Design and Analysis of Admixture Mapping Studies

Abstract

Admixture between populations originating on different continents can be exploited to detect disease susceptibility loci at which risk alleles are distributed differentially between these populations. We first examine the statistical power and mapping resolution of this approach in the limiting situation in which gamete admixture and locus ancestry are measured without uncertainty. We show that, for a rare disease, the most efficient design is to study affected individuals only. In a typical African American population (two-way admixture proportions 0.8/0.2, ancestry crossover rate 2 per 100 cM), a study of 800 affected individuals has 90% power to detect at P values <10(-5) a locus that generates a risk ratio of 2 between populations, with an expected mapping resolution (size of 95% confidence region for the position of the locus) of 4 cM. In practice, to infer locus ancestry from marker data requires Bayesian computationally intensive methods, as implemented in the program ADMIXMAP. Affected-only study designs require strong prior information on the frequencies of each allele given locus ancestry. We show how data from unadmixed and admixed populations can be combined to estimate these ancestry-specific allele frequencies within the admixed population under study, allowing for variation between allele frequencies in unadmixed and admixed populations. Using simulated data based on the genetic structure of the African American population, we show that 60% of information can be extracted in a test for linkage using markers with an ancestry information content of 36% at 3-cM spacing. As in classic linkage studies, the most efficient strategy is to use markers at a moderate density for an initial genome search and then to saturate regions of putative linkage with additional markers, to extract nearly all information about locus ancestry.

Admixture as the basis for genetic mapping

“…Additional areas of Box 3. Relationship between phenotype and excess admixture Phenotypic variation among admixed individuals can be modeled as a function of excess admixture in a linear regression [28,76]. In each of the panels in Figure I, ellipses encompass data points among hybrid individuals (parental individuals are not included) in the relationship between phenotypes (arbitrary magnitude on the y axis) and excess admixture at a single locus (see Box 1).…”

“…Similarly, admixture Excess admixture quantifies the extent to which alleles and genotypes are introgressed into a foreign genetic background or are found in their native background. The contribution of each parental lineage to the genome of admixed individuals is estimated by a hybrid index (h [3,74]) or an admixture proportion [69,75,76]. In the case of parental taxa that are fully differentiated and do not share alleles, the index is simply a proportion of alleles across the genome that originate from one of the parental taxa.…”

Asociación y efectos de interacción en los genes AGT, AGTR1, ACE, ADRB2, DRD1, ADD1, ADD2, ATP2B1, TBXA2R y PTGS2 sobre la hipertensión en población antioqueña

“…Se hicieron comparaciones de promedios o medianas, entre los grupos de casos y controles para las variables Se utilizó el programa ADMIXMAP, versión 3.8, para calcular la composición genética ancestral (europea, amerindia y africana) para cada individuo, así como para la población total, la de casos y la de controles. ADMIXMAP, versión 3.8, es un programa que emplea aproximaciones clásicas y bayesianas para modelar la mezcla individual, haciendo uso de la información aportada por los genotipos para los AIM en la población híbrida en estudio, así como de la información a priori sobre la frecuencia de estos marcadores en las poblaciones parentales (66,67). Los índices de ascendencia individuales europeo, africano y amerindio obtenidos, se usaron como posibles variables de confusión, por los cuales las asociaciones fueron corregidas.…”

Genome-wide screen for asthma in Puerto Ricans: evidence for association with 5q23 region

“…The individual ancestry estimates (IAE) were calculated using two different programs, Admixmap (Hoggart et al 2003;Hoggart et al 2004) and Structure (Falush et al 2003;Pritchard et al 2000), and the 2730 AIMs. The IAE from these two programs were highly correlated (r > 0.9) (results not shown).…”