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PubMed2006

An alternative foundation for the planning and evaluation of linkage analysis. II. Implications for multiple test adjustments.

The 'multiple testing problem' currently bedevils the field of genetic epidemiology. Briefly stated, this problem arises with the performance of more than one statistical test and results in an increased probability of committing at least one Type I error. The accepted/conventional way of dealing with this problem is based on the classical Neyman-Pearson statistical paradigm and involves adjusting one's error probabilities. This adjustment is, however, problematic because in the process of doing that, one is also adjusting one's measure of evidence. Investigators have actually become wary of looking at their data, for fear of having to adjust the strength of the evidence they observed at a given locus on the genome every time they conduct an additional test. In a companion paper in this issue (Strug & Hodge I), we presented an alternative statistical paradigm, the 'evidential paradigm', to be used when planning and evaluating linkage studies. The evidential paradigm uses the lod score as the measure of evidence (as opposed to a p value), and provides new, alternatively defined error probabilities (alternative to Type I and Type II error rates). We showed how this paradigm separates or decouples the two concepts of error probabilities and strength of the evidence. In the current paper we apply the evidential paradigm to the multiple testing problem - specifically, multiple testing in the context of linkage analysis. We advocate using the lod score as the sole measure of the strength of evidence; we then derive the corresponding probabilities of being misled by the data under different multiple testing scenarios. We distinguish two situations: performing multiple tests of a single hypothesis, vs. performing a single test of multiple hypotheses. For the first situation the probability of being misled remains small regardless of the number of times one tests the single hypothesis, as we show. For the second situation, we provide a rigorous argument outlining how replication samples themselves (analyzed in conjunction with the original sample) constitute appropriate adjustments for conducting multiple hypothesis tests on a data set.

Chromosome Mapping↗
PubMed2005

Factors affecting statistical power in the detection of genetic association.

The mapping of disease genes to specific loci has received a great deal of attention in the last decade, and many advances in therapeutics have resulted. Here we review family-based and population-based methods for association analysis. We define the factors that determine statistical power and show how study design and analysis should be designed to maximize the probability of localizing disease genes.

Data Interpretation, Statistical↗
PubMed2006

Are molecular haplotypes worth the time and expense? A cost-effective method for applying molecular haplotypes.

Because current molecular haplotyping methods are expensive and not amenable to automation, many researchers rely on statistical methods to infer haplotype pairs from multilocus genotypes, and subsequently treat these inferred haplotype pairs as observations. These procedures are prone to haplotype misclassification. We examine the effect of these misclassification errors on the false-positive rate and power for two association tests. These tests include the standard likelihood ratio test (LRTstd) and a likelihood ratio test that employs a double-sampling approach to allow for the misclassification inherent in the haplotype inference procedure (LRTae). We aim to determine the cost-benefit relationship of increasing the proportion of individuals with molecular haplotype measurements in addition to genotypes to raise the power gain of the LRTae over the LRTstd. This analysis should provide a guideline for determining the minimum number of molecular haplotypes required for desired power. Our simulations under the null hypothesis of equal haplotype frequencies in cases and controls indicate that (1) for each statistic, permutation methods maintain the correct type I error; (2) specific multilocus genotypes that are misclassified as the incorrect haplotype pair are consistently misclassified throughout each entire dataset; and (3) our simulations under the alternative hypothesis showed a significant power gain for the LRTae over the LRTstd for a subset of the parameter settings. Permutation methods should be used exclusively to determine significance for each statistic. For fixed cost, the power gain of the LRTae over the LRTstd varied depending on the relative costs of genotyping, molecular haplotyping, and phenotyping. The LRTae showed the greatest benefit over the LRTstd when the cost of phenotyping was very high relative to the cost of genotyping. This situation is likely to occur in a replication study as opposed to a whole-genome association study.

Cost-Benefit Analysis↗
PubMed1991

[Addition to the article by Iu. E. Dubrova et al. "Influence of heterozygosity on the variation of anthropometric traits in newborns." Determination of intersample differences by covariation and correlation statistics].

Relationships between heterozygosity of 13 polymorphic loci and variation of the set of anthropometric traits were studied in 302 singleton newborns (174 boys, 128 girls). Statistically significant correlation between heterozygosity and all traits were not found. Variances of four traits--body weight, body length, circumference of head and breast measured by first principal component decrease with increase of heterozygosity in boys and girls. At the same time, variance of body proportions (second principal component) increase significantly in boys only. The highest values of correlation between four traits were found in the group of low heterozygous boys. The groups of newborns with different level of heterozygosity are characterized by different combinations of first principal component and the number of minor deviations from development (stigma). It is concluded from the whole set of data that newborn boys with the average level of heterozygosity have the highest level of viability.

Anthropometry↗
PubMed2003

A note on genetic variance components in mixed models.

