Parkinson's Disease and Low Frequency Alleles Found Together Throughout LRRK2

Summary

Mutations within LRRK2, most notably p.G2019S, cause Parkinson's disease (PD) in rare monogenic families, and sporadic occurrences in diverse populations. We investigated variation throughout LRRK2 (84 SNPs; genotype or diplotype found for 49 LD blocks) for 275 cases (European ancestry, onset at age 60 or older) and 275 neurologically healthy control subjects (NINDS Neurogenetics Repository). Three grade-of-membership groups, i.e. genetic risk sets, were identified that exactly matched many subjects (cases: 46, 4, 137; controls: 0, 178, 0), and distinguished 94% of the subjects (i.e. >50% likeness to one set). Set I, affected, carried certain low frequency alleles located in multiple functional domains. Set II was unaffected. Set III, also affected, resembled set II except for slightly elevated frequencies of minor alleles not defining set I. We conclude that certain low frequency alleles distributed throughout LRRK2 are a genetic background to a third of cases, defining a distinct subset.

Introduction

Parkinson's disease (PD, OMIM #168600) is a chronic neurodegenerative disease with a cumulative prevalence of greater than one per thousand people (Kuopio et al., 1999). It is well characterized clinically (resting tremor, bradykinesia, postural instability, rigidity) and pathologically (loss of dopaminergic neurons in the pars compacta of the substantia nigra). Genetically, rare monogenic families have identified five causative genes, the most common (∼7%) (Healy et al., 2008) being leucine-rich repeat kinase 2 (LRRK2; OMIM *609007) located on chromosome 12q12. Candidate gene studies have to date been less successful in demonstrating the genetic background to sporadic PD.

Our interest focused on the LRRK2 gene as both familial and sporadic cases in diverse populations are known to carry the p.G2019S mutation, e.g. ∼1% of sporadic cases with European ancestry (Healy et al., 2008). The gene is large, spanning 1.4 Mb and consisting of 51 exons and multiple functional domains (Leucine-rich repeats (LRR), Roc, COR, RAS, Kinase, WD40 motif). The encoded protein dardarin is thought to play a role in intracellular signaling (Marin et al., 2008). It is expressed in multiple brain regions, particularly in the substantia nigra, consistent with direct involvement in dopaminergic cell death.

Nonetheless, a genome-wide search (Fung et al., 2006) did not identify any SNP within (51 SNPs), or close to, LRRK2 as relevant to PD in the NINDS Neurogenetic Repository sample of 275 cases of European ancestry with onset at age 60 or older, and 275 neurologically healthy control subjects applying a rather stringent criterion (uncorrected P-value < 0.0001). These negative findings were not likely related to differing population structure for case and control subjects as comparison of the two groups demonstrated no appreciable differences (STRUCTURE; http://pritch.bsd.uchicago.edu/structure.html) (Fung et al., 2006; Falush et al., 2003).

Paisan-Ruiz et al. then sequenced all 51 exons, and at least 50 bp of flanking intronic sequence, for the sample (Paisan-Ruiz et al., 2008). Coding variants were found in twelve patients (4 cases carried p.G2019S) as well as in seven control subjects (no p.G2019S mutations). A total of 135 variants were identified in the sample including SNPs from the genome-wide study and those identified by sequencing, many were unique to one or several subjects. Considering the 84 SNPs having minor allele frequency of 5 or more, six SNPs scattered throughout the gene were associated with PD by χ² testing using a weaker criterion (p < 0.05), indicated by an asterisk throughout this paper: rs1157655* (A allele, intron 2), rs1907632* (T, intron 11), rs11564205* (G, intron 34), rs11564203* (A, intron 39), rs11829088* (G, intron 39), and rs11564173* (A, intron 46).

Our goal was to take this information a step further by first identifying linkage disequilibrium blocks (LD) to simplify the data and render it more meaningful, and then, by identifying genetic risk sets for PD, each defined by genotype/diplotype frequencies for the LD blocks. This was accomplished by grade-of-membership analysis (GoM) (Clive et al., 1983; Woodbury & Clive, 1974; Woodbury et al., 1978; Woodbury et al., 1994).

GoM is a form of latent classification analysis that incorporates large amounts of information to identify major patterns within the data. It allows individuals to resemble one of the identified patterns, or GoM groups (here genetic risk sets) or more often, to partly resemble two or more groups. The degree of likeness of individuals to each GoM group is given by membership scores in the groups, like weights, which range from zero (no likeness of the subject to the GoM group) to one (an exact match), summing to one for each subject. This fuzziness with respect to individuals minimizes the number of groups needed to represent the sample. Unlike other forms of latent classification, it operates efficiently in L1 space (linear differences), rather than L2 space (sum of squares differences), providing 5-fold better ability to identify patterns according to the signal detection literature, i.e. high power compared to more usual genetic epidemiologic approaches (Corder et al., 2001). Importantly, the GoM groups (represented here by frequencies for genotypes/diplotypes), and the likeness of individuals to the groups (represented by membership scores), are jointly estimated using maximum likelihood (see Methods section), closely defining the space concerning LRRK2, avoiding multiple comparisons. The best number of groups is decided according to an information criterion or empirically, as in this instance when three groups were sufficient to distinguish most (94%) case and control subjects, also identifying a distinct subset, about a third, of cases.

