Functional linear models for region-based association analysis

Regional association analysis is one of the most powerful tools for gene mapping because instead analysis of individual variants it simultaneously considers all variants in the region. Recent development of the models for regional association analysis involves functional data analysis approach. In the framework of this approach, genotypes of variants within region as well as their effects are described by continuous functions. Such approach allows us to use information about both linkage and linkage disequilibrium and reduce the influence of noise and/or observation errors. Here we define a functional linear mixed model to test association on independent and structured samples. We demonstrate how to test fixed and random effects of a set of genetic variants in the region on quantitative trait. Estimation of statistical properties of new methods shows that type I errors are in accordance with declared values and power is high especially for models with fixed effects of genotypes. We suppose that new functional regression linear models facilitate identification of rare genetic variants controlling complex human and animal traits. New methods are implemented in computer software FREGAT which is available for free download at http://mga.bionet.nsc.ru/soft/FREGAT/.     

Genetic control of PI and GC variants in the American Mink

Genetic polymorphism of the serum α-protease inhibitor (PI) and group-specific component (GC) in minks was revealed using one-dimensional polyacrylamide gel electrophoresis and immunoblotting. Two codominant alleles were identified at each of the two loci. The data ruled out the possibility of any linkage between the PI, GC and the coat colour gene Crystal ( Cr ).     

Estimation of Allele Frequencies in Indigenous Populations of Siberia Based on Pedigree Data

Procedure is described to estimate allele frequencies in indigenous populations of Siberia using phenotype data not only for pure-blood representatives of the ethnic groups examined, but also for the descendants of mixed marriages. Implementation of the method requires reconstruction of the pedigree structure for the sample examined. Inclusion of the data on descendants of mixed marriages into the analysis increases the sample information content and decreases variance of the estimates obtained. The advantages of the method are illustrated using an example of Tundra Nentsy, for whom it was shown that variance of estimates at the analysis of the blood groups allele frequencies can be diminished approximately by a factor of 1.5.     

Inheritance of litter size at birth in the Brazilian grass mouse (Akodon cursor,Sigmodontinae, Rodentia)

By means of complex segregation analysis we studied the inheritance of litter size in two large pedigrees of captive-bred colonies of the Brazilian grass mouse Akodon cursor. Genetic analysis has revealed a highly significant influence of genetic factors on the variation of litter size (heritability, h2, was estimated as 0.44). The inheritance followed the classical polygene model: neither the major-gene model nor the polygene with unequal contribution model described the data significantly better.     

Inheritance of Oligodontia in Kerry Blue Terrier Dogs

Defects of the premolar tooth formula (oligodontia, tooth number reduction) were studied in dogs of the Kerry Blue Terrier breed. For this purpose, a database including 480 individuals of 96 litters was constructed. The occurrence of oligodontia was investigated in pedigree groups with inbred and outbred crosses. No selective mating choice for the anomaly under study was found in the sample. The results indicate that oligodontia is inherited, which requires comprehensive study of its genetic control and search for corresponding genes.     

Use of Genotypes of Common Variants for Genome-Wide Regional Association Analysis

Regional association analysis is a new statistical method which simultaneously considers all variants in a selected genome region. This method was created for the analysis of rare genetic variants, whose genotypes are determined by exome or genome sequencing. The gene is usually considered as a region. It was also proposed to use a regional analysis for testing of the association between a complex trait and a set of common variants genotyped by the panels developed for genome-wide association analysis. In this case, overlapping genome regions (sliding windows) are usually considered as a region. Since the size of such regions can be rather large, there is a risk of overestimation (inflation) of the test statistic and an increase in the type I error. In this work, the effect of the size of the region on the type I error was studied for traits with different heritability. The results of simulating experiments demonstrated that the physical size of the region but not the number of genetic variants in it is a limiting factor. The higher the trait heritability, the greater the type I error differs from the declared value. The analysis of a large number of real traits confirmed these conclusions. It is necessary to take into account these results during the interpretation of the results of regional association analysis conducted on large regions using common genetic variants.     

On the possibility of pleiotropic monogenic control of hereditary polyposis and primary cancer of the colon.