Burton et al. ([1999] Genet. Epidemiol. 17:118-140) proposed a series of generalized linear mixed models for pedigree data that account for residual correlation between related individuals. These models may be fitted using Markov chain Monte Carlo methods, but the posterior mean for small variance components can exhibit marked positive bias. Burton et al. ([1999] Genet. Epidemiol. 17:118-140) suggested that this problem could be overcome by allowing the variance components to take negative values. We examine this idea in depth, and show that it can be interpreted as a computational device for locating the posterior mode without necessarily implying that the original random effects structure is incorrect. We illustrate the application of this technique to mixed models for familial data.

Data Interpretation, Statistical↗
PubMed2001

Power to detect linkage based on multiple sets of data in the presence of locus heterogeneity: comparative evaluation of model-based linkage methods for affected sib pair data.

The development of rigorous methods for evaluating the overall strength of evidence for genetic linkage based on multiple sets of data is becoming increasingly important in connection with genomic screens for complex disorders. We consider here what happens when we attempt to increase power to detect linkage by pooling multiple independently collected sets of families under conditions of variable levels of locus heterogeneity across samples. We show that power can be substantially reduced in pooled samples when compared to the most informative constituent subsamples considered alone, in spite of the increased sample size afforded by pooling. We demonstrate that for affected sib pair data, a simple adaptation of the lod score (which we call the compound lod), which allows for intersample admixture differences can afford appreciably higher power than the ordinary heterogeneity lod; and also, that a statistic we have proposed elsewhere, the posterior probability of linkage, performs at least as well as the compound lod while having considerable computational advantages. The companion paper (this issue, pp 217-225) shows further that in application to multiple data sets, familiar model-free methods are in some sense equivalent to ordinary lod scores based on data pooling, and that they therefore will also suffer dramatic losses in power for pooled data in the presence of locus heterogeneity and other complicating factors.

Chromosome Mapping↗
PubMed2005

Effects of updating linkage evidence across subsets of data: reanalysis of the autism genetic resource exchange data set.

Results of autism linkage studies have been difficult to interpret across research groups, prompting the use of ever-increasing sample sizes to increase power. However, increasing sample size by pooling disparate collections for a single analysis may, in fact, not increase power in the face of genetic heterogeneity. Here, we applied the posterior probability of linkage (PPL), a method designed specifically to analyze multiple heterogeneous data sets, to the Autism Genetic Resource Exchange collection of families by analyzing six clinically defined subsets of the data and updating the PPL sequentially over the subsets. Our results indicate a substantial probability of linkage to chromosome 1, which had been previously overlooked; our findings also provide a further characterization of the possible parent-of-origin effects at the 17q11 locus that were previously described in this sample. This analysis illustrates that the way in which heterogeneity is addressed in linkage analysis can dramatically affect the overall conclusions of a linkage study.

Autistic Disorder↗
PubMed2002

Increase in power of transmission-disequilibrium tests for quantitative traits.

Allison ([1997] Am. J. Hum. Genet. 60:676-690) proposed four versions of the transmission-disequilibrium test (TDT) for quantitative traits when there is extreme-threshold sampling, i.e., the trios having an offspring trait value between a priori defined thresholds are excluded from the analysis. Keeping intact the ideology and construction of these tests, we propose here an extreme-offspring design for the trios: for each parent pair of which at least one is heterozygous at a marker locus, the offspring having the most extreme trait value is selected for the trio. Our simulation studies show that the effect of the extreme-offspring design can be quite substantial (up to 30% increase in test power), and that the increase is greater for smaller values of the association parameter and for traits with smaller heritability: just those cases where the increase in power is especially desirable.

Data Interpretation, Statistical↗
PubMed1989

Effect of framing on the perception of genetic recurrence risks.

Individuals asked to evaluate genetic recurrence risks were found to be influenced by the way the risks were framed. Presenting a single risk figure resulted in overweighting of low probabilities and underweighting of high probabilities, as compared to presenting a list of sequential risks. Differences were also found between meanings attached to verbal expressions of risks when translating from verbal to numerical expressions, and vice versa. The implications of these findings for genetic counseling are discussed.

Data Interpretation, Statistical↗
PubMed2002

Genetic diversity impacts of forest fires, forest harvesting, and alternative reforestation practices in black spruce (Picea mariana).