Methods

Study Subjects

The 275 sporadic PD cases age 60 or older, and 275 neurologically healthy control subjects having a similar age-sex distribution, are members of a NINDS cohort hosted by the Coriell Institute (http://ccr.coriell.org/Sections/Collections/NINDS/). Initially, 51 SNPs tagging each intron were investigated for the subjects in a genome-wide association analysis using HumanHap317 SNP arrays (Fung et al., 2006). Subsequently, all 51 exons were sequenced for the subjects including at least 50 bp of flanking intronic sequence to identify additional mutations and SNPs. This identified 12 cases who carried mutations (p.H275H, p.M712V, p.A1430A, p.R1728L, p.R1728H, p.G2019S (n = 4), p.T2141M, p.R2143H, p.L2466H) as well as 7 controls with LRRK2 variants (p.C228S, p.Y707Y, p.A716V, p.K871E, p.L1870F, p.E2395K, p.G2432G) (Paisan-Ruiz et al., 2008). Information on age and sex was available for cases.

Coding of the Data for Entry into GoM Analysis

GoM evaluates categorical data, usually 2 to 6 possible outcomes for each variable. PD status: 0 = control, 1 = onset < age 65, 2 = onset age 65 to 74, 3 = onset age 75 to 88. Sex: 0 = control, 1 = male (case), 2 = female (case). Mutation status: 0 = no mutation, 1 = mutation (case), 2 = mutation (control). Number of very low frequency alleles found at 34 loci (minor allele count ≤ 5 at each locus): 0, 1, or 2 (at 2 to 5 of these loci).

A total of 84 SNPs had minor allele counts of 5 or more. These loci were in strong LD (Paisan-Ruiz et al., 2008). Thus, we created relatively independent variables and facilitated the identification of relevant low frequency alleles by the identification of LD blocks. The LD blocks were identified using the Carlson method (HelixTree Software) specifying the minor allele frequency threshold as 0.01 (not the default value of 0.10) and the R^2 LD threshold as 0.80.

There were 49 LD blocks, labeled from B0 to B48 (Table 1). B0 had minor allele frequency 0.009, i.e. <0.01. B1 to B12 consisted of multiple loci usually dispersed over a large section of the gene, and overlapping each other. Three of these LD blocks contained the six SNPs previously associated with PD in the sample: B15*, rs1157655* (intron 2); B4*, rs1907632* (intron 11) + rs11564205* (intron 34) + rs11564203* (intron 39) + rs11829088* (intron 39); and, B43*, rs11564173* (intron 46). Thus B4* contained four of the six associated SNPs distributed across functional domains including LRR, Roc, COR and Kinase domains.

Table 1. Table 1 LD blocks located within the LRRK2 gene.

