- Open Access
Whole genome sequencing data from pedigrees suggests linkage disequilibrium among rare variants created by population admixture
© Feng and Zhu; licensee BioMed Central Ltd. 2014
- Published: 17 June 2014
Next-generation sequencing technologies have been designed to discover rare and de novo variants and are an important tool for identifying rare disease variants. Many statistical methods have been developed to test, using next-generation sequencing data, for rare variants that are associated with a trait. However, many of these methods make assumptions that rare variants are in linkage equilibrium in a gene. In this report, we studied whether transmitted or untransmitted haplotypes carry an excess of rare variants using the whole genome sequencing data of 15 large Mexican American pedigrees provided by the Genetic Analysis Workshop 18. We observed that an excess of rare variants are carried on either transmitted or nontransmitted haplotypes from parents to offspring. Further analyses suggest that such nonrandom associations among rare variants can be attributed to population admixture and single-nucleotide variant calling errors. Our results have significant implications for rare variant association studies, especially those conducted in admixed populations.
- Rare Variant
- Transmission Disequilibrium Test
- Population Admixture
- Affected Offspring
- Family Trio
Next-generation sequencing technologies have become a major tool for identifying disease-associated rare variants . Many statistical methods have been developed to test for association between rare variants and complex traits using next-generation sequencing data [2–8]. Most statistical methods for rare variant association testing either do not address rare variant calling errors or indirectly assume that rare variants are correctly called. We studied the distribution of rare variants in transmitted and untransmitted haplotypes from parents to their offspring in nuclear families using whole genome sequencing data from15 large Mexican American pedigrees provided by the Genetic Analysis Workshop 18 (GAW18). We observed an excess of rare variants falling on either transmitted or nontransmitted haplotypes from parents to offspring, suggesting linkage disequilibrium (LD) among rare variants and/or single-nucleotide variant (SNV) calling errors.
The GAW18 data includes 464 Mexican American individuals from 16 large pedigrees with half genome sequence data available. Our goal is to study the LD among rare variants using the sequencing data by comparing the number of rare variants on transmitted and untransmitted haplotypes. In our analysis, we excluded all the single-nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) >0.01. We also excluded these SNPs with (a)a missing genotyping rate >5%; (b) Hardy-Weinberg equilibrium (HWE) test p values <0.001; and (c) observed Mendelian errors.
GAW18 also provides hypertension data. We used the hypertension status provided by GAW18, which is based on blood pressure measurements at 4 study exams in the past 20 years. An individual is defined as hypertensive if the individual's systolic blood pressure (SBP)>140, or diastolic blood pressure (DBP)>90, or on antihypertensive medications at one of 4 exams and as normotensive otherwise. If an individual has missing values for all 4 exams, the individual's hypertension status is considered as missing.
We first selected family trios of "mother, father, and child" from each of the 16 large pedigrees. Among the 16 pedigrees, 1 pedigree does not include any trio who has sequencing data available. Thus, the family trios were all from 15 pedigrees. Similar to the traditional transmission disequilibrium test (TDT), we examine the transmission of a rare variant allele. Because our analysis focuses on those variants with a MAF≤1%, we expect no more than 1 parent to be heterozygous. If both parents are heterozygous for a variant, this variant is excluded from our analysis. Thus, any minor transmitted alleles to an offspring from a parent must fall on the same haplotype because of no recombination. We examine all the rare variants in a gene or a region simultaneously instead of examining 1 variant at a time. Let m be the number of trios regardless of an offspring's disease status in the data and L be the number of rare variants in a gene or a genomic region. We denote m12 to be the total number of transmitted minor alleles and m21 to be the total number of nontransmitted minor alleles across the L variants in m trios. In this way, m12 is equal to the total number of rare variants falling in transmitted haplotypes, and m21 is the total number of rare variants in nontransmitted haplotypes, respectively. If rare variants are randomly distributed in haplotypes (or, equivalently, there is no LD among rare variants), we would expect m12 =m21. We can use the TDT statistic for testing the randomness among the rare variants. The statistic T follows a chi square distribution with 1 degree of freedom (DF). When only the trios with affected offspring are included, this test will test the association between rare variants and disease status.
We applied the proposed methods to the GAW18 sequence data. After quality control, there are 2,749,275 rare SNPs remaining for association analysis. We identified 5 trios with affected offspring and 36 trios with unaffected offspring. Because the number of affected offspring trios is small, we only analyzed the unaffected offspring trios. Because some of the trios were selected from the same pedigrees, we analyzed 15 independent unaffected offspring trios. This was done by randomly selecting 1 family trio if multiple family trios were available for a pedigree. We grouped SNPs into genes or regions according to the Ensembl software (http://www.ensembl.org). As a result, we had 38,091 genes and regions. The average number of SNPs in a gene or a region was 92.
