Stacey S. Cherny

Merlin — Rapid analysis of dense genetic maps using sparse gene flow trees

Efforts to find disease genes using high-density single-nucleotide polymorphism (SNP) maps will produce data sets that exceed the limitations of current computational tools. Here we describe a new, efficient method for the analysis of dense genetic maps in pedigree data that provides extremely fast solutions to common problems such as allele-sharing analyses and haplotyping. We show that sparse binary trees represent patterns of gene flow in general pedigrees in a parsimonious manner, and derive a family of related algorithms for pedigree traversal. With these trees, exact likelihood calculations can be carried out efficiently for single markers or for multiple linked markers. Using an approximate multipoint calculation that ignores the unlikely possibility of a large number of recombinants further improves speed and provides accurate solutions in dense maps with thousands of markers. Our multipoint engine for rapid likelihood inference (Merlin) is a computer program that uses sparse inheritance trees for pedigree analysis; it performs rapid haplotyping, genotype error detection and affected pair linkage analyses and can handle more markers than other pedigree analysis packages.

Genetic power calculator: Design of linkage and association genetic mapping studies of complex traits

SUMMARY A website for performing power calculations for the design of linkage and association genetic mapping studies of complex traits. AVAILABILITY The package is made available athttp://statgen.iop.kcl.ac.uk/gpc/.

Combined Linkage and Association Sib-Pair Analysis for Quantitative Traits

An extension to current maximum-likelihood variance-components procedures for mapping quantitative-trait loci in sib pairs that allows a simultaneous test of allelic association is proposed. The method involves modeling of the allelic means for a test of association, with simultaneous modeling of the sib-pair covariance structure for a test of linkage. By partitioning of the mean effect of a locus into between- and within-sibship components, the method controls for spurious associations due to population stratification and admixture. The power and efficacy of the method are illustrated through simulation of various models of both real and spurious association.

GRR: graphical representation of relationship errors

SUMMARY A graphical tool for verifying assumed relationships between individuals in genetic studies is described. GRR can detect many common errors using genotypes from many markers. AVAILABILITY GRR is available at http://bioinformatics.well.ox.ac.uk/GRR.

Diabetes Prevalence and Therapeutic Target Achievement in the United States, 1999 to 2006

OBJECTIVE Changes in the prevalence, treatment, and management of diabetes in the United States from 1999 to 2006 were studied using data from the National Health and Nutrition Examination Survey. METHODS Data on 17,306 participants aged 20 years or more were analyzed. Glycemic, blood pressure, and cholesterol targets were glycosylated hemoglobin less than 7.0%, blood pressure less than 130/80 mm Hg, and low-density lipoprotein (LDL) cholesterol less than 100 mg/dL, respectively. RESULTS The prevalence of diagnosed diabetes was 6.5% from 1999 to 2002 and 7.8% from 2003 to 2006 (P < .05) and increased significantly in women, non-Hispanic whites, and obese people. Although there were no significant changes in the pattern of antidiabetic treatment, the age-adjusted percentage of people with diagnosed diabetes achieving glycemic and LDL targets increased from 43.1% to 57.1% (P < .05) and from 36.1% to 46.5% (P < .05), respectively. Glycosylated hemoglobin decreased from 7.62% to 7.15% during this period (P < .05). The age-adjusted percentage achieving all 3 targets increased insignificantly from 7.0% to 12.2%. CONCLUSIONS The prevalence of diagnosed diabetes increased significantly from 1999 to 2006. The proportion of people with diagnosed diabetes achieving glycemic and LDL targets also increased. However, there is a need to achieve glycemic, blood pressure, and LDL targets simultaneously.

Powerful regression-based quantitative-trait linkage analysis of general pedigrees

We present a new method of quantitative-trait linkage analysis that combines the simplicity and robustness of regression-based methods and the generality and greater power of variance-components models. The new method is based on a regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method and have performed simulation studies to assess, under a range of conditions, estimation accuracy, type I error rate, and power. For normally distributed traits and in large samples, the method is found to give the correct type I error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus-although, in cases where asymptotic theory is doubtful, significance levels should be checked by simulations. In large sibships, the new method is slightly more powerful than variance-components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.

Linkage between sexual orientation and chromosome Xq28 in males but not in females

We have extended our analysis of the role of the long arm of the X chromosome (Xq28) in sexual orientation by DNA linkage analyses of two newly ascertained series of families that contained either two gay brothers or two lesbian sisters as well as heterosexual siblings. Linkage between the Xq28 markers and sexual orientation was detected for the gay male families but not for the lesbian families or for families that failed to meet defined inclusion criteria for the study of sex–linked sexual orientation. Our results corroborate the previously reported linkage between Xq28 and male homosexuality in selected kinships and suggest that this region contains a locus that influences individual variations in sexual orientation in men but not in women.

Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases

Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of “missing heritability” has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of “missing heritability” for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus‐specific heritability. The method is extended to deal with haplotypes, multi‐allelic markers, multi‐locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimers disease, bipolar disorder, breast cancer, coronary artery disease, Crohns disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at http://sites.google.com/site/honcheongso/software/varexp. Genet. Epidemiol. 2011.

High-resolution mapping of quantitative trait loci in outbred mice

Screening the whole genome of a cross between two inbred animal strains has proved to be a powerful method for detecting genetic loci underlying quantitative behavioural traits, but the level of resolution offered by quantitative trait loci (QTL) mapping is still too coarse to permit molecular cloning of the genetic determinants. To achieve high-resolution mapping, we used an outbred stock of mice for which the entire genealogy is known. The heterogeneous stock (HS) was established 30 years ago from an eight-way cross of C57BL/6, BALB/c, RIII, AKR, DBA/2, I, A/J and C3H inbred mouse strains. At the time of the experiment reported here, the HS mice were at generation 58, theoretically offering at least a 30-fold increase in resolution for QTL mapping compared with a backcross or an F2 intercross. Using the HS mice we have mapped a QTL influencing a psychological trait in mice to a 0.8-cM interval on chromosome 1. This method allows simultaneous fine mapping of multiple QTLs, as shown by our report of a second QTL on chromosome 12. The high resolution possible with this approach makes QTLs accessible to positional cloning.

An improved multipoint sib-pair analysis of quantitative traits

Kruglyak and Lander (1995) recently published a multipoint sib-pair procedure based on the expected distribution of zero, one and two marker alleles shared identical by descent (ibd) and the method of maximum-likelihood (ML). Their approach uses phenotypic sib-pair differences, which ignores the bivariate structure of sib-pair data. Their simulations suggested that their method was more powerful than the regression method of Haseman and Elston (1972). We show through computation and simulation that their approach can be made more powerful still if the bivariate nature of sib-pair data is acknowledged. In addition, methods based on the average number of shared alleles that also employ bivariate ML procedures (Nance and Neale, 1989; Xu and Atchley, 1995) are more powerful than the approach they recommend and very similar to true ML using the distribution of ibd. The simple ML approach using the average number of shared alleles that we recommend seems to offer both optimal power and flexibility.