Block Number	SNP Name	Minor Allele Freq.	Location
49 LD blocks (B0 to B48) were identified from 84 SNPs located within the LRRK2 gene having minor allele count of 5 or more (Carlson method, R^2 minimum LD threshold 0.80, minimum allele frequency threshold 0.01, HelixTree Software). The SNPs are listed in map order from 5′ to 3′ except for non-contiguous SNPs belonging to B1 to B12 consisting of multiple SNPs, highlighted in gray. SNPs previously associated with PD [B15* (rs1157655), B4* (rs1907632, rs11564205, rs11564203, rs11829088), B43* (rs11564173); p < 0.05 in χ^2 testing] (Paisan-Ruiz et al., 2008) and those identified here according to a less stringent criterion [B28* (rs10784498), B31* (rs33958906); p < 0.10 in an additive co-dominant model] are in bold and tagged with an asterisk.
13	rs1388587	0.294	5′ near gene
7	rs2201144	0.081	5′ near gene
7	rs2131088	0.068	Intron 4
7	rs1388596	0.067	Intron 7
14	rs12230685	0.156	Intron 2
15*	rs11175655*	0.144	Intron 2
16	rs10878245	0.399	Exon 5
17	rs10878246	0.163	Intron 5
18	rs10878247	0.291	Intron 5
8	rs10878249	0.399	Intron 5
8	c.839–160C>T	0.354	Intron 7
8	rs7955902	0.361	Intron 9
19	rs11564187	0.027	Intron 5
20	rs7134379	0.273	Intron 8
21	rs1491938	0.433	Intron 10
22	rs7969677	0.194	Intron 11
4*	rs1907632*	0.166	Intron 11
4*	rs11564205*	0.164	Intron 34
4*	rs11564203*	0.164	Intron 39
4*	rs11829088*	0.164	Intron 39
23	rs2723264	0.209	Intron 12
3	rs10784461	0.467	Intron 13
3	rs10784462	0.463	Intron 14
3	rs11175847	0.460	Intron 18
3	rs36220740	0.468	Intron 19
3	rs12820920	0.461	Intron 21
2	rs7308720	0.066	Exon 14
2	rs7133914	0.068	Exon 30
2	rs11175964	0.066	Exon 30
2	rs11176022	0.063	Intron 37
2	rs10878386	0.069	Intron 39
2	rs11176195	0.069	Intron 47
2	rs12426498	0.066	Intron 50
9	rs10784470	0.285	Intron 15
9	rs11564148	0.283	Exon 34
9	rs4768230	0.282	Intron 35
10	rs11564129	0.093	Intron 16
10	rs11564149	0.095	Intron 28
24	rs10506151	0.138	Intron 16
25	rs10878307	0.068	Exon 18
26	c.2680 + 11insA	0.075	Intron 20
27	rs7966550	0.138	Exon 22
28*	rs10784498*	0.357	Intron 26
29	c.4309 + 12delT	0.025	Intron 30
1	rs10650388	0.368	Intron 30
1	rs7302503	0.362	Intron 31
1	rs1427267	0.362	Intron 32
1	rs1427263	0.364	Exon 34
1	rs7137665	0.362	Intron 36
1	rs4768236	0.366	Intron 47
1	rs3761863	0.368	Exon 49
1	rs12426362	0.370	Intron 49
30	rs11175985	0.134	Intron 31
31*	rs33958906*	0.026	Exon 32
5	rs1896252	0.461	Intron 33
5	rs11176013	0.455	Exon 34
5	rs10878371	0.459	Exon 37
5	rs7298930	0.457	Intron 39
32	rs35303786	0.014	Exon 34
33	rs17444054	0.028	Intron 37
34	rs10878372	0.218	Intron 37
0	c.5656 + 7C>T	0.009	Intron 38
35	c.5656 + 35G>A	0.027	Intron 38
36	rs12370996	0.040	Intron 39
11	rs11176052	0.262	Intron 39
11	rs11176053	0.262	Intron 39
37	rs7307562	0.378	Intron 39
38	rs2404835	0.329	Intron 40
6	rs1427271	0.165	Intron 40
6	rs7307310	0.138	Intron 43
6	rs4768238	0.137	Intron 50
6	rs1465527	0.137	3′ near gene
39	rs10735934	0.494	Intron 40
40	rs10506155	0.328	Intron 41
41	rs33995883	0.021	Exon 42
12	rs10878405	0.312	Exon 43
12	rs10467147	0.317	3′ near gene
42	rs11176143	0.113	Intron 43
43*	rs11564173*	0.114	Intron 46
44	c.7029–8(8>9T)	0.369	Intron 47
45	rs33962975	0.130	Exon 48
46	rs11835105	0.184	Intron 48
47	rs3789329	0.029	Intron 49
48	rs1820545	0.438	3′ near gene

Next, diplotype for individuals was inferred for LD blocks B1 to B12 using the E-M algorithm. Each value had >99% probability, with 9 exceptions (>92% probability). Diplotype was not inferred when there was missing genotype information as this would misidentify infrequent alleles, if present. The data was nearly complete (648 missing values among the 49 blocks). Missing values did not substantially represent untyped very low frequency alleles: the majority of missing data was limited to 5 case and 8 control subjects. Diplotype for B1 to B12, and genotype for B0, B13 to B48 were coded numerically, grouping low frequency values <5% together (Table 2).

Table 2.  Haplotype frequencies for B1 to B12 consisting of multiple SNPs.

Block	Haplotype	Frequency
Haplotype frequencies for LD blocks B1 to B12 consisting of multiple SNPs.
7	TAT	0.910
	CTC	0.060
	CAT	0.010
	CTT	0.002
	TAC	0.002
	TTC	0.001
8	TCC	0.590
	CTA	0.350
	CCC	0.040
	CCA	0.010
	TTA	0.003
	CTC	0.003
	TTC	0.002
4*	CAGT	0.830
	TGAG	0.160
	TAAG	0.008
	CGGT	0.004
	TGGT	0.002
	TAGT	0.002
	CGAG	0.001
	CGGG	0.001
3	ACGCA	0.510
	GGTAG	0.450
	GGGCA	0.010
	ACGAA	0.010
	ACTAG	0.050
	GCTAG	0.050
	GGGAA	0.003
	GGTCG	0.003
	ACTCG	0.002
	AGGCA	0.002
	ACGCG	0.001
	GCGCA	0.001
2	CGGTAAT	0.930
	GAACTGC	0.060
	GAATTGC	0.004
	CAACTGC	0.003
	CAGTAAT	0.002
	GAACAGT	0.002
	CGGTAAC	0.001
	CGGTAGC	0.001
9	GTG	0.700
	TAA	0.270
	TTG	0.020
	GAA	0.006
	GAG	0.005
10	TC	0.900
	CT	0.090
	TT	0.004
	CC	0.002
1	CAGATACA	0.590
	AGACCCTT	0.330
	CAGATCTT	0.020
	AGACCACA	0.020
	CAGATATT	0.006
	CGACCCTT	0.004
	AAGATACA	0.003
	CAGATCCA	0.003
	CAGACACA	0.003
	CGAATCCT	0.002
	AGACCATA	0.002
	AGACTCTT	0.002
	CAGCTACA	0.002
	AGACCCCT	0.002
	AAGCCATA	0.002
	CGACCACA	0.001
	CGACTCTT	0.001
	CAGACACT	0.001
	AAGCTACA	0.001
	CAGATCCT	0.001
	AAGATCCT	0.000
	AAGATCTT	0.000
5	CGCC	0.530
	TATA	0.450
	TGCC	0.005
	CGCA	0.003
	TGTA	0.003
	CGTC	0.003
	CATA	0.002
	TATC	0.001
	CACC	0.001
	TACC	0.001
11	CC	0.740
	TT	0.260
6	CCGT	0.830
	TTAC	0.140
	TCGT	0.030
	TTGC	0.005
	CCAT	0.002
	CCAC	0.001
	TTGT	0.001
	TCAC	0.001
12	GG	0.670
	AA	0.300
	GA	0.020
	AG	0.010