The results of the segregation analysis of hereditary adenomatous polyposis and primary cancer of the colon are given, taking into account the age-dependent manifestation of genotypes and the specificity of pedigree sampling. The results are used for testing the hypothesis of pleiotropic monogenic control of these two colon pathologies.     

Region-Based Association Test for Familial Data under Functional Linear Models

Region-based association analysis is a more powerful tool for gene mapping than testing of individual genetic variants, particularly for rare genetic variants. The most powerful methods for regional mapping are based on the functional data analysis approach, which assumes that the regional genome of an individual may be considered as a continuous stochastic function that contains information about both linkage and linkage disequilibrium. Here, we extend this powerful approach, earlier applied only to independent samples, to the samples of related individuals. To this end, we additionally include a random polygene effects in functional linear model used for testing association between quantitative traits and multiple genetic variants in the region. We compare the statistical power of different methods using Genetic Analysis Workshop 17 mini-exome family data and a wide range of simulation scenarios. Our method increases the power of regional association analysis of quantitative traits compared with burden-based and kernel-based methods for the majority of the scenarios. In addition, we estimate the statistical power of our method using regions with small number of genetic variants, and show that our method retains its advantage over burden-based and kernel-based methods in this case as well. The new method is implemented as the R-function ‘famFLM’ using two types of basis functions: the B-spline and Fourier bases. We compare the properties of the new method using models that differ from each other in the type of their function basis. The models based on the Fourier basis functions have an advantage in terms of speed and power over the models that use the B-spline basis functions and those that combine B-spline and Fourier basis functions. The ‘famFLM’ function is distributed under GPLv3 license and is freely available at http://mga.bionet.nsc.ru/soft/famFLM/.     

A genomewide screen for late-onset Alzheimer disease in a genetically isolated Dutch population

Alzheimer disease (AD) is the most common cause of dementia. We conducted a genome screen of 103 patients with late-onset AD who were ascertained as part of the Genetic Research in Isolated Populations (GRIP) program that is conducted in a recently isolated population from the southwestern area of The Netherlands. All patients and their 170 closely related relatives were genotyped using 402 microsatellite markers. Extensive genealogy information was collected, which resulted in an extremely large and complex pedigree of 4,645 members. The pedigree was split into 35 subpedigrees, to reduce the computational burden of linkage analysis. Simulations aiming to evaluate the effect of pedigree splitting on false-positive probabilities showed that a LOD score of 3.64 corresponds to 5% genomewide type I error. Multipoint analysis revealed four significant and one suggestive linkage peaks. The strongest evidence of linkage was found for chromosome 1q21 (heterogeneity LOD [HLOD]=5.20 at marker D1S498). Approximately 30 cM upstream of this locus, we found another peak at 1q25 (HLOD=4.0 at marker D1S218). These two loci are in a previously established linkage region. We also confirmed the AD locus at 10q22-24 (HLOD=4.15 at marker D10S185). There was significant evidence of linkage of AD to chromosome 3q22-24 (HLOD=4.44 at marker D3S1569). For chromosome 11q24-25, there was suggestive evidence of linkage (HLOD=3.29 at marker D11S1320). We next tested for association between cognitive function and 4,173 single-nucleotide polymorphisms in the linked regions in an independent sample consisting of 197 individuals from the GRIP region. After adjusting for multiple testing, we were able to detect significant associations for cognitive function in four of five AD-linked regions, including the new region on chromosome 3q22-24 and regions 1q25, 10q22-24, and 11q25. With use of cognitive function as an endophenotype of AD, our study indicates the that the RGSL2, RALGPS2, and C1orf49 genes are the potential disease-causing genes at 1q25. Our analysis of chromosome 10q22-24 points to the HTR7, MPHOSPH1, and CYP2C cluster. This is the first genomewide screen that showed significant linkage to chromosome 3q23 markers. For this region, our analysis identified the NMNAT3 and CLSTN2 genes. Our findings confirm linkage to chromosome 11q25. We were unable to confirm SORL1; instead, our analysis points to the OPCML and HNT genes.     

Region-Based Association Analysis of Human Quantitative Traits in Related Individuals

Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.