+00
A_-11	*, S	2	7.305e-01	6.940e-01	1.000e+00
A_65	R, G	2	6.831e-01	7.107e-01	1.000e+00
A_-21	*, M	2	6.789e-01	7.122e-01	1.000e+00
DPB1_57	E, D	2	6.767e-01	7.129e-01	1.000e+00
A_95	I, V, L	3	1.360e+00	7.148e-01	1.000e+00
A_127	N, K	2	6.643e-01	7.174e-01	1.000e+00
C_177	E, K	2	6.392e-01	7.264e-01	1.000e+00
C_97	W, R	2	6.277e-01	7.306e-01	1.000e+00
A_44	R, K	2	6.239e-01	7.320e-01	1.000e+00
A_67	V, M	2	6.239e-01	7.320e-01	1.000e+00
A_150	A, V	2	6.239e-01	7.320e-01	1.000e+00
A_158	A, V	2	6.239e-01	7.320e-01	1.000e+00
A_276	*, P	2	6.091e-01	7.374e-01	1.000e+00
A_321	*, S	2	6.091e-01	7.374e-01	1.000e+00
DPB1_205	*, V, M	3	1.192e+00	7.550e-01	1.000e+00
A_298	*, I	2	5.482e-01	7.603e-01	1.000e+00
A_307	*, M	2	5.482e-01	7.603e-01	1.000e+00
DRB1_85	V, A	2	4.685e-01	7.912e-01	1.000e+00
A_246	*, A	2	4.583e-01	7.952e-01	1.000e+00
A_70	H, Q	2	4.549e-01	7.965e-01	1.000e+00
DQA1_25	Y, F	2	4.525e-01	7.975e-01	1.000e+00
B_95	L, W, I	3	9.848e-01	8.049e-01	1.000e+00
A_116	D, Y, H	3	8.461e-01	8.384e-01	1.000e+00
DPB1_65	I, L	2	3.524e-01	8.385e-01	1.000e+00
DPB1_35	F, L, Y	3	8.446e-01	8.388e-01	1.000e+00
C_6	R, K	2	3.499e-01	8.395e-01	1.000e+00
A_17	R, S	2	3.409e-01	8.433e-01	1.000e+00
A_62	R, G, E, Q, L	5	2.026e+00	8.456e-01	1.000e+00
C_9	Y, D, F, S	4	1.236e+00	8.722e-01	1.000e+00
A_299	*, T	2	2.288e-01	8.919e-01	1.000e+00
A_334	*, V	2	2.288e-01	8.919e-01	1.000e+00
DQB1_26	L, Y, G	3	6.006e-01	8.963e-01	1.000e+00
DQA1_34	Q, E	2	1.851e-01	9.116e-01	1.000e+00
A_152	E, V, A, R	4	9.529e-01	9.169e-01	1.000e+00
B_74	D, Y	2	1.542e-01	9.258e-01	1.000e+00
C_116	S, F, Y, L	4	7.424e-01	9.460e-01	1.000e+00
C_16	G, S	2	1.028e-01	9.499e-01	1.000e+00
A_97	R, M, I	3	2.072e-01	9.764e-01	1.000e+00
A_166	E, D	2	2.644e-02	9.869e-01	1.000e+00
A_167	W, G	2	2.644e-02	9.869e-01	1.000e+00
A_114	Q, H, R, E	4	2.952e-01	9.901e-01	1.000e+00
DRB1_38	V, L	2	0.000e+00	1.000e+00	1.000e+00
DPA1_91	*	0	0.000e+00	1.000e+00	1.000e+00
DPA1_111	*	0	0.000e+00	1.000e+00	1.000e+00
DPA1_127	*	0	0.000e+00	1.000e+00	1.000e+00
DPA1_160	*	0	0.000e+00	1.000e+00	1.000e+00
DPA1_190	*	0	0.000e+00	1.000e+00	1.000e+00
DPA1_228	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_-27	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_-21	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_-9	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_-6	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_-5	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_-4	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_203	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_220	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_221	*	0	0.000e+00	1.000e+00	1.000e+00
DQB1_224	*	0	0.000e+00	1.000e+00	1.000e+00
DRB1_-16	*	0	0.000e+00	1.000e+00	1.000e+00
DRB1_189	*	0	0.000e+00	1.000e+00	1.000e+00
DRB1_207	*	0	0.000e+00	1.000e+00	1.000e+00
DRB1_233	*	0	0.000e+00	1.000e+00	1.000e+00

Next, we can investigate how effect estimates are distributed for a given associated amino acid position, e.g. DQB1_9:

HLA_AA_DQB1_9_results <- runMiDAS(
  HLA_AA_model,
  experiment = "hla_aa",
  inheritance_model = "dominant",
  omnibus_groups_filter = "DQB1_9",
  lower_frequency_cutoff = 0.02,
  upper_frequency_cutoff = 0.98,
  correction = "bonferroni",
  exponentiate = TRUE
)

kableResults(HLA_AA_DQB1_9_results, scroll_box_height = "250px")