Grade-of Membership Analysis

Patterns of polymorphisms associated with high and low risk were identified by grade-of-membership analysis (GoM), alluding to the graded membership scores of individuals in the identified GoM groups; here, patterns of polymorphisms. These scores reflect the resemblance of an individual to the groups. Each group is represented by a set of outcome probabilities for the variables, e.g. being male or female.

More formally, the GoM model likelihood can be described after first identifying four indices. One is the number of subjects I (i= 1, 2, … , I). Here, I= 550. The second index is the number of variables J (j= 1, 2, … , J). There are J= 9 variables (final model). Our third index is Lj: the set of response levels for the Jth variable. This leads to the definition of the basic GoM model where the probability that the ith subject has the Ljth level of the Jth variable is defined by a binary variable (ie, y_ijl= 0, 1). The model with these definitions is

• (1)

where the g_ik are convexly constrained scores (i.e., 0.0 ≤g_ik≤ 1.0; ∑_k g_ik= 1.0) for subjects and the λ_kjl are probabilities that, for the Kth latent group, the Ljth level is found for the Jth variable. The procedure thus uses this expression to identify K profiles representing the pattern of J×Lj responses found for I subjects.

The parameters g_ik and λ_kjl are estimated simultaneously using the likelihood function (in its most basic form).

• (2)

In the likelihood y_ijl is 1.0 if the Ljth level is present and 0.0 if it is not present.

Variables used to define the GoM groups are termed ‘internal’ variables. Initially, each facet of the data was used to construct models specifying K = 2, 3, 4, 5, or 6 groups. These models, despite efforts to minimize LD, reflected relationships among the variables unrelated to PD status. Thus a second set of models was constructed employing a reduced set of ‘internal’ variables: PD status, sex, mutation status, number of very low frequency alleles, and information on five SNP blocks that demonstrated a modicum of association with PD (B15*, B4*, B28*, B31*, B43*) (Armitage trend test, p < 0.10, HelixTree Software).

Information on the other LD blocks further characterized the groups as ‘external’ variables. One option in the likelihood is to separate calculations for ‘internal’ and ‘external’ (here, LD blocks not demonstrating evidence of association) variables. For internal variables, maximum likelihood estimations [MLE] of g_ik and λ_kjl are generated and the information in internal variables is used to define the K groups. For external variables the likelihood is evaluated (and MLE of λ_kj; generated) but the information is not used to redefine the K groups; that is, the likelihood calculations for likelihood equations involving the g_ik are disabled for external variables so that the g_ik, and the definition of the K groups, is not changed. The model presented here represents three patterns of polymorphisms that distinguish parsimoniously between high and low risk, and identifies two patterns associated with risk, one concise and the other diverse.

Results

Overview

Three patterns of genetic variation were identified that represent 94% of the subjects. The number of very low frequency alleles and the occurrence of mutation played very limited roles in defining the patterns and distinguishing the subjects. Pattern I represented a specific set of minor alleles as a background to PD among about a third of cases. Pattern II was unaffected. Pattern III represented PD associated with a more diverse occurrence of other low frequency alleles. Age at the time of diagnosis ranged from age 60 to the late 80's for both patterns I and III, but occurrence before age 65 was more likely for pattern I (22% vs 11%).

Pattern I: A specific set of minor alleles scattered throughout LRRK2 was a common background to sporadic PD (Table 3). A core set of minor alleles was found: Minor alleles were the rule for B15* (GA, not GG), for B4* (CAGT:TGAG– or else diverse minor diplotypes, not the common diplotype CAGT:CAGT) and for B43* (GA or AA, not GG). These three LD blocks include all six of the SNPs individually associated with PD in the sample, and were the most informative blocks (B4*, H = 0.55; B15*, H = 0.53; B43*, H = 0.52). The H statistic (Shannon, Bell Laboratories, Berkeley Heights, NJ, USA) describes the extent to which outcomes for the variable differ for patterns I, II, and III. Values above 0.50 denote strong differences among the GoM groups for the variable.

Table 3.  Patterns of risk (I, II, III) for sporadic PD involving LRRK2.

