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. Author manuscript; available in PMC: 2014 Nov 20.
Published in final edited form as: Genet Epidemiol. 2010 Feb;34(2):133–139. doi: 10.1002/gepi.20442

Shades of gray: A comparison of linkage disequilibrium between Hutterites and Europeans

Emma E Thompson 1, Ying Sun 1, Dan Nicolae 1,2, Carole Ober 1
PMCID: PMC4238926  NIHMSID: NIHMS635587  PMID: 19697328

Abstract

Founder or isolated populations have advantages for genetic studies due to decreased genetic and environmental heterogeneity. However, whereas longer range linkage disequilibrium (LD) in these populations is expected to facilitate gene localization, extensive LD may actually limit the ability for gene discovery. The North American Hutterite population is one of the best characterized young founder populations and members of this isolate have been the subjects of our studies of complex traits, including fertility, asthma and cardiovascular disease, for >20 years. Here, we directly assess the patterns and extent of global LD using single nucleotide polymorphism (SNP) genotypes with minor allele frequencies (MAFs) ≥5% from the Affymetrix GeneChip® Mapping 500K array in 60 relatively unrelated Hutterites and 60 unrelated Europeans (HapMap CEU). Although LD among some marker pairs extends further in the Hutterites than in Europeans, the pattern of LD and minor allele frequencies are surprisingly similar. These results indicate that 1) identifying disease genes should be no more difficult in the Hutterites than in outbred European populations, 2) the same common susceptibility alleles for complex diseases should be present in the Hutterites and outbred European populations, and 3) imputation algorithms based on HapMap CEU should be applicable to the Hutterites.

Introduction

Finding and characterizing variation in the human genome that confers risk for common diseases remains a major challenge in human genetics. The advantages of isolated and founder populations for genetic mapping studies have long been recognized with respect to Mendelian diseases [de la Chapelle 1993; Peltonen 2000], and more recently with respect to common diseases [Heutink and Oostra 2002; Peltonen, et al. 2000]; also see Wright, Carothers et al. [Wright, et al. 1999] for a review of the importance of population choice for study design [Lander and Schork 1994; Sheffield, et al. 1998]. Both reduced genetic heterogeneity due to the small number of founders and the increased linkage disequilibrium (LD) due to recent ancestry facilitated the discovery of many rare Mendelian disease genes in founder populations, such as the Finns (e.g., [Nikali, et al. 1995; Sankila, et al. 1987]), Amish (e.g., [Polymeropoulos, et al. 1996; Ruiz-Perez, et al. 2000]), Hutterites (e.g. [Boycott, et al. 2008; Weiler, et al. 1998]), Ashkenazi Jews (e.g., [Anderson, et al. 2001; Slaugenhaupt, et al. 2001]), Sardinians (e.g., [Verhoeven, et al. 2001]), and Icelanders (e.g., [Gulcher, et al. 1997]). Although these same features, in addition to the generally more uniform lifestyle shared among members of these groups, should also enhance our ability to discover common risk alleles for complex diseases [Heutink and Oostra 2002; Lander and Schork 1994; Newman, et al. 2004], the more extensive LD in these populations may limit the ability to move from gene localization to gene discovery.

Recent technological advances have allowed the incorporation of LD-based genome-wide association studies into the arsenal of mapping tools in many laboratories. Numerous platforms are available that generate dense single nucleotide polymorphism (SNP) genotypes (300,000 to 1 million), and the HapMap [Frazer, et al. 2007] provides the framework for both interpreting the results of genome-wide association studies and imputing genotypes for SNPs not represented on the platforms [Browning and Browning 2007; Marchini, et al. 2007; Nicolae 2006; Servin and Stephens 2007]. However, the applicability of the HapMap resource for interpreting results of genome-wide association studies and for imputing genotypes in isolated populations has not been extensively evaluated (see Bonnen et al. 2006 [Bonnen, et al. 2006] for an exception).

We have focused our genetic mapping studies in the Hutterites of South Dakota. The Hutterites are an Anabaptist religious group established in the Tyrolean Alps in 1528. The more than 1,000 Hutterites in our studies are related to each other in a 13-generation pedigree with 62 founders, The average inbreeding coefficient is approximately 3%, equivalent to that of 1 ½ cousins (first cousins once removed) [Ober, et al. 1998]. Hutterites live communally on large farms, called colonies, resulting in a remarkably similar environment among individuals both within and between colonies and reduces confounding effects of environmental variation. This isolate is one of the best characterized young founder populations and has been the subject of our genetic studies for more than 20 years [Abney, et al. 2002; Gallego Romero and Ober 2008; Newman, et al. 2004; Ober, et al. 2001; Ober, et al. 1987; Ober, et al. 2000; Weiss, et al. 2006].

