Genome Screens Using Linkage Disequilibrium Tests: Optimal Marker Characteristics and Feasibility

Abstract

Linkage disequilibrium (LD) testing has become a popular and effective method of fine-scale disease-gene localization. It has been proposed that LD testing could also be used for genome screening, particularly as dense maps of diallelic markers become available and automation allows inexpensive genotyping of diallelic markers. We compare diallelic markers and multiallelic markers in terms of sample sizes required for detection of LD, by use of a single marker locus in a case-control study, for rare monophyletic diseases with Mendelian inheritance. We extrapolate from our results to discuss the feasibility of single-marker LD screening in more-complex situations. We have used a deterministic population genetic model to calculate the expected power to detect LD as a function of marker density, age of mutation, number of marker alleles, mode of inheritance of a rare disease, and sample size. Our calculations show that multiallelic markers always have more power to detect LD than do diallelic markers (under otherwise equivalent conditions) and that the ratio of the number of diallelic to the number of multiallelic markers needed for equivalent power increases with mutation age and complexity of mode of inheritance. Power equivalent to that achieved by a multiallelic screen can theoretically be achieved by use of a more dense diallelic screen, but mapping panels of the necessary resolution are not currently available and may be difficult to achieve. Genome screening that uses single-marker LD testing may therefore be feasible only for young (<20 generations), rare, monophyletic Mendelian diseases, such as may be found in rapidly growing genetic isolates.

On the advantage of haplotype analysis in the presence of multiple disease susceptibility alleles

“…For a single susceptibility allele, Akey et al [2001] reported that with equally frequent haplotypes made up of 2 or 4 biallelic sites, a chi-square test has greater power when based on haplotype than when based on individual SNPs. Their model involves recombination dynamics, but their conclusion is essentially the same as that of Chapman and Wijsman [1998], who showed that with equally frequent alleles, the power of the chi-square test is greater for a multiple allele marker than for a biallelic marker. The results of this investigation suggest that these conclusions can be extended to multiple susceptibility alleles at a disease locus.…”

“…; t at the disease locus. The case for a single susceptibility allele is given by Akey et al [2001] and Chapman and Wijsman [1998].…”

“…We consider the consequences of relaxing this assumption in the Discussion. The distribution given in Table I differs from the initial conditions employed by Chapman and Wijsman [1998, their Table 1] and Akey et al [2001] for a single susceptibililty allele. They fixed marginal marker allele frequencies at the time of sampling, whereas we fixed ancestral marker allele frequencies and allowed alternative mutational histories to influence marginal marker allele frequencies at the time of sampling, depending on how the mutation process distributed susceptibility alleles among marker alleles.…”

“…Since, for a fixed number of marker alleles, equally frequent alleles yield the most powerful test [Chapman and Wijsman, 1998], we computed PrfLRT power ! 0:80g under a uniform distribution of marker alleles.…”

Genotype-based association analysis via entropy

“…Although many LD methods have been well developed currently for complex disease genes, haplotype-based analysis is one of the major statistical methods because haplotypes of multiple single-nucleotide polymorphisms (SNPs) are considered a more informative format of polymorphisms for genetic analysis than single SNP. 1 The classical haplotype-based statistic is to compare haplotype frequencies between affected and unaffected individuals 1,2 or to compare haplotype similarities between affected and unaffected individuals. 3,4 Recently, Zhao et al 5 proposed an entropy-based statistic T PE for a genome-wide association study through a nonlinear transformation of haplotype frequencies.…”