Variable	Outcome	I	II	III	H
Patterns of risk for PD are displayed (I, II, III). Each pattern is defined by the displayed probabilities. Pattern I is affected and carries a set of minor alleles for B15*, B17, B4* (most notably), B28*, B31*, B11, B43* and B46. Pattern II is unaffected and at low risk with respect to genetic variation within LRRK2. Pattern III is affected and has slightly elevated minor allele frequencies at locations not found for pattern I. Variables used to identify the patterns are shown in bold. The GoM maximum likelihood method automatically generates outcome probabilities for other variables based on the membership of individuals in the identified sets. Most subjects matched (100% membership) or closely resembled (>50% membership) one of these patterns: I: 46 cases were exact matches (91 were close matches); II: 178 control subjects were exact matches (250 were close matches); III: 137 cases were exact matches (178 were close matches). None of the control subjects were exact matches to I or III; 10% of control subjects resembled I. Information content for each variable is denote by ‘H’. Values near zero indicate similar outcome frequencies similar for each set; higher values indicate greater information content.
PD status	Control	0	100	0	0.71
	Onset age 59 to 64	22	0	11
	Onset age 65 to 74	43	0	55
	Onset age 75 to 88	35	0	34
Sex	Control	0	100	0	0.70
	Male	68	0	59
	Female	32	0	41
Mutation	No	98	97	95	0.02
	Yes, case	2	0	5
	Yes, control	0	3	0
Number of very low freq. alleles	0	74	80	73	0.004
	1	22	16	20
	2 to 5	4	4	7
B13	CC	47	50	52	0.004
	CG	46	44	37
	GG	7	7	11
B7	TAT:TAT	90	85	86	0.03
	TAT:CTC	0	12	13
	Low freq.	10	4	1
B14	CC	91	67	67	0.03
	CT	9	33	33
B15*	GG	0	100	100	0.53
	GA	100	0	0
B16	CC	62	30	26	0.07
	CT	38	52	51
	TT	0	18	23
B17	TT	0	92	98	0.42
	TG	100	8	2
B18	CC	72	47	39	0.04
	CT	28	46	47
	TT	0	8	14
B8	TCC:TCC	60	28	29	0.07
	TCC:CTA	38	43	42
	CTA:CTA	0	17	15
	Low freq.	2	13	14
B19	AA	98	95	93	0.004
	AG or GG	2	5	7
B20	CC	76	50	41	0.05
	CT	24	43	47
	TT	0	8	12
B21	TT	61	25	19	0.09
	TC	39	53	54
	CC	0	22	27
B22	GG	90	59	57	0.04
	GA	10	41	43
B4*	CAGT:CAGT	0	100	100	0.55
	CAGT:TGAG	86	0	0
	Low freq.	14	0	0
B23	CC	87	53	58	0.04
	CT	13	47	42
B3	ACGCA:ACGCA	0	36	38	0.10
	ACGCA:GGTAG	48	39	45
	GGTAG:GGTAG	45	15	11
Low freq.		7	10	6
B2	CGGTAAT:CGGTAAT	95	82	83	0.01
	Low freq.	5	18	17
B9	GTG:GTG	70	45	43	0.04
	GTG:TAA	25	41	42
	TAA:TAA	0	11	9
	Low freq.	5	4	6
B10	TC:TC	85	83	77	0.01
	TC:CT	12	16	20
	Low freq.	3	1	3
B24	CC	90	70	70	0.02
	CA	10	30	30
B25	AA	95	84	85	0.005
	AG or GG	5	16	15
B26	AA	92	85	83	0.005
	AC or CC	8	15	17
B27	TT	89	70	71	0.02
	TC	11	30	29
B28*	GG	0	58	58	0.25
	AG	62	42	42
	AA	38	0	0
B29	AA	98	95	93	0.03
	AC	2	5	7
B1	CAGATACA:CAGATACA	59	27	32	0.06
	AGACCCTT:CAGATACA	25	48	40
	AGACCCTT:AGACCCTT	0	15	18
	Low freq.	16	8	10
B30	CC	86	71	70	0.01
	CT or TT	14	29	30
B31*	CC	94	100	89	0.03
	CT or TT	6	0	11
B5	CGCC:CGCC	49	20	21	0.08
	CGCC:TATA	47	54	47
	TATA:TATA	0	23	29
	Low freq.	4	4	2
B32	TT	93	99	98	0.01
	CT	7	1	2
B33	TT	98	94	93	0.003
	GT or GG	2	6	7
B34	AA	80	54	57	0.02
	AG	20	46	43
B35	GG	98	94	94	0.003
	AG or AA	2	6	6
B00	AA	99	98	98	0.001
	AG	1	2	2
B36	CC	98	92	89	0.01
	CT	2	8	11
B11	CC:CC	1	72	78	0.24
	CC:TT	99	28	22
B37	GG	65	29	31	0.07
	GT	35	52	51
	TT	0	19	18
B38	CC	70	42	38	0.05
	CT	30	42	42
	TT	0	15	19
B6	CCGT:CCGT	87	69	62	0.02
	CCGT:TTAC	8	22	28
	Low freq.	4	8	11
B39	AA	46	20	16	0.09
	AC	54	50	50
	CC	0	31	34
B40	GG	65	38	36	0.04
	AG	34	50	51
	AA	1	12	13
B41	AA	98	96	94	0.003
	AG	2	4	6
B12	GG:GG	48	46	43	0.02
	GG:AA	41	40	41
	AA:AA	1	12	13
	Low freq.	9	3	3
B42	GG	86	77	77	0.005
	GA	14	23	23
B43*	GG	0	100	100	0.52
	GA or AA	100	0	0
B44	AA	65	32	34	0.05
	AC	33	52	47
	CC	1	17	19
B45	AA	86	74	73	0.01
	AG	14	26	27
B46	TT	5	80	85	0.24
	TG	95	20	15
B47	TT	95	95	94	0.0003
	TC or CC	5	5	6
B48	TT	51	25	24	0.06
	CT	50	50	52
	CC	0	25	24

