Association testing by haplotype-sharing methods applicable to whole-genome analysis

We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci

Microarray amplification bias: loss of 30% differentially expressed genes due to long probe – poly(A)-tail distances

BACKGROUND: Laser microdissection microscopy has become a rising tool to assess gene expression profiles of pure cell populations. Given the low yield of RNA, a second round of amplification is usually mandatory to yield sufficient amplified-RNA for microarray approaches. Since amplification induces truncation of RNA molecules, we studied the impact of a second round of amplification on identification of differentially expressed genes in relation to the probe - poly(A)-tail distances. RESULTS: Disagreement was observed between gene expression profiles acquired after a second round of amplification compared to a single round. Thirty percent of the differentially expressed genes identified after one round of amplification were not detected after two rounds. These inconsistent genes have a significant longer probe - poly(A)-tail distance. qRT-PCR on unamplified RNA confirmed differential expression of genes with a probe - poly(A)-tail distance >500 nucleotides appearing only after one round of amplification. CONCLUSION: Our data demonstrate a marked loss of 30% of truly differentially expressed genes after a second round of amplification. Therefore, we strongly recommend improvement of amplification procedures and importance of microarray probe design to allow detection of all differentially expressed genes in case of limited amounts of RNA

NIPTeR:an R package for fast and accurate trisomy prediction in non-invasive prenatal testing

BACKGROUND: Various algorithms have been developed to predict fetal trisomies using cell-free DNA in non-invasive prenatal testing (NIPT). As basis for prediction, a control group of non-trisomy samples is needed. Prediction accuracy is dependent on the characteristics of this group and can be improved by reducing variability between samples and by ensuring the control group is representative for the sample analyzed.RESULTS: NIPTeR is an open-source R Package that enables fast NIPT analysis and simple but flexible workflow creation, including variation reduction, trisomy prediction algorithms and quality control. This broad range of functions allows users to account for variability in NIPT data, calculate control group statistics and predict the presence of trisomies.CONCLUSION: NIPTeR supports laboratories processing next-generation sequencing data for NIPT in assessing data quality and determining whether a fetal trisomy is present. NIPTeR is available under the GNU LGPL v3 license and can be freely downloaded from https://github.com/molgenis/NIPTeR or CRAN.</p

CAG Repeat Size Influences the Progression Rate of Spinocerebellar Ataxia Type 3

Objective: In spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD), the expanded cytosine adenine guanine (CAG) repeat in ATXN3 is the causal mutation, and its length is the main factor in determining the age at onset (AO) of clinical symptoms. However, the contribution of the expanded CAG repeat length to the rate of disease progression after onset has remained a matter of debate, even though an understanding of this factor is crucial for experimental data on disease modifiers and their translation to clinical trials and their design. Methods: Eighty-two Dutch patients with SCA3/MJD were evaluated annually for 15 years using the International Cooperative Ataxia Rating Scale (ICARS). Using linear growth curve models, ICARS progression rates were calculated and tested for their relation to the length of the CAG repeat expansion and to the residual age at onset (RAO): The difference between the observed AO and the AO predicted on the basis of the CAG repeat length. Results: On average, ICARS scores increased 2.57 points/year of disease. The length of the CAG repeat was positively correlated with a more rapid ICARS progression, explaining 30% of the differences between patients. Combining both the length of the CAG repeat and RAO as comodifiers explained up to 47% of the interpatient variation in ICARS progression. Interpretation: Our data imply that the length of the expanded CAG repeat in ATXN3 is a major determinant of clinical decline, which suggests that CAG-dependent molecular mechanisms similar to those responsible for disease onset also contribute to the rate of disease progression in SCA3/MJD. ANN NEUROL 2020

Comparison of the chromosomal pattern of primary testicular nonseminomas and residual mature teratomas after chemotherapy

