Genome wide association is the most widely used methodology for mapping of disease genes for complex traits. Among many limitations of this approach is the requirement for investigating large disease populations and use of sizeable number of genetic markers, which necessitates correction for multiple comparisons and is a reason for its reduced power for signal detection. These limitations have prompted alternative approaches to identify novel disease loci. One such approach is the admixture mapping, which provides a more powered alternative for gene discovery in admixed populations for a trait that is differentially distributed in the ancestral populations. One such example is African Americans (AA) population that is at increased risk for chronic and end stage renal disease, HIV-associated nephropathy (HIVAN), and focal segmental glomerulosclerosis (FSGS), which are believed to originate from their African ancestry.
In the classical sense, admixture is the result of breeding between individuals from two or more isolated populations. The consequence of the gene flow is “temporary” generation of long haplotype blocks that contains genetic variants of one or another population. The approach in admixture mapping is based on the assumption that in the admixed populations the disease- causing alleles will be more frequent on chromosome segments derived from the ancestral population with the higher prevalence for the disease. The admixture mapping associates ancestry of haplotype block with a specific trait using differences in allele frequencies of the ancestral population. The haplotype blocks become shorter with increasing age of the admixed population through higher recombination events during each meiosis. Shorter admixed linkage disequilibrium (ALD) blocks require a higher density of markers to differentiate chromosome ancestry transition. In admixed populations such as AA or Hispanics the gene flow has originated within the last several hundred years, resulting in linked alleles that show extended linkage disequilibrium relative to the ancestral populations. In AA population gametes are roughly 80% derived from African and from 20% European ancestries1. ALD in AA is in average 30-cM regions2 with statistically strongest ALD spanning a 17 cM regions, reflecting six to seven generations of admixture3. Admixture has also occurred in many other world populations such as between the Spanish and Amerindians and with considerable geographical heterogeneity.
Ancestry at any given locus, known as Local ancestry is inferred based on haplotype transitions from one parental population to another. Hidden Markov model (HMM) algorithms is used to identify markers that have a significantly greater-than-chance likelihood of being on the same segment. Admixture mapping is carried out using two distinct case-control and case only methodologies. For case-control studies locus-specific ancestry at each ancestry-informative marker (AIM) between cases and controls is calculated. The admixture mapping has the power to detect association with relatively modest odds ratios with 2,500 or fewer cases3. A moderate number (≈1,500–2,500) of AIM is initially used for gene mapping, followed by dense mapping to identify the functional allele. For case-only studies the initial mapping is carried out by comparing extent of ancestry in each locus to genome-wide average ancestry4,5. Using statistical analysis, deviation of local ancestry away from the genome-wide average will be shown as distinct peaks. Use of far fewer markers compared to GWAS and hence the requirement of modest correction for multiple comparisons is an obvious advantage of the admixture mapping over GWAS.
The major requirement for admixture mapping panels is a set of genetic markers that are informative, i.e. have allele frequencies widely different between ancestral populations (shown as delta, “δ”) and are not in linkage disequilibrium (LD) with each other. Over the last decade there has been a rapid growth in development of panels of AIMs for admixture typing6,7, and software programs and statistical methods for the analysis. Ideal is an allele that is fixed in a population (fixation index (Fst) = 1), but give the scarcity of such markers alleles with Fst > 0.5 are acceptable for the mapping. LAMP-LD is a popular software for admixture mapping in unrelated individuals and LAMP-HAP for nuclear trios. High density genome-wide admixture mapping panels have been for obvious reasons first constructed for AA Latino/Hispanics, and Uyghurs. While admixture between two populations has been modeled in most admixture studies, three-way admixture is not uncommon. Latinos/Hispanics for instance have ancestral contributions that based on their geographical distribution ranges from different Native American populations and Europeans to Africans. Like every association study, replication of the data is required in independent population.
Most recent development is the joint use of admixture mapping and association studies that combines the power of detecting disease susceptibility loci or trait loci with fewer markers and smaller study population in admixture mapping with that of an association testing that improves the resolution8. For related individuals transmission-disequilibrium test has been combined with local ancestry mapping9.