The two LD blocks that demonstrated weaker evidence of association when considered individually contributed minor alleles to pattern I with some probability: B28*, AA (38% chance) or AG (H = 0.25); B31*CT or TT (6% chance) (H = 0.03). Values of H above 0.05 might be considered notable, while lower values indicate that the groups differ only slightly in genotypic frequency. However, it might be noted that SNPs with low frequency minor alleles can vary in frequency of the minor allele, e.g. 3-fold, and still have a low H-value.

Other LD blocks, not providing evidence of association when considered individually, also contributed minor alleles to pattern I: B17 carried TG, while II & III carried TT almost exclusively (H = 0.42); B3 carried ACGCA:GGTAG– or else diverse minor diplotypes, while II & III often carried ACGCA:ACGCA (H = 0.10); B11 carried CC:TT, while II & III often carried CC:CC (H = 0.24); and, B46 usually carried usually TG, while II & III usually carried TT (H = 0.46). This extends the pattern of association of minor alleles from intron 2 to intron 48 as representing a subset of PD.

Finally, diverse low frequency diplotypes were more likely for B7 (10%; 5′ near gene, intron 4, intron 7) (H = 0.03), B1 (16%; introns 30, 31, & 32, exon 34, introns 36 & 47, exon 49, intron 49) (H = 0.06), and B12 (9%; exon 43, 3′ near gene) (H = 0.02). Therefore, pattern I, taking the broadest definition, extends from the 5′ near-gene region to the 3′ near-gene region.

Pattern II: Unaffected. Minor alleles had low frequency. Four case subjects unexpectedly matched this low risk pattern with respect to LRRK2. Thus, PD was possible, if infrequent, when neither high-risk pattern for LRRK2 was present.

Pattern III: This typology also represents sporadic PD at age 60 and older. However, it does not follow the pattern of minor alleles found together for pattern I, and was more diverse. Minor alleles were slightly more likely for LD blocks not part of pattern I: B13, B16, B18, B8, B19, B20, B21, B22, B9, B26, B29, B33, B35, B00, B36, B38, B6, B40, B41, B42, B44, B45, and B47. Again, this pattern involved essentially the whole gene. Curiously, minor genotype frequencies at B19, B29, B33, B35 and B41 were essentially identical – higher for III than for I or II, suggesting that they might be found together for a small subset of cases. Both affected patterns had some chance of carrying minor alleles at B31*, this being the only point of overlap between the relatively distinct pattern found for I and the less distinct pattern of low frequency alleles found for III.

Resemblance of Individuals to the Patterns

This data analytic approach does not force individuals into discrete groups. Instead, individuals divide membership among model-based idealized groups, essentially stereotypes, here labeled, I, II or III, depending on the degree of resemblance. Subjects who match a particular group have a membership score of one in that group, and scores of zero in the other groups. Other subjects have positive scores in two or three groups, summing to one.

Here, the size of group I was 120.044, summing the membership of subjects in group I; clearly smaller than the number of case subjects. The size of group II was 242.766, less than the number of control subjects, indicating that some control subjects carry risk factors for PD. Group III was larger than group I, size 187.19, the sum of I and III being larger than the number of case subjects.

These model-based groups exactly matched many subjects: cases: 46 (17% of all cases), 4, 137; controls: 0, 178, 0. Most (94%) subjects resembled (>50% match) one of these patterns: cases: 91 (33% of all cases), 4, 178; controls: 27, 250, 0. Each case carrying p.G2019S had membership in III (1.00, 0.47, 0.83, 1.00) and, possibly, membership in I (0.00, 0.53, 0.17, 0.00), suggesting that the pattern of minor alleles found for I was not required for causation when the mutation was present.

Multiple Minor Alleles Found Together for Pattern I

To verify that multiple minor alleles were found together for a subset of cases, we went back to the data. All 46 cases who matched pattern I carried minor alleles for the core LD blocks B4* (“TGAG” or diverse minor alleles), B15* (AG), and B43* (AG or AA), whereas all 178 control subjects matching II (65% of all control subjects) and 137 cases matching III (50% of all cases) carried two copies of the common alleles for B4* (“CAGT”), B15* (GG), and B43* (GG).

The core set of minor alleles was usually found with minor alleles at B17 and B46. All 46 cases matching I carried TG or GG at B17, while the common TT genotype was usually found for controls matching II (166 of 178) and cases matching III (134 of 137). Almost all (45 of 46) cases matching I carried TG or GG at B46, whereas the common TT genotype was usually found for controls matching II (146 of 178) and cases matching III (118 of 137). Therefore, alterations in LRRK2 were often occurring together from intron 2 to intron 48 among a subset of 17% of the cases.

