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), gamma3-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.

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.

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Purpose: We investigated the value of transcriptome sequencing (RNAseq) in ascertaining the consequence of DNA variants on RNA transcripts to improve the diagnostic rate from exome or genome sequencing for undiagnosed Mendelian diseases spanning a wide spectrum of clinical indications.Methods: From 234 subjects referred to the Undiagnosed Diseases Network, University of California-Los Angeles clinical site between July 2014 and August 2018, 113 were enrolled for high likelihood of having rare undiagnosed, suspected genetic conditions despite thorough prior clinical evaluation. Exome or genome sequencing and RNAseq were performed, and RNAseq data was integrated with genome sequencing data for DNA variant interpretation genome-wide. Results:The molecular diagnostic rate by exome or genome sequencing was 31%. Integration of RNAseq with genome sequencing resulted in an additional seven cases with clear diagnosis of a known genetic disease. Thus, the overall molecular diagnostic rate was 38%, and 18% of all genetic diagnoses returned required RNAseq to determine variant causality. Conclusion:In this rare disease cohort with a wide spectrum of undiagnosed, suspected genetic conditions, RNAseq analysis increased the molecular diagnostic rate above that possible with genome sequencing analysis alone even without availability of the most appropriate tissue type to assess.