Eric M. Sobel

Identification of the gene altered in Berardinelli-Seip congenital lipodystrophy on chromosome 11q13.

Congenital generalized lipodystrophy, or Berardinelli–Seip syndrome (BSCL), is a rare autosomal recessive disease characterized by a near-absence of adipose tissue from birth or early infancy and severe insulin resistance. Other clinical and biological features include acanthosis nigricans, hyperandrogenism, muscular hypertrophy, hepatomegaly, altered glucose tolerance or diabetes mellitus, and hypertriglyceridemia. A locus (BSCL1) has been mapped to 9q34 with evidence of heterogeneity. Here, we report a genome screen of nine BSCL families from two geographical clusters (in Lebanon and Norway). We identified a new disease locus, designated BSCL2, within the 2.5-Mb interval flanked by markers D11S4076 and D11S480 on chromosome 11q13. Analysis of 20 additional families of various ethnic origins led to the identification of 11 families in which the disease cosegregates with the 11q13 locus; the remaining families provide confirmation of linkage to 9q34. Sequence analysis of genes located in the 11q13 interval disclosed mutations in a gene homologous to the murine guanine nucleotide-binding protein (G protein), γ3-linked gene (Gng3lg) in all BSCL2-linked families. BSCL2 is most highly expressed in brain and testis and encodes a protein (which we have called seipin) of unknown function. Most of the variants are null mutations and probably result in a severe disruption of the protein. These findings are of general importance for understanding the molecular mechanisms underlying regulation of body fat distribution and insulin resistance.

Genome-wide association analysis by lasso penalized logistic regression

MOTIVATION In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations. METHOD The present article evaluates the performance of lasso penalized logistic regression in case-control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression. RESULTS This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs. AVAILABILITY The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Detection and Integration of Genotyping Errors in Statistical Genetics

Detection of genotyping errors and integration of such errors in statistical analysis are relatively neglected topics, given their importance in gene mapping. A few inopportunely placed errors, if ignored, can tremendously affect evidence for linkage. The present study takes a fresh look at the calculation of pedigree likelihoods in the presence of genotyping error. To accommodate genotyping error, we present extensions to the Lander-Green-Kruglyak deterministic algorithm for small pedigrees and to the Markov-chain Monte Carlo stochastic algorithm for large pedigrees. These extensions can accommodate a variety of error models and refrain from simplifying assumptions, such as allowing, at most, one error per pedigree. In principle, almost any statistical genetic analysis can be performed taking errors into account, without actually correcting or deleting suspect genotypes. Three examples illustrate the possibilities. These examples make use of the full pedigree data, multiple linked markers, and a prior error model. The first example is the estimation of genotyping error rates from pedigree data. The second-and currently most useful-example is the computation of posterior mistyping probabilities. These probabilities cover both Mendelian-consistent and Mendelian-inconsistent errors. The third example is the selection of the true pedigree structure connecting a group of people from among several competing pedigree structures. Paternity testing and twin zygosity testing are typical applications.

The genetic architecture of Down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies

Down syndrome (DS), or trisomy 21, is a common disorder associated with several complex clinical phenotypes. Although several hypotheses have been put forward, it is unclear as to whether particular gene loci on chromosome 21 (HSA21) are sufficient to cause DS and its associated features. Here we present a high-resolution genetic map of DS phenotypes based on an analysis of 30 subjects carrying rare segmental trisomies of various regions of HSA21. By using state-of-the-art genomics technologies we mapped segmental trisomies at exon-level resolution and identified discrete regions of 1.8–16.3 Mb likely to be involved in the development of 8 DS phenotypes, 4 of which are congenital malformations, including acute megakaryocytic leukemia, transient myeloproliferative disorder, Hirschsprung disease, duodenal stenosis, imperforate anus, severe mental retardation, DS-Alzheimer Disease, and DS-specific congenital heart disease (DSCHD). Our DS-phenotypic maps located DSCHD to a <2-Mb interval. Furthermore, the map enabled us to present evidence against the necessary involvement of other loci as well as specific hypotheses that have been put forward in relation to the etiology of DS—i.e., the presence of a single DS consensus region and the sufficiency of DSCR1 and DYRK1A, or APP, in causing several severe DS phenotypes. Our study demonstrates the value of combining advanced genomics with cohorts of rare patients for studying DS, a prototype for the role of copy-number variation in complex disease.

Multipoint Estimation of Identity-by-Descent Probabilities at Arbitrary Positions among Marker Loci on General Pedigrees

Objectives: To describe, implement, and test an efficient algorithm to obtain multipoint identity-by-descent (IBD) probabilities at arbitrary positions among marker loci for general pedigrees. Unlike existing programs, our algorithm can analyze data sets with large numbers of people and markers. The algorithm has been implemented in the SimWalk2 computer package. Methods: Using a rigorous testing regimen containing five pedigrees of various sizes with realistic marker data, we compared several widely used IBD computation programs: Allegro, Aspex, GeneHunter, MapMaker/Sibs, Mendel, Sage, SimWalk2, and Solar. Results: The testing revealed a few discrepancies, particularly on consanguineous pedigrees, but overall excellent results in the deterministic multipoint packages. SimWalk2 was also found to be in good agreement with the deterministic multipoint programs, usually matching to two decimal places the kinship coefficient that ranges from 0 to 1. However, the packages based on single-point IBD estimation, while consistent with each other, often showed poor results, disagreeing with the multipoint kinship results by as much as 0.5. Conclusions: Our testing has clearly shown that multipoint IBD estimation is much better than single-point estimation. In addition, our testing has validated our algorithm for estimating IBD probabilities at arbitrary positions on general pedigrees.