We then considered the 94% of cases who resembled (>50% match) one of the patterns (Fig. 1): Minor alleles were much more likely for all the SNPs comprising these mentioned LD blocks for the third of cases like pattern I than for other cases or the control subjects. This was most evident for each of the four loci that composed B4* (>95% probability at each locus).

Figure 1. The frequency of minor alleles is shown for PD cases like (>50% match, i.e. a membership score of 0.50 or higher) pattern I (n = 91), control subjects like pattern II (n = 250), and PD cases like pattern III (n = 178).

Discussion

There is ample evidence that the LRRK2 gene is a determinant of PD in certain families and for the general population, involving the p.G2019S mutation (Healy et al., 2008) and allelic associations of SNPs located within LRRK2 (Paisan-Ruiz et al., 2008). We sought to identify patterns of polymorphisms within the gene that more fully describe the genetic background of sporadic PD in relation to LRRK2. To accomplish this aim we identified LD blocks to simplify the data and allow identification of low frequency alleles. Information on genotype/diplotype for these blocks identified three patterns of risk represented by GoM groups (I, II, and III). GoM has been employed in a similar way to reduce complex data to a tractable number of patterns in previous medical and genetic studies; to define subtypes of disease and patterns of disease progression, endophenotypes, genetic risk sets for disease, and as a form of sibpair linkage analysis having apparently high statistical power (Corder & Woodbury, 1993; Corder et al., 2000; Corder et al., 2001; Corder et al., 2005; Corder et al., 2006; Corder & Hefler, 2006; Corder & Mellick, 2006; Corder et al., 2007; Corder et al., 2008a,b; Corder & Beaumont, 2007; Golanska et al., 2009; Hallmayer et al., 2005; Helisalmi et al., 2004; Iivonen et al., 2004; Licastro et al., 2007a,b).

The three patterns distinguished between high (I, III) and low (II) risk with respect to LRRK2, also defining a distinct subset of minor alleles found together, distributed widely throughout the gene across functional domains (I). These stereotypic backgrounds effectively partitioned the subjects into three groups: 94% of the subjects resembled one of the patterns, and a third of the cases carried most of the minor alleles characteristic of pattern I.

To emphasize that a distinct subset of cases was characterized by a set of minor alleles found together distributed throughout the LRRK2 locus, the 46 cases who matched pattern I, did, in fact, carry one or two copies of minor alleles at all six loci previously identified as associated with sporadic PD in the dataset, here, represented by B4*, B15*, and B43*. These cases usually carried additional minor alleles also identified as part of pattern I. None of the subjects matching patterns II (65% of all controls) or III (50% of all cases) carried minor alleles at these locations. Four of the six associated loci (Paisan-Ruiz et al., 2008) were located in one LD block (B4*), rs1907632_T (intron 11), rs11564205_G (intron 34), rs11564203_A (intron 39) and rs11829088_G (intron 39) (i.e. ‘TGAG’ or other very minor alleles), that extends from intron 11 to intron 39 across several functional domains of dardarin, including LRR, Roc, COR and Kinase domains.

The minor alleles at high probability for pattern I, taking the broadest definition, were located in non-coding regions throughout the gene region from the 5′ near gene region to the 3′ near gene region, except for B31* located in exon 32. Thus, alterations within introns located throughout the gene appear to alter LRRK2 function especially when they are found together, possibly defining a distinct very high risk allele. This information might possibly be used in the future to identify persons at very high risk before the onset of symptoms, when preventive interventions might be undertaken. It might also motivate focused cell culture studies of LRRK2 function.

Moreover, when evaluated in this way, diverse low frequency diplotypes were more likely for I than for II or III at B7 (which provided no statistically significant evidence of association on its own) (10%; 3 SNPS extending from the 5′UTR to intron 7), B1 (16%; 7 SNPs extending from intron 30 to intron 49), and B12 (9%; 1 SNP located within the 3′ UTR). These low frequency variants were also scattered throughout the LRRK2 locus and most of them were located within intronic sequences, with the exception of rs1427263 and rs3761863 at B1 spanning functional domains such as Roc, COR, Kinase and WD40 domains. Thus, LRRK2 alterations were dispersed from the 5′ UTR to the 3′ UTR regions, involving essentially the whole gene.

Taking a weaker criterion, namely, at least 50% match to pattern I, 91 cases (33%) had a relatively distinct genetic background to PD involving minor alleles at the LRRK2 locus: >95% of these cases carried minor alleles at all four loci that comprise LD block B4*. In contrast, none of the control subjects matched pattern I, although 27 had >50% resemblance to pattern I and might possibly be at elevated risk for PD.

Pattern III representing the majority of cases had only slight elevations in the frequencies of minor alleles at other locations. Many cases matched (n = 137) or resembled (n = 178) this pattern. None of the control subjects matched or resembled pattern III. Possibly, pattern III is a mixture of many patterns of vulnerability involving LRRK2. The only point of overlap between I and III was that both patterns involved the possible occurrence of minor alleles at B31*. The stereotypic groups had the following probability of carrying a minor allele at B31*: 6% chance, 0%, 11%. Thus the minor allele was associated with high risk. However, eight control subjects (3%) carried B31*TC; three of whom resembled pattern I and might be considered to be at elevated risk for PD, and five of whom had limited resemblance to I and/or III having membership scores of from 0.33 to 0.41, and might be considered to be at lesser risk.