P values of the top 20 genes based on 15 nuclear families
# of rare variants transmitted
# of rare variants non transmitted
# of trios contributing most statistical evidence
# rare variants transmitted in the most contributing trios
# of rare variants nontransmitted in the most contributing trios
4.45 × 10−69
1.23 × 10−48
1.11 × 10−45
1.33 × 10−45
1.47 × 10−40
2.47 × 10−37
1.75 × 10−35
2.93 × 10−35
6.58 × 10−35
9.20 × 10−34
1.56 × 10−32
5.53 × 10−31
1.88 × 10−30
7.44 × 10−30
3.83 × 10−29
5.09 × 10−29
1.04 × 10−27
1.49 × 10−27
1.57 × 10−26
2.55 × 10−26
It has been suggested that rare variants are likely independent in general . However, our analysis suggests that substantial LD among rare variants could be introduced by population admixture. Wang and Zhu  suggested that there are substantial genotype calling errors, especially for rare and de novo variants, in whole sequencing data. But genotype calling errors are unable to explain the excess of rare variants carried by a few haplotypes in this data. When association tests for rare variants are conducted in admixed populations such as African Americans and Mexican Americans, the LD among rare variants created by population admixture can generate false-positive findings. Our results also suggest that even the TDT may not overcome this problem if multiple rare variants are analyzed together.
In summary, our analysis indicates that substantial LD among rare variants can be created by population admixture and by genotype calling errors. Novel statistical approaches for rare variant association analysis are required to account for the LD among the rare variants because of either population admixture or genotype calling errors. Family data have been suggested as having many statistical advantages in detecting rare disease variants [4, 10] and may help address these problems.
The work was supported by the National Institutes of Health, grant number HL086718 and HL053353 from National Heart, Lung, Blood Institute, HG003054 and HG005854 from the National Human Genome Research Institute. The GAW18 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The Genetic Analysis Workshop is supported by NIH grant R01 GM031575.
This article has been published as part of BMC Proceedings Volume 8 Supplement 1, 2014: Genetic Analysis Workshop 18. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcproc/supplements/8/S1. Publication charges for this supplement were funded by the Texas Biomedical Research Institute.
- A map of human genome variation from population-scale sequencing. Nature. 2010, 467: 1061-1073. 10.1038/nature09534. 1000 Genomes Project ConsortiumGoogle Scholar
- Bansal V, Libiger O, Torkamani A, Schork NJ: Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet. 2010, 11: 773-785. 10.1038/nrg2867.PubMed CentralView ArticlePubMedGoogle Scholar
- Browning SR, Browning BL: Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007, 81: 1084-1097. 10.1086/521987.PubMed CentralView ArticlePubMedGoogle Scholar
- Feng T, Elston RC, Zhu X: Detecting rare and common variants for complex traits: sibpair and odds ratio weighted sum statistics (SPWSS, ORWSS). Genet Epidemiol. 2011, 35: 398-409. 10.1002/gepi.20588.PubMed CentralView ArticlePubMedGoogle Scholar
- Han F, Pan W: A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered. 2010, 70: 42-54. 10.1159/000288704.PubMed CentralView ArticlePubMedGoogle Scholar
- Li B, Leal SM: Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008, 83: 311-321. 10.1016/j.ajhg.2008.06.024.PubMed CentralView ArticlePubMedGoogle Scholar
- Neale BM, Rivas MA, Voight BF, Altshuler D, Devlin B, Orho-Melander M, Kathiresan S, Purcell SM, Roeder K, Daly MJ: Testing for an unusual distribution of rare variants. PLoS Genet. 2011, 7: e1001322-10.1371/journal.pgen.1001322.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X: Rare variant association testing for sequencing data using the sequence kernel association test (SKAT). Am J Hum Genet. 2011, 89: 82-93. 10.1016/j.ajhg.2011.05.029.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang H, Zhu X: De novo mutations discovered in 8Mexican American families through whole genome sequencing. BMC Proc. 2014, 8 (suppl 2): S24-PubMed CentralView ArticlePubMedGoogle Scholar
- Zhu X, Feng T, Li Y, Lu Q, Elston RC: Detecting rare variants for complex traits using family and unrelated data. Genet Epidemiol. 2010, 34: 171-187. 10.1002/gepi.20449.PubMed CentralView ArticlePubMedGoogle Scholar
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