MiDAS analysis results
aa	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic	Ntotal	Ntotal [%]	N(disease=0)	N(disease=0) [%]	N(disease=1)	N(disease=1) [%]
DQB1_9_F	7.000e-07	2.000e-06	2.0572	0.1451	1.5509	2.740	4.973	277	13.85%	103	10.30%	174	17.40%
DQB1_9_Y	1.103e-01	3.308e-01	0.5876	0.3329	0.2994	1.116	-1.597	960	48.00%	485	48.50%	475	47.50%
DQB1_9_*	1.000e+00	1.000e+00	1.0000	0.2167	0.6531	1.531	0.000	94	4.70%	47	4.70%	47	4.70%

This shows us that individuals carrying a Phenylalanine (F) at position 9 of DQB1 have a significantly increased risk, whereas individuals carrying a Tyrosine (Y) at the same amino acid position have a decreased risk.

It is logical to hypothesize that the risk residue is found on HLA-DQB1*06:02, the previously associated HLA risk allele. MiDAS thus provides the function getAllelesforAA to map amino acid residues back to the respective HLA alleles.

HLA_AA_DQB1_9_alleles <- getAllelesForAA(HLA_AA,"DQB1_9")

HLA-DQB1 (9)	HLA-DQB1 alleles	count	frequency
	05:03, 06:01	97	4.85%
F	04:01, 04:02, 04:23, 06:02	302	15.10%
Y	02:01, 02:02, 02:10, 03:01, 03:02, 03:03, 03:04, 03:05, 03:19, 03:22, 03:251, 03:96, 05:01, 05:02, 05:04, 05:107, 06:03, 06:04, 06:07, 06:09	1601	80.05%

Finally, it is also interesting to note that there are several amino acid positions coming up that determine the Bw4 binding motif (e.g. B_81), which is a determinant for interactions of HLA class I alleles with KIR on Natural Killer cells.

Let’s assume outcome differences in our disease of interest have been shown to be related to NK cell biology. HLA class I alleles can be grouped according to how they interact with KIR, expressed on NK cells. We here now prepare a new MiDAS object, grouping HLA alleles into categories defining their interactions with KIRs (Bw4 vs. Bw6, C1 vs. C2). Then we test these variables for association with disease outcome:

NKlig <- prepareMiDAS(
  hla_calls = dat_HLA,
  colData = dat_pheno,
  experiment = c("hla_alleles", "hla_NK_ligands")
)

NKlig_model <- glm(disease ~ term, data = NKlig, family = binomial())
NKlig_results <- runMiDAS(
  NKlig_model,
  experiment = "hla_NK_ligands",
  inheritance_model = "dominant",
  correction = "bonferroni",
  exponentiate = TRUE
)

kableResults(NKlig_results)

MiDAS analysis results
allele.group	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic	Ntotal	Ntotal [%]	N(disease=0)	N(disease=0) [%]	N(disease=1)	N(disease=1) [%]
Bw6	4.249e-04	2.124e-03	0.5747	0.1572	0.4213	0.7806	-3.524	786	39.30%	416	41.60%	370	37.00%
Bw4	1.051e-03	5.256e-03	1.6877	0.1597	1.2363	2.3140	3.276	796	39.80%	377	37.70%	419	41.90%
Bw4 (HLA-B only)	6.713e-03	3.357e-02	1.4482	0.1366	1.1087	1.8947	2.711	682	34.10%	321	32.10%	361	36.10%
C1	9.438e-02	4.719e-01	1.3257	0.1685	0.9537	1.8482	1.673	828	41.40%	404	40.40%	424	42.40%
C2	1.000e+00	1.000e+00	1.0000	0.1313	0.7730	1.2936	0.000	634	31.70%	317	31.70%	317	31.70%

We find that HLA-Bw6 and HLA-Bw4 carrier status are associated with decreased and increased disease risk, respectively. Of course, this is interesting enough for us to invest in some KIR typing efforts.

KIR associations and HLA-KIR interactions

Do we see association on the level of KIR genes, and when considering defined HLA-KIR interactions?

Now that we have performed KIR genotyping, or e.g. inferred KIR types from available whole-genome sequencing data, we can import this information, and check the gene frequencies. In our example, we could successfully infer KIR gene presence status for 935 out of the 1,000 individuals in our data set.

dat_KIR <- readKirCalls(
  file = system.file("extdata", "MiDAS_tut_KIR.txt", package = "midasHLA")
)

