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Population substructure can lead to confounding in tests for genetic association, and failure to adjust properly can result in spurious findings. Here we address this issue of confounding by considering the impact of global ancestry (average ancestry across the genome) and local ancestry (ancestry at a specific chromosomal location) on regression parameters and relative power in ancestry-adjusted and -unadjusted models. We examine theoretical expectations under different scenarios for population substructure; applying different regression models, verifying and generalizing using simulations, and exploring the findings in real-world admixed populations. We show that admixture does not lead to confounding when the trait locus is tested directly in a single admixed population. However, if there is more complex population structure or a marker locus in linkage disequilibrium (LD) with the trait locus is tested, both global and local ancestry can be confounders. Additionally, we show the genotype parameters of adjusted and unadjusted models all provide tests for LD between the marker and trait locus, but in different contexts. The local ancestry adjusted model tests for LD in the ancestral populations, while tests using the unadjusted and the global ancestry adjusted models depend on LD in the admixed population(s), which may be enriched due to different ancestral allele frequencies. Practically, this implies that global-ancestry adjustment should be used for screening, but local-ancestry adjustment may better inform fine mapping and provide better effect estimates at trait loci., (© 2017 WILEY PERIODICALS, INC.)

A multiple analytic approach may be useful for analyzing complex traits since different methods extract both similar and distinct, but complementary pieces of information from genome screen data on extended pedigrees. We examined the usefulness of combining p-values both across methods and across adjacent markers, taking into account the observed correlation structure among these p-values. To this end, we employed the recently proposed truncated product method [Zaykin et al., Genet Epidemiol, in press]. It appears that this approach is helpful for visualizing priority regions for follow-up analysis and reducing the number of false-positive linkage signals.

Once linkage is detected to a quantitative trait locus (QTL), the next step towards localizing the gene involved may be to identify those families, or individuals, in whom the putative mutations are segregating. In this paper, we describe a jackknife procedure for identifying individuals (and families) who contribute disproportionately to the linkage. Following initial detection of linkage to a QTL, the strategy involves sequentially removing each individual (or each family) from the analysis and recomputing the lod score associated with the linked region using data from all remaining subjects (or families). This procedure can be used to determine if particular observations have substantial impact on evidence for linkage. Identification of such observations may provide insights for further efforts to localize the QTL.

Complex parametric segregation and linkage analysis was performed on the simulated quantitative trait Q1 for all 200 replicates of the nuclear families data set. The segregation analysis inferred a major gene in 46% of the replicates. Among all replicates, including those that rejected a major gene, the power to detect suggestive linkage to any of three loci affecting Q1 was 0.600 and the false positive rate was 0.002. Among the replicates where a major gene was found, the power to detect suggestive linkage was 0.652 and the false positive rate was also 0.002. Thus, for purposes of linkage to this complex trait, a prior segregation then linkage analysis approach located a gene in 30% of all replicates, whereas a linkage only approach located a gene in 60% of all replicates.