Here, we present the results of the first genome-wide comparison of LD between the Hutterites and unrelated Europeans (HapMap CEU) using genotype data from the Affymetrix Mapping GeneChip® 500K mapping array. Our results illustrate that the differences in patterns of LD and minor allele frequencies (MAFs) between these two populations are quite subtle and indistinguishable in much of the genome. We conclude that identifying complex diseases genes should be no more challenging in the Hutterites compared to outbred European populations, common susceptibility alleles for complex diseases that are present in outbred European populations should also be present in the Hutterites, and imputation algorithms based on HapMap CEU should be applicable to the Hutterites.

Materials and Methods

For these studies, we selected 60 Hutterites (30 females and 30 males) who are not related to one another as first-degree relatives. For an outbred European comparison group, HapMap CEU, we utilized genotypes for the parents of 30 trios to represent 60 unrelated individuals (30 females and 30 males).

Genotypes for SNPs from the Affymetrix GeneChip ® Mapping 500K Array that were present on both the early access and commercial chip (N=428,867) and had a MAF >5% (N=315,493) in the Hutterites were subject to quality control (QC) checks (for details on our QC checks and SNP composition, see [Ober, et al. 2008]). Briefly, SNPs with Hardy-Weinberg equilibrium P <0.001 (N=5,080), more than five Mendelian errors (N=11,359), and call rates <90% (N=3,617) were further excluded, yielding a final set of 295,437 SNPs. Genotypes for all autosomal QC SNPs in the Hutterites were obtained for the HapMap (CEU) to assess global LD.

In addition to assessments of global (genome-wide) LD, we also investigated the LD pattern in eight discrete regions. First, we selected the longest and shortest q arms (2q and 21q, respectively) in the human genome. Next, we selected four 500 Kb genomic regions on the basis of recombination rate and gene density, and one region each on Xp and Xq from the dataset of Conrad et al. [Conrad, et al. 2006], to represent the most extreme of the first two categories (highest and lowest recombination rates and gene densities) and the two X chromosome arms.

Results

We first examined MAFs in the Hutterites and CEU by comparing the longest (chromosome 2q) and shortest (chromosome 21q) chromosome q-arm. Allele frequencies for SNPs on 2q and 21q are highly correlated in the Hutterites and CEU (r=0.646 and 0.656, respectively), but show wide variation at each MAF (Figure 1). A similar pattern of scatter to that in Figure 1 was seen in simulated data sets where genotypes were generated from populations with identical MAFs (data not shown).

Figure 1.

Figure 1

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Comparison of minor allele frequencies between Hutterites and CEU samples on chr2q and chr21q. MAFs in Hutterites (N=60) are plotted on the X axis, and CEU (N=60) appears on the Y axis. Only SNPs with MAF≥5% are included.

We next investigated patterns of LD between the two populations by comparing chromosomes 2q and 21q as well as the entire genome, for all SNPs within 500Kb windows (Figure 2). Median r2 values at distances greater than 100 Kb are similar for the two populations (solid lines in Figure 2), although LD extends over longer distances in the Hutterites at r2<0.20 (genome-wide median r2=0.047 in the Hutterites vs. 0.024 in CEU; see inset tables, Figure 2).

Figure 2.

Figure 2

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Comparison of LD (measured using r2) in CEU (blue lines) and Hutterites (green lines). Solid lines correspond to median values and dotted to upper and lower quartiles. Inter-marker distance (Kb) is shown on the X axis. Pairwise LD values between markers on chromosomes 2q (A), 21q (B), and the whole genome (C), are plotted. r2 values in inset tables are calculated based on all SNP pairs in 10 Kb windows.

The percentages of SNP pairs with strong LD (r2≥0.8) show minor differences in the two populations, even at large inter-marker distances (Table 1). The number of SNP pairs with r2≥0.8 at distances up to 10kb is remarkably similar for more common SNPs (82 and 79% for MAF between 31-40% and 83 and 80% for MAF between 41-50% in Hutterites and CEU, respectively) as well as less frequent SNPs (i.e., MAFs of 5-10%) (89 and 85% in Hutterites and CEU, respectively). Even at distances up to 500kb, the percentage of SNP pairs showing either r2≥0.8 or r2=1 is very similar at MAFs greater than 10%.