KIR data as imported by MiDAS
ID	KIR3DL3	KIR2DS2	KIR2DL2	KIR2DL3	KIR2DP1	KIR2DL1	KIR3DP1	KIR2DL4	KIR3DL1	KIR3DS1	KIR2DL5	KIR2DS3	KIR2DS5	KIR2DS4	KIR2DS1	KIR3DL2
C001	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C002	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C003	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C004	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C005	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C006	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C007	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C008	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C009	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
C010	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C011	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C012	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C013	1	1	1	1	1	0	1	1	0	1	1	0	1	0	1	1
C014	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C015	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C016	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C017	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C018	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
C019	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C020	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C021	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C022	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C023	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C024	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C025	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C026	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C027	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
C028	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
C029	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
C030	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C031	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C032	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C033	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C034	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C035	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C036	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C037	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C038	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C039	1	1	1	1	1	1	1	1	1	0	1	1	0	1	1	1
C040	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C041	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C042	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C043	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C044	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C045	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C046	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C047	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C048	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
C049	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C050	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C051	1	1	1	1	1	0	1	1	0	1	1	0	1	0	1	1
C052	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C053	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C054	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C055	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
C056	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C057	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C058	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C059	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C060	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C061	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C062	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
C063	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
C064	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
C065	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C066	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C067	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C068	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C069	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C070	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C071	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C072	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
C073	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C074	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C075	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C076	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C077	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C078	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C079	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C080	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C081	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C082	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C083	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C084	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C085	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C086	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C087	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C088	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C089	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
C090	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C091	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C092	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C093	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C094	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C095	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C096	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C097	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C098	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C099	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C101	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
C102	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C103	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C104	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C105	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C106	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C107	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
C108	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C109	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C110	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C111	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C112	1	1	1	1	1	0	1	1	1	0	0	0	0	1	1	1
C113	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C114	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C115	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C116	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C117	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C118	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C119	1	1	1	0	1	1	1	1	1	1	1	0	1	1	1	1
C120	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C121	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C122	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C123	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C124	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C125	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C126	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C127	1	1	1	0	0	0	1	1	1	0	0	0	0	1	0	1
C128	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C129	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C130	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C131	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C132	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C133	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C134	1	1	1	0	0	0	1	1	0	1	1	0	1	0	1	1
C135	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C136	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C137	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1
C138	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C139	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C140	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C141	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C142	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C143	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C144	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C145	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
C146	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C147	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C148	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C149	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C150	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C151	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
C152	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C153	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C154	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C155	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C156	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C157	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C158	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C159	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C160	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C161	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C162	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C163	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C164	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C165	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C166	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C167	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C168	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
C169	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C170	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C171	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C172	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C173	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C174	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C175	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C176	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C177	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C178	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C179	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C180	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C181	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C182	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C183	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C184	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C185	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C186	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C187	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C188	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C189	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C190	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C191	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C192	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C193	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C194	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C195	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C196	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C197	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C198	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C199	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C200	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C201	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1
C202	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C203	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1
C204	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C205	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C206	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	1
C207	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C208	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C209	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C210	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C211	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1
C212	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C213	1	1	1	1	1	1	1	1	0	1	1	1	0	0	1	1
C214	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C215	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C216	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C217	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C218	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C219	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C220	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C221	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C222	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C223	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C224	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C225	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C226	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C227	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C228	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C229	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C230	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C231	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C232	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C233	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C234	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C235	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C236	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C237	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C238	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C239	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C240	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C241	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C242	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C243	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C244	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C245	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C246	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C247	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C248	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C249	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C250	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C251	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C252	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C253	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C254	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C255	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
C256	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C257	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C258	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C259	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C260	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1
C261	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C262	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C263	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C264	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C265	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C266	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C267	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C268	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C269	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C270	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C271	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C272	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C273	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C274	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C275	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C276	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C277	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C278	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C279	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C280	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C281	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C282	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C283	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C284	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
C285	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C286	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C287	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C288	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C289	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C290	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C291	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C292	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C293	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
C294	1	1	1	0	0	0	1	1	1	0	0	0	0	1	0	1
C295	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C296	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
C297	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C298	1	1	1	1	1	0	1	1	1	0	0	0	0	1	1	1
C299	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C300	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C301	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C302	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C303	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
C304	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C305	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C306	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1
C307	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C308	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C309	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C310	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C311	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C312	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C313	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C314	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C315	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C316	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C317	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C318	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C319	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C320	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C321	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C322	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C323	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C324	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C325	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C326	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C327	1	0	0	1	1	1	1	1	0	1	1	1	0	0	1	1
C328	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C329	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C330	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C331	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
