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. Author manuscript; available in PMC: 2017 Jan 6.
Published in final edited form as: Ann Hum Genet. 2009 May 21;73(Pt 4):391–403. doi: 10.1111/j.1469-1809.2009.00524.x

Parkinson’s disease and low frequency alleles found together throughout LRRK2

Coro Paisán-Ruiz a, Nicole Washecka b, Priti Nath b, Andrew B Singleton b,c, Elizabeth H Corder d
PMCID: PMC5217459  NIHMSID: NIHMS125612  PMID: 19489756

Abstract

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 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 people1. 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%)2 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 ancestry2. The gene is large, spanning 1.4 Mb, 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 signaling3. It is expressed in multiple brain regions, particularly in the substantia nigra, consistent with direct involvement in dopaminergic cell death.

Nonetheless, a genome-wide search4 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 & 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)4,5.

Paisan-Ruiz et al. then sequenced all 51 exons, and at least 50 bp of flanking intronic sequence, for the sample6. Twelve patients were found to carry coding variants (4 cases carried p.G2019S) as well as 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 chi-square 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)6.

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)710.

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 approaches11. 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 arrays4. 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 LRRK2 variants (p.C228S, p.Y707Y, p.A716V, p.K871E, p.L1870F, p.E2395K, p.G2432G)6. 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 LD6. 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)6. Thus B4* contained four of the six associated SNPs distributed across functional domains including LRR, Roc, COR and Kinase domains.

Table 1. LD blocks located within the LRRK2 gene.

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. SNPs previously associated with PD [B15* (rs1157655), B4* (rs1907632, rs11564205, rs11564203, rs11829088), B43* (rs11564173); p < 0.05 in chi-sq testing]6 and those identified here according to a less stringent criterion [B28* (rs10784498), B31* (rs33958906); p < 0.10 in an additive co-dominant model] are highlighted in red, and tagged with an asterisk.

Block
Number
SNP Name Minor
Allele Freq.
Location
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.

Haplotype frequencies for LD blocks B1 to B12 consisting of multiple SNPs.

Block Haplotype Frequency
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, yijl = 0, 1). The model with these definitions is

Prob(yijl=1.0)=kgikλkjl, (1)

where the gik are convexly constrained scores (i.e., 0.0 ≤ gik ≤ 1.0; Σk gik = 1.0) for subjects and the λkjl 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 gik and λkjl are estimated simultaneously using the likelihood function (in its most basic form).

L=ijl(kgikλkjl)yijl. (2)

In the likelihood yijl 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 gik and λkjl 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 λkj; generated) but the information is not used to redefine the K groups, that is, the likelihood calculations for likelihood equations involving the gik are disabled for external variables so that the gik, and the definition of the K groups, is not changed. The model 7 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 I and III, but occurrence before age 65 was more likely for 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 sample6, and were the most informative blocks (B4*, H=0.55; B15*, H=0.53; B43*, H=0.52). The H statistic (Shannon Labs) describes the extent that outcomes for the variable differ for 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.

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’ (Shannon, Bell Labs): Values near zero indicate similar outcome frequencies similar for each set; higher values indicate greater information content.

Variable Outcome I II III H
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 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 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 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 (Figure 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. Minor allele frequencies.

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 mutation2 and allelic associations of SNPS located within LRRK26. 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 power1126.

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 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 loci6 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 may 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 who 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*)6 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.

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).

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
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 controls 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 ancestry2729; 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 ignor 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 the idiopathic PD and to gain molecular insights into the biochemical pathway that underlies this complex disorder.

Acknowledgments

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.

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