Benchmarks were established for genetic diversity inherent in natural mature populations, and genetic diversity impacts of forest fires, clearcut harvesting and alternative natural and artificial silvicultural regeneration practices were determined in black spruce (Picea mariana). Allozymes of 32 loci were used to determine and compare genetic diversity and genetic relationships of adjacent or nearby four stand types: post-fire natural mature (FNM), post-fire natural young (FNR), post-harvest natural young (HNR) and post-harvest plantation (PLT), of black spruce at each of the four study sites located in two ecoregions in Manitoba: Ecoregion 90-Lac Seul Upland (Eastern) and Ecoregion 158 - Mid-Boreal Lowland (Northern). Both allelic- and genotypic-based genetic diversity parameters, as well as latent genetic potential, were determined. Black spruce populations showed typical moderate to high levels of allozyme genetic diversity. The mean genetic diversity parameters over the 16 black spruce populations sampled were as follows: percent loci polymorphic - 67%, mean number of alleles per locus - 2.52, effective number of alleles per locus - 1.70, observed heterozygosity - 0.222, expected heterozygosity - 0.308, mean number of observed genotypes per locus - 3.65, mean number of expected genotypes per locus - 5.03, genotype additivity (observed) - 116.8, genotype additivity (expected) - 161, genotype multiplicity (observed) - 6.16 x 10(15), genotype multiplicity (expected) - 2.06 x 10(19) and latent genetic potential - 26.12. The four stand types (FNM, FNR, HNR and PLT) had comparable and statistically similar genetic diversity levels at each of the four study sites as well as overall. No significant differences in black spruce genetic diversity levels were observed between the two ecoregions in Manitoba, as well as between the post-fire and post-harvest regenerated stands. No particular order of genetic relatedness among the four stand types was observed. Black spruce populations showed some sort of site-related differentiation in their genetic constitution. Allelic heterogeneity and genetic distances among populations within stand types and among four stand types suggest that the genetic diversity was maintained at the landscape level in black spruce. The results of our study demonstrate that forest fires and currently used clearcut harvesting, and alternative natural and artificial silvicultural regeneration practices, do not adversely affect genetic diversity in black spruce, and that the genetic diversity effects of clearcut harvesting are not significantly different from those due to forest fires in black spruce.

Alleles↗
PubMed2005

A statistical multiprobe model for analyzing cis and trans genes in genetical genomics experiments with short-oligonucleotide arrays.

Short-oligonucleotide arrays typically contain multiple probes per gene. In genetical genomics applications a statistical model for the individual probe signals can help in separating "true" differential mRNA expression from "ghost" effects caused by polymorphisms, misdesigned probes, and batch effects. It can also help in detecting alternative splicing, start, or termination.

Animals↗
PubMed1991

A review of forty-five years study of Hiroshima and Nagasaki atomic bomb survivors. Biochemical genetics study.

After 40 years of studies on the children of atomic bomb survivors and a suitable control population in which no statistically significant genetic effects of A-bomb radiation were observed, two new projects have been initiated in order to detect mutations in the DNA and RNA molecules. Permanent cell lines from peripheral B-lymphocytes from approximately 500 families composed of exposed parents and their children and approximately 500 control families are being established by Epstein-Barr virus transformation. Cells will be sources for DNA and RNA samples in the screening for mutations. After comparison of efficiencies of the scanning techniques, we selected the denaturing gradient gel electrophoresis (DGGE) of DNA fragments amplified by polymerase chain reaction (PCR) for our purpose. A small scale pilot study has started to solve problems and obtain a better efficiency in this approach. Current thinking about the most efficient procedures is presented.

Humans↗
PubMed1990

Fisher's contributions to genetics and heredity, with special emphasis on the Gregor Mendel controversy.

R. A. Fisher is widely respected for his contributions to both statistics and genetics. For instance, his 1930 text on The Genetical Theory of Natural Selection remains a watershed contribution in that area. Fisher's subsequent research led him to study the work of (Johann) Gregor Mendel, the 19th century monk who first developed the basic principles of heredity with experiments on garden peas. In examining Mendel's original 1865 article, Fisher noted that the conformity between Mendel's reported and proposed (theoretical) ratios of segregating individuals was unusually good, "too good" perhaps. The resulting controversy as to whether Mendel "cooked" his data for presentation has continued to the current day. This review highlights Fisher's most salient points as regards Mendel's "too good" fit, within the context of Fisher's extensive contributions to the development of genetical and evolutionary theory.

Animals↗
PubMed2006

A comparison of methods for intermediate fine mapping.

The arrival of highly dense genetic maps at low cost has geared the focus of linkage analysis studies toward developing methods for placing putative trait loci in narrow regions with high confidence. This shift has led to a new analytic scheme that expands the traditional two-stage protocol of preliminary genome scan followed by fine mapping through inserting a new stage in between the two. The goal of this new "intermediate" fine mapping stage is to isolate disease loci to narrow intervals with high confidence so that association studies can be more focused, efficient, and cost-effective. In this paper, we compared and contrasted five methods that can be used for performing this intermediate step. These methods are: the lod support approach, the generalized estimating equations (GEE) method, the confidence set inference (CSI) procedure, and two bootstrap methods. We compared these methods in terms of the coverage probability and precision of localization of the resulting intervals. Results from a simulation study considering several two-locus models demonstrated that the two bootstrap methods yield intervals with approximately correct coverage. On the other hand, the 1-lod support intervals, and those produced by the GEE method, tend to significantly undercover the trait locus, while the regions obtained by the CSI incline to overcover the gene position. When the observed coverage of the confidence intervals produced by all the methods was held to be the same, those obtained through the CSI procedure displayed a higher ability to localize loci, especially when these loci have a minor contribution to the trait and when the amount of data available for the analysis is relatively small. However, with very large sample sizes, lod support intervals emerged as a winner. Application of the methods to the data from the Arthritis Research Campaign National Repository led to intervals containing the position of a known trait locus for all methods, with the greatest precision achieved by the CSI.

Arthritis, Rheumatoid↗