C332	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C333	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C334	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C335	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C336	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C337	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C338	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C339	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C340	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C341	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C342	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C343	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C344	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C345	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C346	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C347	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C348	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C349	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C350	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C351	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C352	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C353	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C354	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C355	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C356	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C357	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C358	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C359	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C360	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C361	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C362	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C363	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C364	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C365	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C366	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C367	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1
C368	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
C369	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C370	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C371	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C372	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C373	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C374	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C375	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C376	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C377	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C378	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C379	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C380	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C381	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C382	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C383	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C384	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C385	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C386	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C387	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C388	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C389	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C390	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C391	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C392	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C393	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C394	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C395	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C396	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C397	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C398	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
C399	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C400	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C401	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C402	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C403	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C404	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C405	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C406	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C407	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C408	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C409	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C410	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C411	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1
C412	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C413	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C414	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C415	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C416	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C417	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C418	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C419	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C420	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C421	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C422	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C423	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C424	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C425	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C426	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C427	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C428	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C429	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C430	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C431	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C432	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C433	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C434	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
C435	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C436	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C437	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C438	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C439	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C440	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C441	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C442	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C443	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C444	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C445	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C446	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C447	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C448	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C449	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C450	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
C451	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C452	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C453	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C454	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1
C455	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C456	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C457	1	1	1	0	1	1	1	1	1	1	1	1	0	1	0	1
C458	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C459	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C460	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
C461	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C462	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C463	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
C464	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C465	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C466	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C467	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C468	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C469	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C470	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C471	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C472	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C473	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C474	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C475	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C476	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C477	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C478	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C479	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C480	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C481	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C482	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
C483	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C484	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C485	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C486	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C487	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
C488	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C489	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C490	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C491	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
C492	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C493	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C494	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
C495	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C496	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
C497	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
C498	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C499	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
C500	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P001	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P002	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P003	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P004	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P005	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P006	1	1	1	0	0	0	1	1	1	1	1	0	1	1	1	1
P007	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P008	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P009	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P010	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P011	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P012	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P013	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P014	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P015	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P016	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P017	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P018	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P019	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P020	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P021	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P022	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P023	1	1	1	1	1	1	1	1	1	0	1	1	0	1	1	1
P024	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P025	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P026	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P027	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P028	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P029	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P030	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P031	1	1	1	0	0	0	1	1	1	0	1	0	1	1	1	1
P032	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P033	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P034	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P035	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P036	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
P037	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P038	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P039	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P040	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P041	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P042	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P043	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P044	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P045	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P046	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P047	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P048	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P049	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P050	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P051	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P052	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P053	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P054	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P055	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
P056	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P057	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P058	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P059	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P060	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P061	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P062	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P063	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P064	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P065	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P066	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P067	1	1	1	0	0	0	1	1	1	0	0	0	0	1	0	1
P068	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P069	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P070	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P071	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
P072	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P073	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P074	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P075	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P076	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P077	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P078	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
P079	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P080	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P081	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P082	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P083	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P084	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P085	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P086	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P087	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P088	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P089	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P090	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P091	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P092	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P093	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P094	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P095	1	0	0	1	1	1	1	1	1	1	1	1	0	1	0	1
P096	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P097	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P098	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P099	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P100	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P101	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P102	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P103	1	1	1	1	1	0	1	1	0	1	1	0	1	0	1	1
P104	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P105	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P106	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P107	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P108	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P109	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P110	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P111	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P112	1	1	1	1	1	0	1	1	1	0	0	0	0	1	1	1
P113	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P114	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1
P115	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P116	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P117	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P118	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P119	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P120	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P121	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P122	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P123	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P124	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P125	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P126	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P127	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P128	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P129	1	1	1	0	0	0	1	1	1	1	1	0	1	1	1	1
P130	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P131	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P132	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
P133	1	0	0	1	1	1	1	1	0	1	1	0	1	0	1	1
P134	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P135	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P136	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P137	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P138	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
P139	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P140	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P141	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P142	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P143	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P144	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P145	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P146	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P147	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P148	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P149	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P150	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P151	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P152	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P153	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P154	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P155	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P156	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P157	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P158	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P159	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P160	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P161	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P162	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P163	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P164	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P165	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P166	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P167	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P168	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P169	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P170	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P171	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P172	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P173	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P174	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P175	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P176	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P177	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P178	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P179	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P180	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P181	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P182	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P183	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P184	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P185	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P186	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P187	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P188	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P189	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