The conclusion that we draw is that a well-defined subset of PD occurring at ages 60 and older in populations with European ancestries has a pattern of multiple minor alleles found together. This information might be useful to define risk for presently healthy individuals. Whether these findings apply to other populations is an open question. None of the six SNPs included in a core set of matches to pattern I (B15*, B4*, B43*) would be useful when investigating Asian populations (Table 4); therefore, the investigated set of SNPs are not relevant to all other populations.

Table 4.  Core SNP frequencies in diverse populations.

CEU	SNP	Position	ObsHET	PredHET	HWpval	MAF	Alleles
	rs11175655	38909994	0.183	0.193	1	0.108	G:A
	rs1907632	38936769	0.233	0.231	1	0.133	G:A
	rs11564205	39000276	0.233	0.231	1	0.133	A:G
	rs11564203	39010848	0.233	0.231	1	0.133	G:A
	rs11829088	39014046	0.233	0.231	1	0.133	T:G
	rs11564173	39036738	0.15	0.167	0.79	0.092	G:A

YRB	SNP	Position	ObsHET	PredHET	HWpval	MAF	Alleles
	rs11175655	38909994	0.117	0.139	0.55	0.075	G:A
	rs1907632	38936769	0.217	0.219	1	0.125	G:A
	rs11564205	39000276	0.333	0.339	1	0.217	A:G
	rs11564203	39010848	0.317	0.289	0.87	0.175	G:A
	rs11829088	39014046	0.333	0.32	1	0.2	T:G
	rs11564173	39036738	0.333	0.299	0.75	0.183	G:A

CHB-JPT	SNP	Position	ObsHET	PredHET	HWpval	MAF	Alleles
Core SNP frequencies in diverse populations. Analysis performed by Haploview 4.1 software (http://www.broad.mit.edu/haploview/haploview) with HapMap data http://www.hapmap.org/. YRI: Yoruba in Ibadan, Nigeria; JPT: Japanese in Tokyo, Japan; CHB: Han Chinese in Beijing, China; CEU: CEPH (Utah residents with ancestry from northern and western Europe).
	rs11175655	38909994	0	0	0	0	G:G
	rs1907632	38936769	0.011	0.011	1	0.006	G:A
	rs11564205	39000276	0.011	0.011	1	0.006	A:G
	rs11564203	39010848	0.011	0.011	1	0.006	G:A
	rs11829088	39014046	0.011	0.011	1	0.006	T:G
	rs11564173	39036738	0	0	0	0	G:G

Mutations were not a major background to sporadic PD (<5%). The four case subjects who carried p.G2019S resembled III more than I, suggesting that the pattern of multiple minor alleles found for I was not needed for the mutation to be penetrant. The phenotypic variability, and incomplete penetrance, found in some p.G2019S carriers may depend on specific alterations found for LRRK2 and contributions of interacting proteins. Very low frequency alleles, mostly found in flanking intronic regions, played only a small role.

One limitation of many genetic studies is that control subjects are drawn from persons who are not yet affected rather than persons established to be at low risk. Here, 10% of the control subjects who resembled I may not have displayed any clinical features because of their age at the sample collection, or the absence of other important risk factors. The data analytic approach taken here tends to minimize the problem of control subjects at high-risk when identifying the genetic background relevant to disease.

No significant association between disease and common variability in LRRK2 has been previously reported in samples of European ancestry (Biskup et al., 2005; Paisan-Ruiz et al., 2005, 2006); however, these data suggest that LRRK2 variations may contribute to the risk for sporadic PD in the North American population and that this contribution is triggered mainly by multiple low frequency minor alleles scattered throughout the LRRK2 locus. One speculation is that low frequency alleles as a class are less robust compared to the more common alleles. These results are cautionary suggesting that information on low frequency alleles should not be ignored in data analysis, e.g. they can be grouped together, that stringent p-values in genome-wide studies may ignore what might later turn out to be important risk factors, and that where possible the use of LD and higher dimensional data analysis may be needed to establish a pattern(s) of risk.

These findings indicate the importance of specific multiple minor alleles within the LRRK2 gene as a background to perhaps one-third of sporadic PD occurring at ages 60 and older, and that a second pattern of risk involving minor alleles at alternate loci might, in part, be a background to sporadic PD among the majority of cases. However, further analyses in the LRRK2 gene and additional molecular approaches, such as gene-gene interactions and gene-environment-interactions, are probably necessary in order to assess the role of minor alleles within the LRRK2 locus in idiopathic PD and to gain molecular insights into the biochemical pathway that underlies this complex disorder.

Acknowledgements

All samples used here were from the National Institute of Neurological Disorders and Stroke–supported Neurogenetics Repository hosted by the Coriell Institute for Research (Camden, NJ; http://ccr.coriell.org/Sections/Collections/NINDS/). This work was supported in part by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, Department of Health and Human Services, project Z01 AG000957-06.