Determining trait locus position from multipoint analysis: accuracy and power of three different statistics.

Previous work using two-point linkage analysis showed that performing a lod score (LOD) analysis twice, once assuming dominant and once assuming recessive inheritance, and then taking the larger of the two values (designated MMLS) usually has more power to detect linkage than any other method tested. Using computer simulation for a variety of complex inheritance models, we demonstrated power for the MMLS comparable with analysis assuming the true model. However, reports in the literature suggested that the MMLS approach might fail to detect linkage using multipoint analysis due to genetic model misspecification. Here, we tested the robustness of the MMLS approach under multipoint analysis. We simulated data under complex inheritance models, including heterogeneity, epistatic, and additive models. We examined the expected maximum LOD, LOD assuming heterogeneity (HLOD), and nonparametric linkage statistics and the corresponding estimated position in a chromosomal interval of 10 markers with 10% recombination between markers. The mean estimates of position were generally good for all three statistics except when heterogeneity existed, where the LOD and the NPL did not perform as well as the HLOD. The MMLS approach was as robust using multipoint as using two-point linkage analysis. LOD and/or the HLOD generally had more power to detect linkage than NPL across a variety of generating models, even after correcting for the multiple tests. For finding linkage to one locus of several contributing to disease expression, assuming the dominant and recessive models with reduced penetrance is a good approximation of the mode of inheritance at that locus.

Greenberg DA ，Abreu PC 《GENETIC EPIDEMIOLOGY》

A study comparing precision of the maximum multipoint heterogeneity LOD statistic to three model-free multipoint linkage methods.

This study compared the performance of the maximum lod (MLOD), maximum heterogeneity lod (MHLOD), maximum non-parametric linkage score (MNPL), maximum Kong and Cox linear extension (MKC(lin)) of NPL, and maximum Kong and Cox exponential extension (MKC(exp)) of NPL as calculated in Genehunter 1.2 and Genehunter-Plus. Our performance measure was the distance between the marker with maximum value for each linkage statistic and the trait locus. We performed a simulation study considering: 1) four modes of transmission, 2) 100 replicates for each model, 3) 58 pedigrees (with 592 subjects) per replicate, 4) three linked marker loci each having three equally frequent alleles, and 5) either 0% unlinked families (linkage homogeneity) or 50% unlinked families (linkage heterogeneity). For each replicate, we obtained the Haldane map position of the location at which each of the five statistics is maximized. The MLOD and MHLOD were obtained by maximizing over penetrances, phenocopy rate, and risk-allele frequencies. For the models simulated, MHLOD appeared to be the best statistic both in terms of identifying a marker locus having the smallest mean distance from the trait locus and in terms of the strongest negative correlation between maximum linkage statistic and distance of the identified position and the trait locus. The marker loci with maximum value of the Kong and Cox extensions of the NPL statistic also were closer to the trait locus than the marker locus with maximum value of the NPL statistic.

Finch SJ ，Chen CH ，Gordon D ，Mendell NR ... -  《GENETIC EPIDEMIOLOGY》

Effect of heterogeneity and assumed mode of inheritance on lod scores.

Heterogeneity is a major factor in many common, complex diseases and can confound linkage analysis. Using computer-simulated heterogeneous data we tested what effect unlinked families have on a linkage analysis when heterogeneity is not taken into account. We created 60 data sets of 40 nuclear families each with different proportions of linked and unlinked families and with different modes of inheritance. The ascertainment probability was 0.05, the disease had a penetrance of 0.6, and the recombination fraction for the linked families was zero. For the analysis we used a variety of assumed modes of inheritance and penetrances. Under these conditions we looked at the effect of the unlinked families on the lod score, the evaluation of the mode of inheritance, and the estimate of penetrance and of the recombination fraction in the linked families. 1. When the analysis was done under the correct mode of inheritance for the linked families, we found that the mode of inheritance of the unlinked families had minimal influence on the highest maximum lod score (MMLS) (i.e., we maximized the maximum lod score with respect to penetrance). Adding sporadic families decreased the MMLS less than adding recessive or dominant unlinked families. 2. The mixtures of dominant linked families with unlinked families always led to a higher MMLS when analyzed under the correct (dominant) mode of inheritance than when analyzed under the incorrect mode of inheritance. In the mixtures with recessive linked families, assuming the correct mode of inheritance generally led to a higher MMLS, but we observed broad variation.(ABSTRACT TRUNCATED AT 250 WORDS)

Durner M ，Greenberg DA 《american journal of medical genetics》

Inferring mode of inheritance by comparison of lod scores.

One usually must assume a mode of inheritance when using lod scores for linkage analysis. In this study, we asked the question, "If one assumed mode of inheritance in a linkage analysis gives a higher lod score than another, does that indicate that the mode of inheritance that led to the higher lod score is more 'correct' than the other?" We simulated data under a variety of penetrances, assuming either dominant or recessive inheritance. We then analyzed those simulated data under the correct mode of inheritance, assuming a range of penetrance values, and under the incorrect model, also assuming a range of penetrance values. We found that, if there was enough information for a maximum lod score of at least 3.0, assuming the correct penetrance value or mode of inheritance in the analysis led to a higher lod score than assuming the incorrect penetrance or the incorrect mode of inheritance. These results cannot yet be generalized outside of the specific modes of inheritance and penetrance combinations that we have modeled. Also, penetrance was modeled as "random." The effect of "reduced penetrance" caused by other genetic factors has not yet been tested. We also tested the effect of non-standard ascertainment on drawing conclusions about mode of inheritance from linkage data. Even when families were ascertained only if the family was multiplex (i.e., more than one affected sib), assuming the correct mode of inheritance gave a higher lod score than assuming the incorrect mode of inheritance. This method has the promise of both simplifying and expanding the application of linkage analysis.(ABSTRACT TRUNCATED AT 250 WORDS)

Greenberg DA 《american journal of medical genetics》

Direct power comparisons between simple LOD scores and NPL scores for linkage analysis in complex diseases.

Several methods have been proposed for linkage analysis of complex traits with unknown mode of inheritance. These methods include the LOD score maximized over disease models (MMLS) and the "nonparametric" linkage (NPL) statistic. In previous work, we evaluated the increase of type I error when maximizing over two or more genetic models, and we compared the power of MMLS to detect linkage, in a number of complex modes of inheritance, with analysis assuming the true model. In the present study, we compare MMLS and NPL directly. We simulated 100 data sets with 20 families each, using 26 generating models: (1) 4 intermediate models (penetrance of heterozygote between that of the two homozygotes); (2) 6 two-locus additive models; and (3) 16 two-locus heterogeneity models (admixture alpha = 1.0,.7,.5, and.3; alpha = 1.0 replicates simple Mendelian models). For LOD scores, we assumed dominant and recessive inheritance with 50% penetrance. We took the higher of the two maximum LOD scores and subtracted 0.3 to correct for multiple tests (MMLS-C). We compared expected maximum LOD scores and power, using MMLS-C and NPL as well as the true model. Since NPL uses only the affected family members, we also performed an affecteds-only analysis using MMLS-C. The MMLS-C was both uniformly more powerful than NPL for most cases we examined, except when linkage information was low, and close to the results for the true model under locus heterogeneity. We still found better power for the MMLS-C compared with NPL in affecteds-only analysis. The results show that use of two simple modes of inheritance at a fixed penetrance can have more power than NPL when the trait mode of inheritance is complex and when there is heterogeneity in the data set.

Abreu PC ，Greenberg DA ，Hodge SE 《-》