P190	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P191	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P192	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P193	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
P194	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P195	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P196	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P197	1	1	1	0	0	0	1	1	1	1	1	0	1	1	1	1
P198	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P199	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P200	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P201	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P202	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P203	1	1	1	0	1	1	1	1	1	1	1	1	0	1	0	1
P204	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P205	1	1	1	0	1	1	1	1	1	1	1	1	0	1	0	1
P206	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P207	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P208	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P209	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P210	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P211	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P212	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P213	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P214	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P215	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P216	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P217	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P218	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P219	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P220	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P221	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P222	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P223	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P224	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	1
P225	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P226	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P227	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1
P228	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P229	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P230	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P231	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
P232	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P233	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P234	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P235	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P236	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P237	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P238	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P239	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P240	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P241	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P242	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P243	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P244	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P245	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P246	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P247	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P248	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P249	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P250	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P251	1	0	0	1	1	1	1	1	1	1	1	1	0	1	0	1
P252	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P253	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P254	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P255	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P256	1	1	1	0	1	1	1	1	1	1	1	1	0	1	0	1
P257	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P258	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P259	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P260	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P261	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P262	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P263	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P264	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
P265	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P266	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P267	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P268	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P269	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P270	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P271	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P272	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P273	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P274	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P275	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P276	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P277	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P278	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P279	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	1
P280	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P281	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P282	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P283	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P284	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P285	1	1	1	1	1	0	1	1	1	0	0	0	0	1	1	1
P286	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P287	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P288	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P289	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P290	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P291	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P292	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P293	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P294	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
P295	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P296	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P297	1	1	1	0	1	1	1	1	1	1	1	0	1	1	1	1
P298	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P299	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P300	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P301	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P302	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P303	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P304	1	0	0	1	1	1	1	1	1	1	1	1	0	1	0	1
P305	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P306	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P307	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P308	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P309	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P310	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P311	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P312	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P313	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P314	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P315	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P316	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P317	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P318	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P319	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P320	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P321	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
P322	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P323	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P324	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P325	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P326	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P327	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P328	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P329	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
P330	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P331	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P332	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P333	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P334	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P335	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P336	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P337	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P338	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
P339	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
P340	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P341	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P342	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P343	1	1	1	0	0	0	1	1	1	1	1	0	1	1	1	1
P344	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P345	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P346	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P347	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P348	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P349	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P350	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P351	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P352	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P353	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P354	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P355	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P356	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P357	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P358	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P359	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P360	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P361	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P362	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P363	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P364	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P365	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P366	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P367	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P368	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P369	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P370	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P371	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P372	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P373	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P374	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P375	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P376	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P377	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P378	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P379	1	1	1	0	1	1	1	1	0	1	1	1	1	0	1	1
P380	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P381	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P382	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P383	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P384	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P385	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P386	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P387	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P388	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P389	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P390	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1
P391	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P392	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P393	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P394	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P395	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P396	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P397	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P398	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
P399	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P400	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P401	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P402	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P403	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P404	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P405	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P406	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P407	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P408	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P409	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P410	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P411	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P412	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P413	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
P414	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P415	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P416	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P417	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P418	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	1
P419	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P420	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P421	1	0	0	1	1	1	1	1	0	1	1	1	1	0	1	1
P422	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P423	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P424	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P425	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1
P426	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1
P427	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P428	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P429	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P430	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P431	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P432	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P433	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P434	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P435	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	1
P436	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P437	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P438	1	0	0	1	1	1	1	1	1	0	0	0	0	1	1	1
P439	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P440	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P441	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P442	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1
P443	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P444	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P445	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P446	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P447	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P448	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P449	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P450	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P451	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P452	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	1
P453	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P454	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P455	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P456	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P457	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	1
P458	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P459	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P460	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P461	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P462	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1
P463	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P464	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P465	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P466	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P467	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1
P468	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P469	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P470	1	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1
P471	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P472	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P473	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P474	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P475	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1
P476	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P477	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P478	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P479	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P480	1	1	1	0	0	0	1	1	1	0	0	0	0	1	0	1
P481	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P482	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
P483	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P484	1	1	1	0	1	1	1	1	1	0	1	1	0	1	0	1
P485	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1
P486	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P487	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P488	1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
P489	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P490	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P491	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P492	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P493	1	1	1	1	1	1	1	1	0	1	1	1	1	0	1	1
P494	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0	1
P495	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P496	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P497	1	0	0	1	1	1	1	1	1	0	0	0	0	1	0	1
P498	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
P499	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1
P500	1	1	1	1	1	1	1	1	1	0	1	0	1	1	1	1

