A major quantitative trait locus determining serum leptin levels and fat mass is located on human chromosome 2

Comuzzie, Anthony G.; Hixson, James E.; Almasy, Laura; Mitchell, Braxton D.; Mahaney, Michael C.; Dyer, Thomas D.; Stern, Michael P.; MacCluer, Jean W.; Blangero, John

doi:10.1038/ng0397-273

Cited by 422 publications

(301 citation statements)

References 30 publications

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“…This study provides substantial evidence for linkage supporting QTLs previously reported to be linked to serum leptin and plasma adiponectin levels. The linkage region on chromosome 2 in this study (with the peak at 72.6 cM for PBF) overlaps with the region found to be linked to leptin in several studies [14][15][16] and with the region linked to plasma adiponectin by Comuzzie et al 17 The linkage regions identified on chromosome 4 and 5 have not previously been reported as harboring QTLs influencing obesity phenotypes. Yet, the relatively strong linkage signals observed in this study suggest that these genomic regions may indeed contain QTLs influencing obesity traits.…”

Section: Discussionsupporting

confidence: 82%

A genome-wide scan for quantitative trait loci linked to obesity phenotypes among West Africans

et al. 2004

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Section: Discussionsupporting

confidence: 82%

A genome-wide scan for quantitative trait loci linked to obesity phenotypes among West Africans

et al. 2004

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“…We did not, however, observe linkage of leptin levels to chromosome 7, where the leptin gene is, to chromosome 1, where the leptin receptor is, 24 or to the region on chromosome 2p of the gene for POMC. 25 Failure to demonstrate evidence for linkage of leptin to chromosome 7 has been observed in other linkage analyses, 26,27 and a recent meta-analysis showed no association between the common leptin receptor and measures of body weight. 28 In our cohort of predominantly obese individuals, leptin levels may be more strongly determined by genes that code for related metabolic pathways that modulate leptin levels (eg circulating neurotransmitters, inflammatory cytokines, corticosteroids, insulin, etc), or those that influence tissue sensitivity to leptin, than by the leptin gene or receptor itself.…”

Section: Discussionmentioning

confidence: 93%

“…Additional analyses were conducted for leptin residuals without adjusting for BMI, as has been done in other linkage analyses of leptin. [12][13][14][15] Since BMI and age were correlated (r ¼ 0.27, Po0.0001), the latter analyses did not adjust for age, which would have partially adjusted for BMI. After adjusting for BMI, age was not a significant predictor of leptin levels.…”

Section: Discussionmentioning

confidence: 99%

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Linkage of serum leptin levels in families with sleep apnea

et al. 2004

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OBJECTIVE:To identify regions on the genome linked to plasma leptin levels. DESIGN: Full genome scan with 402 microsatellite markers, spaced B10 cM apart. Data were analyzed using the HasemanElston regression linkage analysis. SUBJECTS: A total of 160 sibling pairs from 59 predominantly African American, obese families recruited to participate in a genetic-epidemiological study of obstructive sleep apnea. MEASUREMENTS: Serum leptin levels adjusted for age, sex, race and body mass index (BMI). RESULTS: Suggestive linkage peaks were observed on chromosomes 2 (P ¼ 0.00170; marker D2S1384), 3 (P ¼ 0.00007; marker D3S3034), 4 (P ¼ 0.00020; marker D4S1652) and 21 (P ¼ 0.00053; marker D21s1411). CONCLUSION: The peak on chromosome 3 is near the gene for glycogensynthase kinase 2 beta, an important factor in glucose homeostasis. Linkage was generally stronger after BMI adjustment, suggesting the potential influence of a number of metabolic pathways on leptin levels other than those that directly determine obesity levels. The evidence of linkage for leptin levels is consistent with prior linkage analyses for cholesterol, hypertension and other metabolic phenotypes.

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“…[26][27][28][29][30][31][32] This is a problem because failure to identify causal variants within linkage regions may impede gene discovery. Part of the difficulty may be related to the analytical strategy using association-based methods to follow up linkage.…”

Section: Discussionmentioning

confidence: 99%

A novel method, the Variant Impact On Linkage Effect Test (VIOLET), leads to improved identification of causal variants in linkage regions

et al. 2013

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The Human Genome Project was expected to individualize medicine by rapidly advancing knowledge of common complex disease through discovery of disease-causing genetic variants. However, this has proved challenging. Although linkage analysis has identified replicated chromosomal regions, subsequent detection of causal variants for complex traits has been limited. One explanation for this difficulty is that utilization of association to follow up linkage is problematic given that linkage and association are not required to co-occur. Indeed, co-occurrence is likely to occur only in special circumstances, such as Mendelian inheritance, but cannot be universally expected. To overcome this problem, we propose a novel method, the Variant Impact On Linkage Effect Test (VIOLET), which differs from other quantitative methods in that it is designed to follow up linkage by identifying variants that influence the variance explained by a quantitative trait locus. VIOLET's performance was compared with measured genotype and combined linkage association in two data sets with quantitative traits. Using simulated data, VIOLET had high power to detect the causal variant and reduced false positives compared with standard methods. Using real data, VIOLET identified a single variant, which explained 24% of linkage; this variant exhibited only nominal association (P ¼ 0.04) using measured genotype and was not identified by combined linkage association. These results demonstrate that VIOLET is highly specific while retaining low false-negative results. In summary, VIOLET overcomes a barrier to gene discovery and thus may be broadly applicable to identify underlying genetic etiology for traits exhibiting linkage.

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A major quantitative trait locus determining serum leptin levels and fat mass is located on human chromosome 2

Cited by 422 publications

References 30 publications

A genome-wide scan for quantitative trait loci linked to obesity phenotypes among West Africans

A genome-wide scan for quantitative trait loci linked to obesity phenotypes among West Africans

Linkage of serum leptin levels in families with sleep apnea

A novel method, the Variant Impact On Linkage Effect Test (VIOLET), leads to improved identification of causal variants in linkage regions

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