Table 1.

Counts and percentages of autosomal SNP pairs showing perfect (r2=1) or strong (r2≥0.8) LD at various inter-marker distances (IMD) in the Hutterites and CEU. For each MAF bin, only SNPs with corresponding MAFs in both populations are included.

MAF 5-10%
IMD (kb) r2=1 r2≥0.8
HT CEU HT CEU
Count % Count % Count % Count %
≤10 2,653 82.73 2,332 72.72 2,867 89.40 2,735 85.28
10-20 1,195 68.80 895 51.53 1,315 75.71 1,177 67.76
20-50 1,733 57.19 1,163 38.38 2,001 66.04 1,688 55.71
50-100 1,077 36.52 714 24.21 1,294 43.88 1,045 35.44
100-200 692 16.34 375 8.86 889 21.00 648 15.30
200-300 201 5.74 78 2.23 275 7.86 113 3.23
300-400 76 2.36 28 0.87 99 3.07 38 1.18
400-500 45 1.50 8 0.27 78 2.60 19 0.63
MAF 11-20%
IMD (kb) r2=1 r2≥0.8
HT CEU HT CEU
Count % Count % Count % Count %
≤10 11,887 69.07 10,193 59.23 13,631 79.20 13,141 76.36
10-20 4,736 49.40 3,422 35.69 5,841 60.93 5,307 55.36
20-50 6,355 32.49 4,163 21.28 8,372 42.80 7,127 36.43
50-100 3,667 15.35 2,220 9.29 5,414 22.67 4,127 17.28
100-200 2,578 6.60 1,480 3.79 3,859 9.89 2,702 6.92
200-300 1,224 3.34 787 2.15 1,799 4.91 1,153 3.15
300-400 705 2.04 538 1.55 980 2.83 677 1.95
400-500 545 1.61 489 1.44 748 2.21 565 1.67
MAF 21-30%
IMD (kb) r2=1 r2≥0.8
HT CEU HT CEU
Count % Count % Count % Count %
≤10 19,123 69.10 16,024 57.90 22,243 80.38 21,537 77.82
10-20 7,370 49.30 5,170 34.59 9,306 62.26 8,495 56.83
20-50 9,421 31.26 5,956 19.76 12,977 43.06 11,114 36.87
50-100 5,221 14.27 2,968 8.11 8,049 22.00 6,309 17.24
100-200 3,201 5.35 1,746 2.92 5,170 8.65 3,753 6.28
200-300 1,354 2.41 814 1.45 2,135 3.81 1,399 2.49
300-400 720 1.35 541 1.02 1,083 2.03 716 1.34
400-500 552 1.07 492 0.96 805 1.56 617 1.20
MAF 31-40%
IMD (kb) r2=1 r2≥0.8
HT CEU HT CEU
Count % Count % Count % Count %
≤10 25,439 69.47 21,200 57.90 29,885 81.62 29,055 79.35
10-20 9,477 49.00 6,552 33.88 12,241 63.30 11,243 58.14
20-50 11,816 30.87 7,282 19.02 16,786 43.85 14,544 37.99
50-100 6,326 13.68 3,425 7.41 10,351 22.39 8,141 17.61
100-200 3,719 4.94 1,949 2.59 6,292 8.36 4,800 6.37
200-300 1,465 2.10 822 1.18 2,350 3.37 1,564 2.24
300-400 721 1.09 541 0.81 1,102 1.66 735 1.11
400-500 552 0.86 492 0.76 812 1.26 618 0.96
MAF 41-50%
IMD (kb) r2=1 r2≥0.8
HT CEU HT CEU
Count % Count % Count % Count %
≤10 30,168 69.79 24,942 57.70 35,678 82.54 34,752 80.40
10-20 10,877 49.10 7,374 33.29 14,218 64.18 13,108 59.17
20-50 13,467 30.57 8,123 18.44 19,645 44.59 17,078 38.77
50-100 7,058 13.47 3,798 7.25 11,982 22.86 9,526 18.17
100-200 4,011 4.75 2,072 2.45 7,127 8.44 5,481 6.49
200-300 1,526 1.96 838 1.07 2,598 3.33 1,727 2.21
300-400 756 1.02 542 0.73 1,229 1.65 867 1.17
400-500 583 0.81 492 0.69 881 1.23 687 0.96
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Six discrete regions representing different chromosomal features were selected for comparison between the Hutterite and CEU (Figure 3; for details regarding each region see Table S1). Similar to the genome-wide patterns discussed above, patterns of LD in the six regions are remarkably similar between the two populations. While there are some instances of stronger LD between distant marker pairs in the Hutterites (for example, the q-arm of chromosome X and the region of low recombination on chromosome 12; outlined in red), there are also examples of stronger LD between marker pairs in the CEU sample (gene-poor region of chromosome 21 and p-arm of chromosome X, outlined in blue). These results suggest that while a particular pair of markers may show differences in LD between populations, overall, the CEU and Hutterite populations are not characterized by large scale differences in the distribution and extent of LD. Thus, patterns of LD in the HapMap CEU samples should be informative for imputing genotypes and patterns of LD in the Hutterites, for the majority of variation in the genome.