Next, we want to check our KIR genes frequencies.

kir_freq <- getKIRFrequencies(dat_KIR)

KIR data as imported by MiDAS
gene	Counts	Freq
KIR3DL3	935	1.0000000
KIR2DS2	455	0.4866310
KIR2DL2	449	0.4802139
KIR2DL3	849	0.9080214
KIR2DP1	925	0.9893048
KIR2DL1	918	0.9818182
KIR3DP1	935	1.0000000
KIR2DL4	935	1.0000000
KIR3DL1	853	0.9122995
KIR3DS1	365	0.3903743
KIR2DL5	485	0.5187166
KIR2DS3	294	0.3144385
KIR2DS5	288	0.3080214
KIR2DS4	853	0.9122995
KIR2DS1	371	0.3967914
KIR3DL2	935	1.0000000

Next, we rerun our prepareMiDAS function, this time including the KIR data. We prepare the data to test for both KIR gene associations, as well as HLA-KIR interactions. But first, let’s runMiDAS on the level of KIR genes.

HLAKIR <- prepareMiDAS(
  hla_calls = dat_HLA,
  kir_calls = dat_KIR,
  colData = dat_pheno,
  experiment = c("hla_NK_ligands","kir_genes", "hla_kir_interactions")
)
KIR_model <- glm(disease ~ term, data = HLAKIR, family = binomial())
KIR_results <- runMiDAS(
  KIR_model,
  experiment = "kir_genes",
  lower_frequency_cutoff = 0.02,
  upper_frequency_cutoff = 0.98,
  exponentiate = TRUE
)

kableResults(KIR_results)

MiDAS analysis results
kir.gene	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic	Ntotal	Ntotal [%]	N(disease=0)	N(disease=0) [%]	N(disease=1)	N(disease=1) [%]
KIR3DL1	3.700e-06	3.650e-05	3.4291	0.2661	2.0717	5.9126	4.6303	853	91.23%	405	86.72%	448	95.73%
KIR2DS4	3.700e-06	3.650e-05	3.4291	0.2661	2.0717	5.9126	4.6303	853	91.23%	405	86.72%	448	95.73%
KIR2DL3	4.414e-02	4.414e-01	0.6282	0.2310	0.3965	0.9835	-2.0128	849	90.80%	433	92.72%	416	88.89%
KIR2DS2	8.295e-02	8.295e-01	1.2552	0.1311	0.9710	1.6235	1.7338	455	48.66%	214	45.82%	241	51.50%
KIR2DL2	1.087e-01	1.000e+00	1.2341	0.1311	0.9546	1.5962	1.6043	449	48.02%	212	45.40%	237	50.64%
KIR2DS5	1.143e-01	1.000e+00	0.7992	0.1420	0.6046	1.0552	-1.5790	288	30.80%	155	33.19%	133	28.42%
KIR2DS3	1.658e-01	1.000e+00	1.2160	0.1411	0.9224	1.6044	1.3858	294	31.44%	137	29.34%	157	33.55%
KIR2DS1	1.949e-01	1.000e+00	0.8407	0.1338	0.6465	1.0927	-1.2962	371	39.68%	195	41.76%	176	37.61%
KIR3DS1	7.178e-01	1.000e+00	0.9527	0.1341	0.7324	1.2391	-0.3614	365	39.04%	185	39.61%	180	38.46%
KIR2DL5	9.208e-01	1.000e+00	0.9871	0.1309	0.7636	1.2759	-0.0994	485	51.87%	243	52.03%	242	51.71%

We found an association of KIR3DL1 gene presence with disease status. Since HLA alleles encoding Bw4 epitopes are ligands for KIR3DL1, we can now ask the question whether patients coding for both receptor and ligand are at increased risk for our disease phenotype.

HLA-KIR interactions

Do known biological interactions between KIR receptors and their HLA ligands show significant assocation?

If both HLA alleles and KIR gene presence / absence information is available, MiDAS can encode experimentally validated receptor-ligand interactions, defined according to Pende et al. Please note that there is extensive allelic variation in KIR genes, as well as evidence that their interaction with HLA does not only depend on the presence of the KIR gene, but also allele status. Currently, MiDAS does not consider allelic variation in KIR, but a custom dictionary can be provided by the user (see documentation).

First let’s explore HLA-KIR interactions frequencies

hlakir_freq <- getFrequencies(HLAKIR, experiment = "hla_kir_interactions")

HLA-KIR interaction frequencies
term	Counts	Freq
C2_KIR2DL1	580	0.6079665
C1_KIR2DL2	373	0.3922187
B*46:01_KIR2DL2	4	0.0040000
B*73:01_KIR2DL2	0	0.0000000
C2_KIR2DL2	283	0.2966457
C1_KIR2DL3	704	0.7402734
B*46:01_KIR2DL3	10	0.0100000
B*73:01_KIR2DL3	2	0.0020000
C2_KIR2DL3	537	0.5628931
A*23_KIR3DL1	36	0.0363269
A*24_KIR3DL1	196	0.1983806
A*32_KIR3DL1	54	0.0541625
A*03_KIR3DL2	189	0.1926606
A*11_KIR3DL2	116	0.1161161
Bw4_KIR3DL1	680	0.7188161
Bw4(HLA-B_only)_KIR3DL1	585	0.6138510
C2_KIR2DS1	237	0.2484277
C1_KIR2DS2	379	0.3985279
A*11:01_KIR2DS2	48	0.0480480
C*02:02_KIR2DS4	76	0.0763052
C*04:01_KIR2DS4	180	0.1832994
C*05:01_KIR2DS4	122	0.1238579
C*01:02_KIR2DS4	85	0.0855131
C*14:02_KIR2DS4	28	0.0280280
C*16:01_KIR2DS4	42	0.0421264
A*11_KIR2DS4	102	0.1021021
C2_KIR2DS5	178	0.1865828
B*51_KIR3DS1	53	0.0532663
A03_A11_KIR2DS2	126	0.1285714

HLAKIR_model <- glm(disease ~ term, data = HLAKIR, family = binomial())
HLA_KIR_results <- runMiDAS(
  HLAKIR_model,
  experiment = "hla_kir_interactions",
  lower_frequency_cutoff = 0.02,
  upper_frequency_cutoff = 0.98,
  exponentiate = TRUE
)

kableResults(HLA_KIR_results)