A Susceptibility Locus for Migraine with Aura, on Chromosome 4q24

Migraine is a complex neurovascular disorder with substantial evidence supporting a genetic contribution. Prior attempts to localize susceptibility loci for common forms of migraine have not produced conclusive evidence of linkage or association. To date, no genomewide screen for migraine has been published. We report results from a genomewide screen of 50 multigenerational, clinically well-defined Finnish families showing intergenerational transmission of migraine with aura (MA). The families were screened using 350 polymorphic microsatellite markers, with an average intermarker distance of 11 cM. Significant evidence of linkage was found between the MA phenotype and marker D4S1647 on 4q24. Using parametric two-point linkage analysis and assuming a dominant mode of inheritance, we found for this marker a maximum LOD score of 4.20 under locus homogeneity (P=.000006) or locus heterogeneity (P=.000011). Multipoint parametric (HLOD = 4.45; P=.0000058) and nonparametric (NPL(all) = 3.43; P=.0007) analyses support linkage in this region. Statistically significant linkage was not observed in any other chromosomal region.

A multiple sequence alignment program

A program is described for simultaneously aligning two or more molecular sequences which is based on first finding common segments above a specified length and then piecing these together to maximize an alignment scoring function. Optimal as well as near-optimal alignments are found, and there is also provided a means for randomizing the given sequences for testing the statistical significance of an alignment. Alignments may be made in the original alphabets of the sequences or in user-specified alternate ones to take advantage of chemical similarities (such as hydrophobic-hydrophilic).

Mendel: the Swiss army knife of genetic analysis programs

UNLABELLED Mendel is one of the few statistical genetics packages that provide a full spectrum of gene mapping methods, ranging from parametric linkage in large pedigrees to genome-wide association with rare variants. Our latest additions to Mendel anticipate and respond to the needs of the genetics community. Compared with earlier versions, Mendel is faster and easier to use and has a wider range of applications. Supported platforms include Linux, MacOS and Windows. AVAILABILITY Free from www.genetics.ucla.edu/software/mendel.

WW-domain-containing oxidoreductase is associated with low plasma HDL-C levels.

Low serum HDL-cholesterol (HDL-C) is a major risk factor for coronary artery disease. We performed targeted genotyping of a 12.4 Mb linked region on 16q to test for association with low HDL-C by using a regional-tag SNP strategy. We identified one SNP, rs2548861, in the WW-domain-containing oxidoreductase (WWOX) gene with region-wide significance for low HDL-C in dyslipidemic families of Mexican and European descent and in low-HDL-C cases and controls of European descent (p = 6.9 x 10(-7)). We extended our investigation to the population level by using two independent unascertained population-based Finnish cohorts, the cross-sectional METSIM cohort of 4,463 males and the prospective Young Finns cohort of 2,265 subjects. The combined analysis provided p = 4 x 10(-4) to 2 x 10(-5). Importantly, in the prospective cohort, we observed a significant longitudinal association of rs2548861 with HDL-C levels obtained at four different time points over 21 years (p = 0.003), and the T risk allele explained 1.5% of the variance in HDL-C levels. The rs2548861 resides in a highly conserved region in intron 8 of WWOX. Results from our in vitro reporter assay and electrophoretic mobility-shift assay demonstrate that this region functions as a cis-regulatory element whose associated rs2548861 SNP has a specific allelic effect and that the region forms an allele-specific DNA-nuclear-factor complex. In conclusion, analyses of 9,798 subjects show significant association between HDL-C and a WWOX variant with an allele-specific cis-regulatory function.

Linkage Analysis without Defined Pedigrees

The need to collect accurate and complete pedigree information has been a drawback of family‐based linkage and association studies. Even in case‐control studies, investigators should be aware of, and condition on, familial relationships. In single nucleotide polymorphism (SNP) genome scans, relatedness can be directly inferred from the genetic data rather than determined through interviews. Various methods of estimating relatedness have previously been implemented, most notably in PLINK. We present new fast and accurate algorithms for estimating global and local kinship coefficients from dense SNP genotypes. These algorithms require only a single pass through the SNP genotype data. We also show that these estimates can be used to cluster individuals into pedigrees. With these estimates in hand, quantitative trait locus linkage analysis proceeds via traditional variance components methods without any prior relationship information. We demonstrate the success of our algorithms on simulated and real data sets. Our procedures make linkage analysis as easy as a typical genomewide association study. Genet. Epidemiol. 2011.