About 70 to 75% of patients with nonseminomatous testicular germ cell tumors (NSs) present with metastases. When these metastases are treated with chemotherapy, often residual mature teratoma (RMT) is left. RMT is composed of fully differentiated somatic tissue. Untreated metastases of NSs rarely consist exclusively of mature somatic tissue. Apparently, after chemotherapy treatment there is a shift towards higher degrees of differentiation. Investigating tumor progression and the mechanism(s) involved in therapy-related differentiation, we compared the cytogenetically abnormal karyotypes of a series of 70 NSs with those of 31 RMTs. In NSs and RMTs, the modal total chromosome number does not differ and is in the triploid range. Both the frequency and the average copy number of i(12p) are the same, and the pattern of chromosomal over-and underrepresentation and distribution of breakpoints do not differ significantly in these series. So, we found the chromosomal pattern of RMTs as abnormal as those of primary NSs. Based on cytogenetics, we found no indication that specific chromosomal alterations parallel metastasis and therapy-related differentiation of the metastases. The cytogenetic data suggest that both induction of differentiation of (selected) cells or selection of cells with capacity to differentiate are possible mechanisms for the therapy-related differentiation of RMTs. (C) Elsevier Science Inc., 1997

A biological question and a balanced (orthogonal) design: the ingredients to efficiently analyze two-color microarrays with Confirmatory Factor Analysis

BACKGROUND: Factor analysis (FA) has been widely applied in microarray studies as a data-reduction-tool without any a-priori assumption regarding associations between observed data and latent structure (Exploratory Factor Analysis). A disadvantage is that the representation of data in a reduced set of dimensions can be difficult to interpret, as biological contrasts do not necessarily coincide with single dimensions. However, FA can also be applied as an instrument to confirm what is expected on the basis of pre-established hypotheses (Confirmatory Factor Analysis, CFA). We show that with a hypothesis incorporated in a balanced (orthogonal) design, including 'SelfSelf' hybridizations, dye swaps and independent replications, FA can be used to identify the latent factors underlying the correlation structure among the observed two-color microarray data. An orthogonal design will reflect the principal components associated with each experimental factor. We applied CFA to a microarray study performed to investigate cisplatin resistance in four ovarian cancer cell lines, which only differ in their degree of cisplatin resistance. RESULTS: Two latent factors, coinciding with principal components, representing the differences in cisplatin resistance between the four ovarian cancer cell lines were easily identified. From these two factors 315 genes associated with cisplatin resistance were selected, 199 genes from the first factor (False Discovery Rate (FDR): 19%) and 152 (FDR: 24%) from the second factor, while both gene sets shared 36. The differential expression of 16 genes was validated with reverse transcription-polymerase chain reaction. CONCLUSION: Our results show that FA is an efficient method to analyze two-color microarray data provided that there is a pre-defined hypothesis reflected in an orthogonal design

Familial Aggregation between the 14th and 21st Century and Type 2 Diabetes Risk in an Isolated Dutch Population

The development of type 2 diabetes results from an interaction of hereditary factors and environmental factors. This study aimed to investigate the contribution of interrelatedness to the risk of developing type 2 diabetes in an isolated Dutch population.A genealogical database from inhabitants living on the former island Urk between the 14th and 21st century was constructed. In a case-control study, effects of interrelatedness and the risk of type 2 diabetes were estimated with Kinship Coefficients (KCs). Relative risks in first, second, and third degree relatives and spouses of inhabitants with type 2 diabetes were compared to matched controls.Patients with type 2 diabetes were more interrelated, expressed by a higher KC compared to controls (7.2 vs. 5.2, p=0.001). First, second and third degree relatives had an increased risk of developing type 2 diabetes. Second degree relatives had a similar risk,1.7 (1.5-2.0) as third degree relatives,1.8 (1.5-2.2). Spouses of patients with diabetes had a 3.4 (2.7-4.4) higher risk of developing type 2 diabetes.Interrelatedness was higher among inhabitants with type 2 diabetes compared to controls. This differences extended beyond the nuclear family, thereby supporting the hypothesis that interrelatedness contributed to the development of type 2 diabetes on Urk. However, the size of this effect was small and the patterns of risk in first, second and third degree relatives suggested that factors other than interrelatedness were the main contributors to the development of type 2 diabetes on Urk