In the study by Shendre et al.10 authors examine the association of local European ancestry (LEA) with common carotid artery intima-media thickness (cCIMT) among AA using a well-defined AA population of Multi-Ethnic Study of Atherosclerosis (MESA). MESA was designed to characterize subclinical cardiovascular disease (CVD) in 45–84 years old men and women without prevalent CVD. AA comprised 28% (n=1,891). A total of 1,554 AA were genotyped using the Affymetrix Human SNP array 6.0. The details of the ultrasound method used to measure cCIMT in the MESA cohort have been described elsewhere11. Incident events had to be documented and meet established criteria that were reviewed by two physicians and determined over an average follow-up of 9.3 years from baseline. After extensive filtering, 611,449 autosomal SNPs (579,847 SNPs for the ARIC study) remained for inclusion in the admixture estimation and association analyses. In addition, 595 SNPs or ancestry informative markers (AIMs) that had been associated with cCIMT in prior studies were included. Linkage disequilibrium was used to determine LEA. HapMap phase II and III data of CEU and YRI were used as reference population and as parameters for the Hidden Markov Model (HMM) to determine the local ancestry within a 300 SNPs long window-based framework. In this method ancestry at each SNP is determined for each individual based on haplotype sets from ancestral populations using a dense set of markers. Local ancestry was graded based on the number of European ancestry alleles at each SNP and their average was used to measure global ancestry for each individual. Ancestry association with cCIMT was tested using the PLINK. Linear regression model was used after adjustment for global ancestry and CV risk factors. The total number of independent tests in the analyses was estimated to be 148.6 based on a method proposed by Shriner et al.12, that calculates the total number of effective independent tests; this resulted in a threshold significance level of 3.36 × 10−4. This threshold was considerably lower than those used for GWAS, providing considerably greater power for signal detection. LEA regions associated with cCIMT were evaluated for associations with stroke and CVD events in the MESA cohort.
The analysis identified the LEA gene region in the SERGEF gene on chromosome 11, which was also associated with higher odds of stroke as the only region that achieved genome-wide significance (β=0.0137, P=2.98×10−4). The LEA gene region in the TPH1 gene on chromosome 11 showed subthreshold association with cCIMT (P=3.42×10−4). A previously associated SNP rs2081015 in the coding region of the GALNT10 gene was reproduced in this study. Although, nominally significant regions associated with cCIMT (P<0.05) in the MESA cohort were replicated in 3000 AA from the Atherosclerosis Risk in Communities (ARIC) cohort, the genome-wide significant association of LEA with cCIMT in the SERGEF gene could not be replicated. There were a number of subthreshold associations between some of the LEA regions associated with cCIMT and clinical CVD events, i.e. protective association in relation to the cardiovascular events and opposite thereof in case of stroke. Interestingly, Regions of LEA associated with cCIMT at previously significant SNPs, i.e. rs2081015 also showed subthreshold levels of significance (β= −0.0118, P=2.45×10−3).
Genome-wide admixture association studies for cCIMT are not novel approaches. The novelty here is in the use of local ancestry association approach to potentially improve power, while reducing false discovery rate. Overall, the study was able to identify novel associations in relation to cCIMT and clinical events. None of these LEA regions, however, met the threshold of genome wide significance. Whether the signal on chromosome 11 in MESA population is a robust or false positive finding should hence be examined in future admixture mappings. The result of the study should by no means undermine the power of admixture mapping in finding disease-associated loci in admixed population. Over last decade a numberd of admixture mapping studies have led to identification of novel genetic loci for hypertension, end stage and chronic renal disease and diabetes among others (see the table). The admixture mapping also holds promise for identifying the genetic contribution to type II diabetes and obesity in Amerindians. The current study, however, reminds us all of the limitations of all currently available mapping methods and the constant need for their improvement.
Table.
Loci identified by Admixture Mapping
| Phenotype | Study Population | Chromosome | Peak SNP/Marker in the region |
|---|---|---|---|
| Hypertension | AA | 6q24.1 | |
| AA | 21q21 | ||
| AA | 6q21 | ||
| MA | 6q21 | rs2272996 | |
| AA | 6q21 | rs2272996 | |
| AA | 6q24.3 | rs2272996 | |
| ESRD | AA | 22q12 | rs482181 |
| Nondiabetic ESRD | 22q13.1 | rs482181(MYH9) | |
| HIVAN | 22q13.1 | rs482181 | |
| FSGS | 22q13.1 | rs482181 | |
| FSGS and HIVAN | AA | 22q13.1 | |
| AA | 2p14 | rs6724395 | |
| Obesity | AA | 2q23.3 | |
| BMI | AA | Xq25 | |
| AA | Xq13 | ||
| AA | 5q13.3 | ||
| AA | 3q29 | D3S1311 | |
| AA | 5q14-5q25 | D5S2501 | |
| AA | 15q26 | D15S816 | |
| Type II DM | AA | 12 | rs1565728 |
| Breast cancer(ER+PR+ vs. ER−PR−) | AA | ||
| Localized tumors | AA | ||
| White blood cell count | AA | 1q | rs2814778 |
| Peripheral arterial disease: ankle-brachial index | AA | 11 | rs9665943 |
| AA | rs9665943 | ||
| LDL-C levels | AA | 3q4q | D3S2427, D4S2367 |
| HDL-C levels | AA | 8q | D8S1136 |
| 14q | GATA193A07 | ||
| 9q | |||
| TG levels | AA | 8q15q | D8S1179D15S652 |
| Multiple sclerosis | AA | 1 | Centromeric |
| Prostate cancer | 8q24 | 3.8 Mb |
ESRD indicates end stage renal disease; HIVAN, HIV nephropathy; FSGS, focal segmental glomerulosclerosis; BMI, body mass index; ER, estrogen; PR, progesterone; HDL-C, high-density lipoprotein cholestrol; LDL-C, low-density lipoprotein cholesterol
Footnotes
Disclosures: None
References
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