MiDAS analysis results
hla.kir.interaction	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic	Ntotal	Ntotal [%]	N(disease=0)	N(disease=0) [%]	N(disease=1)	N(disease=1) [%]
Bw4_KIR3DL1	6.000e-07	1.620e-05	2.0952	0.1487	1.5686	2.810	4.976e+00	680	71.88%	306	64.56%	374	79.24%
Bw4(HLA-B_only)_KIR3DL1	6.240e-05	1.559e-03	1.7131	0.1345	1.3173	2.232	4.004e+00	585	61.39%	262	55.04%	323	67.71%
C1_KIR2DS2	1.895e-03	4.737e-02	1.5127	0.1332	1.1656	1.966	3.106e+00	379	39.85%	167	34.94%	212	44.82%
C1_KIR2DL2	2.877e-03	7.193e-02	1.4889	0.1336	1.1466	1.936	2.981e+00	373	39.22%	165	34.52%	208	43.97%
B*51_KIR3DS1	1.801e-02	4.502e-01	2.0202	0.2973	1.1427	3.692	2.365e+00	53	5.33%	18	3.61%	35	7.04%
A*24_KIR3DL1	1.111e-01	1.000e+00	1.2909	0.1602	0.9437	1.770	1.593e+00	196	19.84%	88	17.81%	108	21.86%
C2_KIR2DS5	1.352e-01	1.000e+00	0.7793	0.1669	0.5610	1.080	-1.494e+00	178	18.66%	98	20.55%	80	16.77%
C2_KIR2DS1	1.549e-01	1.000e+00	0.8076	0.1502	0.6011	1.084	-1.422e+00	237	24.84%	128	26.83%	109	22.85%
A*11_KIR3DL2	1.712e-01	1.000e+00	0.7618	0.1988	0.5143	1.123	-1.368e+00	116	11.61%	65	13.00%	51	10.22%
A*11:01_KIR2DS2	2.415e-01	1.000e+00	0.7038	0.2999	0.3862	1.260	-1.171e+00	48	4.80%	28	5.60%	20	4.01%
A*03_KIR3DL2	3.149e-01	1.000e+00	1.1771	0.1622	0.8568	1.619	1.005e+00	189	19.27%	88	18.00%	101	20.53%
C*02:02_KIR2DS4	3.498e-01	1.000e+00	1.2515	0.2399	0.7832	2.013	9.351e-01	76	7.63%	34	6.84%	42	8.42%
A03_A11_KIR2DS2	3.529e-01	1.000e+00	1.1946	0.1914	0.8215	1.742	9.290e-01	126	12.86%	58	11.86%	68	13.85%
A*11_KIR2DS4	4.096e-01	1.000e+00	0.8413	0.2096	0.5563	1.268	-8.245e-01	102	10.21%	55	11.00%	47	9.42%
C*05:01_KIR2DS4	4.465e-01	1.000e+00	1.1590	0.1938	0.7931	1.698	7.613e-01	122	12.39%	57	11.59%	65	13.18%
C2_KIR2DL3	4.728e-01	1.000e+00	0.9105	0.1306	0.7048	1.176	-7.179e-01	537	56.29%	274	57.44%	263	55.14%
A*32_KIR3DL1	5.709e-01	1.000e+00	0.8530	0.2806	0.4890	1.478	-5.668e-01	54	5.42%	29	5.82%	25	5.01%
C*04:01_KIR2DS4	5.996e-01	1.000e+00	1.0905	0.1650	0.7891	1.508	5.250e-01	180	18.33%	87	17.68%	93	18.98%
C2_KIR2DL2	6.198e-01	1.000e+00	1.0729	0.1418	0.8126	1.417	4.961e-01	283	29.66%	138	28.93%	145	30.40%
C*01:02_KIR2DS4	7.047e-01	1.000e+00	1.0898	0.2270	0.6982	1.705	3.790e-01	85	8.55%	41	8.22%	44	8.89%
C*14:02_KIR2DS4	7.057e-01	1.000e+00	0.8649	0.3843	0.4012	1.840	-3.777e-01	28	2.80%	15	3.00%	13	2.61%
A*23_KIR3DL1	7.483e-01	1.000e+00	1.1153	0.3401	0.5717	2.193	3.209e-01	36	3.63%	17	3.44%	19	3.82%
C*16:01_KIR2DS4	7.576e-01	1.000e+00	1.1023	0.3157	0.5928	2.061	3.086e-01	42	4.21%	20	4.02%	22	4.41%
C2_KIR2DL1	8.945e-01	1.000e+00	0.9826	0.1326	0.7575	1.274	-1.326e-01	580	60.80%	291	61.01%	289	60.59%
C1_KIR2DL3	9.824e-01	1.000e+00	0.9967	0.1479	0.7458	1.332	-2.209e-02	704	74.03%	354	74.06%	350	74.00%

Patients who are carriers of both HLA-Bw4 and KIR3DL1 are at a significantly increased risk for having the disease, which is consistent with the associations we saw on KIR ligand and KIR gene level.

Finally, let’s now take a look at some additional ways to analyze immunogenetics data within MiDAS, which are probably not relevant for most users.

