Family-Based Tests of Association in the Presence of Linkage

Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, MA 02115, USA.
The American Journal of Human Genetics (Impact Factor: 10.93). 01/2001; 67(6):1515-25. DOI: 10.1086/316895
Source: PubMed


Linkage analysis may not provide the necessary resolution for identification of the genes underlying phenotypic variation. This is especially true for gene-mapping studies that focus on complex diseases that do not exhibit Mendelian inheritance patterns. One positional genomic strategy involves application of association methodology to areas of identified linkage. Detection of association in the presence of linkage localizes the gene(s) of interest to more-refined regions in the genome than is possible through linkage analysis alone. This strategy introduces a statistical complexity when family-based association tests are used: the marker genotypes among siblings are correlated in linked regions. Ignoring this correlation will compromise the size of the statistical hypothesis test, thus clouding the interpretation of test results. We present a method for computing the expectation of a wide range of association test statistics under the null hypothesis that there is linkage but no association. To standardize the test statistic, an empirical variance-covariance estimator that is robust to the sibling marker-genotype correlation is used. This method is widely applicable: any type of phenotypic measure or family configuration can be used. For example, we analyze a deletion in the A2M gene at the 5' splice site of "exon II" of the bait region in Alzheimer disease (AD) discordant sibships. Since the A2M gene lies in a chromosomal region (chromosome 12p) that consistently has been linked to AD, association tests should be conducted under the null hypothesis that there is linkage but no association.

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    • "Family-based association analysis was performed using the Transmission Disequilibrium Test (TDT), as implemented in the FBAT software, version 2.0.2 (Horvath et al. 2001). We applied the empirical variance (-e) function to allow for association testing in the presence of linkage, an appropriated approach when multiplex families are used (Lake et al. 2000). Deviations from Hardy–Weinberg equilibrium and linkage disequilibrium (LD) estimations (Prata Village) were performed using the Haploview software, version 4.2 (Barrett et al. 2005). "
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    ABSTRACT: Leprosy is a complex disease with phenotypes strongly influenced by genetic variation. A Chinese genome-wide association study (GWAS) depicted novel genes and pathways associated with leprosy susceptibility, only partially replicated by independent studies in different ethnicities. Here, we describe the results of a validation and replication study of the Chinese GWAS in Brazilians, using a stepwise strategy that involved two family-based and three independent case-control samples, resulting in 3,614 individuals enrolled. First, we genotyped a family-based sample for 36 tag single-nucleotide polymorphisms (SNPs) of five genes located in four different candidate loci: CCDC122-LACC1, NOD2, TNFSF15 and RIPK2. Association between leprosy and tag SNPs at NOD2 (rs8057431) and CCDC122-LACC1 (rs4942254) was then replicated in three additional, independent samples (combined ORAA = 0.49, P = 1.39e-06; ORCC = 0.72, P = 0.003, respectively). These results clearly implicate the NOD2 pathway in the regulation of leprosy susceptibility across diverse populations.
    Human Genetics 11/2014; 133(12). DOI:10.1007/s00439-014-1502-9 · 4.82 Impact Factor
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    • "Most GWAS are based on unrelated subjects, particularly case–control studies in which allele frequencies in the affected subjects are compared to those in the unaffected. An alternative is to utilise information from the family structure of the samples and measure the difference in transmission and nontransmission frequencies of minor alleles to the affected subjects from their parents (Spielman et al., 1993; Lake et al., 2000). Family-based analyses have a number of advantages including controlling for population stratification and offering allele enrichment (Ott et al., 2011). "
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    ABSTRACT: Rare genetic variants are thought to be important components in the causality of many diseases but discovering these associations is challenging. We demonstrate how best to use family-based designs to improve the power to detect rare variant disease associations. We show that using genetic data from enriched families (those pedigrees with greater than one affected member) increases the power and sensitivity of existing case-control rare variant tests. However, we show that transmission- (or within-family-) based tests do not benefit from this enrichment. This means that, in studies where a limited amount of genotyping is available, choosing a single case from each of many pedigrees has greater power than selecting multiple cases from fewer pedigrees. Finally, we show how a pseudo-case-control design allows a greater range of statistical tests to be applied to family data.
    Annals of Human Genetics 03/2014; 78(2):129-40. DOI:10.1111/ahg.12051 · 2.21 Impact Factor
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    • "Note that while genotypic data of the patient were available only for a subset of the families (371/685 ¼ 54%), all families have at least one member who is diagnosed with non-affective psychosis. The statistical software package FBAT was used to perform family based association tests [Lake et al., 2000]. First, we tested the association of the 10 SNPs with disease status as a categorical trait. "
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