Figure 3.

Figure 3

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Comparisons of LD between SNPs in 500 Kb regions in 60 CEU and 60 Hutterite individuals. Hutterite data appear on the lower right and CEU on the upper left of each panel. Examples of regions that show greater LD in Hutterites or CEU are boxed in red or blue, respectively (see Results). Solid gray lines extending the length of the region in the HapMap samples indicate missing data. The genomic coordinates for the six regions can be found in Table S1.

DISCUSSION

Despite the extreme bottleneck in the Hutterite population and the small number of founding genomes that have given rise to the current population [Hostetler 1985], the allele frequency spectrum and pattern of LD are remarkably similar to modern Europeans. This may be partially due to the rapid population expansion following their arrival in North America: the population grew from about 116 individuals when they settled in Russia in the 1700's to an estimated 40,000-50,000 members living today in North America [Hostetler 1985].

Simulation studies predicted that rapid population growth following a founding event can reduce LD in a population [Slatkin 1994], and unexpected decays of LD over relatively short distances have been attributed to rapid population growth in other relatively young genetic isolates [Katoh, et al. 2002; Laan and Paabo 1997]. In addition, high-frequency polymorphisms, which we focused on in this study, are likely to be ancient in origin and to have been present in more than one ancestor and on multiple haplotypes at the time of the founding. Thus, as suggested by simulations, recent demographic events may have little impact on the extent of LD between common SNPs [Kruglyak 1999; Pritchard and Przeworski 2001]. This is supported by empirical studies of LD in populations with different demographic histories but of shared origins [Dunning, et al. 2000; Eaves, et al. 2000; Pardo, et al. 2009; Taillon-Miller, et al. 2000; Xing, et al. 2008].

Only one other recent study has examined genome-wide patterns of LD in a founder population, the Kosrae of Micronesia [Bonnen, et al. 2006]. In that study, pairwise LD between and MAFs for ~110,000 SNPs were compared between 30 Kosraen trios (KOS) and 30 trios from each HapMap population (CEU, YRI, JPT, CHB). Allele frequency distributions were similar for SNPs with MAFs ≥15%, although the proportion of SNP pairs with r2 > 0.8 was higher in the KOS than in the HapMap samples for SNPs with MAF > 0.15, using the (then) current technology could still cover 78% of the SNPs in the genome with high (r2 > 0.8) efficiency [Bonnen, et al. 2006]. Thus, similar to the results of our studies [and the other reports in this issue], we conclude that while differences between isolated and outbred populations may exist on a local scale, global patterns LD, as well as MAF distributions, are likely to be similar for common SNPs across populations with very different demographic histories.

In conclusion, we suggest that common susceptibility alleles for complex diseases will be shared by the Hutterites and outbred European populations (for examples, see [Newman, et al. 2004; Ober, et al. 2008; Weiss, et al. 2005] and that identifying these risk variants should be no more difficult in the Hutterites than in outbred European populations. Lastly, the similar patterns of LD in the Hutterites and HapMap CEU further suggest that imputation algorithms based on LD patterns in HapMap CEU individuals are applicable to the Hutterites. On the other hand, the homogeneous environment that results from their communal lifestyle should reduce environmental and lifestyle heterogeneity, and may facilitate gene discovery for complex diseases in the Hutterites in smaller sample sizes than those required for gene discovery in outbred individuals.

Supplementary Material

01

Acknowledgments

GRANT SUPPORT: R01 HL085197.

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