HLA heterozygosity and evolutionary divergence

Instead of focusing on single HLA alleles or their interactions, you might be interested in HLA diversity. For example, it has been shown that MHC heterozygosity confers a selective advantage against infections with multiple Salmonella strains. But given the degree of variability in HLA genes, heterozygosity is a rather crude measure. On the amino acid sequence level, it is possible to perform more refined analyses, by calculating the level of evolutionary divergence between pairs of HLA alleles of different genes. Pierini and Lenz have found Grantham’s distance score to be a good proxy for HLA functional divergence. It has been demonstrated that HLA-C allelic divergence was associated with HIV viral load. In addition, there is evidence for a role of HLA class I divergence in the efficacy of cancer immunotherapy.

MiDAS provides experiment types hla_het to analyze heterozygosity of class I and II genes, as well as hla_divergence to analyze classical class I genes using Grantham’s distance:

HLA_het <- prepareMiDAS(
  hla_calls = dat_HLA, 
  colData = dat_pheno, 
  experiment = c("hla_het","hla_divergence")
)

HLA_het_model <- glm(outcome ~ term, data=HLA_het, family=binomial())

HLA_het_results <- runMiDAS(HLA_het_model, 
  experiment = "hla_het", 
  exponentiate = TRUE
)

kableResults(HLA_het_results)

MiDAS analysis results
term	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic	Ntotal	Ntotal [%]	N(outcome=0)	N(outcome=0) [%]	N(outcome=1)	N(outcome=1) [%]
C_het	5.405e-02	0.4865	0.5436	0.3165	0.2878	1.003	-1.9264	900	90.00%	246	93.18%	208	88.14%
DPA1_het	2.155e-01	1.0000	1.2585	0.1856	0.8748	1.812	1.2386	350	35.00%	91	34.47%	94	39.83%
DQA1_het	4.297e-01	1.0000	1.2477	0.2802	0.7230	2.179	0.7897	868	86.80%	230	87.12%	211	89.41%
B_het	4.672e-01	1.0000	1.2998	0.3606	0.6455	2.686	0.7271	934	93.40%	244	92.42%	222	94.07%
A_het	5.067e-01	1.0000	1.2057	0.2817	0.6962	2.111	0.6640	892	89.20%	231	87.50%	211	89.41%
DPB1_het	5.279e-01	1.0000	1.1468	0.2170	0.7504	1.759	0.6312	779	77.90%	203	76.89%	187	79.24%
DRB1_het	6.693e-01	1.0000	0.8824	0.2931	0.4954	1.572	-0.4271	890	89.00%	238	90.15%	210	88.98%
DRA_het	6.997e-01	1.0000	0.9329	0.1800	0.6553	1.327	-0.3857	435	43.50%	122	46.21%	105	44.49%
DQB1_het	7.659e-01	1.0000	0.9184	0.2860	0.5234	1.614	-0.2977	882	88.20%	236	89.39%	209	88.56%

HLA_div_results <- runMiDAS(HLA_het_model, 
  experiment = "hla_divergence", 
  exponentiate = TRUE
)

kableResults(HLA_div_results, scroll_box_height = "250px")

MiDAS analysis results
term	p.value	p.adjusted	estimate	std.error	conf.low	conf.high	statistic
ABC_avg	0.3713	1	1.043	4.668e-02	0.9517	1.143	0.8940
A	0.4519	1	1.018	2.416e-02	0.9713	1.068	0.7522
B	0.6163	1	1.013	2.577e-02	0.9632	1.066	0.5012
C	0.7747	1	1.012	4.052e-02	0.9345	1.096	0.2863

In our example dataset, our results show that there is no significant association of HLA heterozygosity or evolutionary divergence with our disease phenotype of interest.

We hope that this tutorial is useful to get you started working with MiDAS. Please don’t hesitate to contact us if you have questions or suggestions for improvement.

Maciej Migdał & Christian Hammer

2024-01-31

Introduction

Data import and sanity check

HLA association analysis

Are classical HLA alleles associated with disease status?

HLA association fine-mapping on amino acid level

KIR associations and HLA-KIR interactions

Do we see association on the level of KIR genes, and when considering defined HLA-KIR interactions?

HLA-KIR interactions

Do known biological interactions between KIR receptors and their HLA ligands show significant assocation?

HLA heterozygosity and evolutionary divergence

MiDAS tutorial

Maciej Migdał & Christian Hammer

2024-01-31

Introduction

Data import and sanity check

HLA association analysis

Are classical HLA alleles associated with disease status?

HLA association fine-mapping on amino acid level

Can we find evidence for a role of HLA variation related to NK cell interactions?

KIR associations and HLA-KIR interactions

Do we see association on the level of KIR genes, and when considering defined HLA-KIR interactions?

HLA-KIR interactions

Do known biological interactions between KIR receptors and their HLA ligands show significant assocation?

HLA heterozygosity and evolutionary divergence